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A case study of apparent immune activation following treatment of a colorectal cancer patient with an… Titmuss, Emma 2018

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A	case	study	of	apparent	immune	activation	following	treatment	of	a	colorectal	cancer	patient	with	an	angiotensin	receptor	blocker		by	Emma	Titmuss	B.Sc.,	The	University	of	Sussex,	2015			A	THESIS	SUBMITTED	IN	PARTIAL	FLFILLMENT	OF	THE	REQUIREMENTS	FOR	THE	DEGREE	OF			MASTER	OF	SCIENCE	in	The	Faculty	of	Graduate	and	Postdoctoral	Studies	(Genome	Science	and	Technology)			THE	UNIVERSITY	OF	BRITISH	COLUMBIA	(Vancouver)		 July	2018		 ©	Emma	Titmuss,	2018			 	 	 ii	The	following	individuals	certify	that	they	have	read,	and	recommend	to	the	Faculty	of	Graduate	and	Postdoctoral	Studies	for	acceptance,	the	thesis	entitled:		A	case	study	of	immune	activation	observed	in	a	colorectal	cancer	patient	following	personalised	therapy	with	an	angiotensin	receptor	blocker	 	 	 	 		submitted	by			Emma	Titmuss	 	 						in	partial	fulfilment	of	the	requirements	for		the	degree	of			Master	of	Science	 	 	 	 	 	 	 	in		 													Genome	Science	and	Technology	 	 	 	 	 					Examining	Committee:	Dr	Marco	Marra,	Medical	Genetics	 	 	 	 	 	 	 	Supervisor		Dr	Robert	Holt,	Medical	Genetics	 	 	 	 	 	 	 	Supervisory	Committee	Member		Dr	Isabella	Tai,	Division	of	Gastroenterology,	Department	of	Medicine	 	 	Supervisory	Committee	Member			 	 	 	 	 	 	 	 	 	 	 	Additional	Examiner		 	 	 iii	Abstract		Despite	being	one	of	the	most	preventable	cancers,	colorectal	cancer	(CRC)	affects	a	large	proportion	of	the	population	and	results	in	~12%	of	all	deaths	due	to	cancer	in	Canada	(Canadian	Cancer	Society,	2017).	Standard	treatments	for	CRC	are	chemotherapy	based,	but	more	targeted	therapies	are	emerging	as	highly	effective	treatments	across	multiple	disease	types.	The	Personalised	Oncogenomics	(POG)	program	at	BC	Cancer	aims	to	discover	actionable	genomic	alterations	using	whole	genome	and	transcriptome	sequence	analysis	of	incurable	cancer	patients	(Laskin	et	al.,	2015).	Occasionally,	selected	patients	may	be	offered	a	treatment	predicted	by	the	POG	analysis.	One	particular	metastatic	CRC	POG	patient	displayed	a	profound	response	upon	treatment	with	an	antihypertensive	drug,	irbesartan	(Avapro),	prescribed	following	genomic	analysis	of	a	biopsy	sample	that	had	revealed	unusually	high	expression	of	FOS	and	JUN	transcripts,	downstream	components	of	the	pathway	on	which	irbesartan	acts.	After	a	durable	18-month	response	to	irbesartan,	the	patient	relapsed	and	a	second	biopsy	was	taken,	providing	a	unique	opportunity	to	study	the	mechanisms	underpinning	the	response	and	relapse	of	the	patient.	Gene	set	enrichment	analysis	of	RNA	and	protein	expression	data	revealed	an	increase	in	abundance	of	genes	involved	in	immune	system	pathways	following	treatment	with	irbesartan,	and	results	from	multiplex	immunohistochemistry	panels	indicated	increased	cytotoxic	T	cell	infiltration	following	treatment.	Combined	with	increases	in	protein	and	RNA	abundance	of	negative	immune	checkpoints	(often	a	resistance	mechanism	to	immune	activation),	and	a	large	repertoire	of	candidate	neo-antigens,	there	is	evidence	to	support	the	hypothesis	that	irbesartan	stimulated	an	anti-tumour	immune	response.	In	contrast	with	immunotherapy	agents	such	as	immune	checkpoint	inhibitors	(ICIs),	irbesartan	is	substantially	cheaper,	and	exhibits	fewer	side		 	 	 iv	effects.	If	a	biomarker	of	response	to	irbesartan	can	be	identified,	there	may	be	future	potential	for	this	drug	to	be	tested	for	clinical	activity	in	a	larger	patient	population.	Furthermore,	this	case	study	demonstrates	the	utility	of	whole	genome	and	transcriptome	sequencing	to	study	response	and	resistance	to	therapies	and	how	these	methods	might	be	used	to	inform	clinical	decision	making.			 	 	 v	Lay	Summary		One	patient	with	stage	IV	colorectal	cancer	was	treated	with	a	drug	usually	prescribed	for	blood	pressure	control,	irbesartan	(Avapro).	After	only	5	weeks	of	treatment,	the	patient	had	a	profound	response	to	treatment,	with	multiple	tumours	throughout	the	patient’s	body	decreasing	in	volume.	Unfortunately,	after	18	months,	the	patient’s	disease	returned	and	at	this	point	a	second	biopsy	was	taken.	This	thesis	reports	the	detection	and	analysis	of	genomic	and	cellular	changes	in	the	tumours,	before	and	after	treatment	to	identify	candidate	mechanisms	of	drug-induced	effects.	My	results	are	consistent	with	the	hypothesis	that	the	drug	may	have	acted	to	activate	the	patient’s	own	immune	system,	resulting	in	a	decrease	in	tumour	burden	to	below	detectable	limits.	Understanding	how	the	drug	may	have	acted	in	this	patient	is	important	as	it	could	lead	to	discovery	of	a	biomarker	of	response	that	could	allow	other	patients	to	benefit	from	irbesartan.			 	 	 vi	Preface		Dr.	Marco	Marra	initiated	investigation	into	the	biological	effects	of	irbesartan	in	this	case	study.	RNA	and	whole	genome	sequencing	were	conducted	at	the	BC	Cancer	Genome	Sciences	Centre	(BCCGSC),	and	processed	according	to	the	standard	POG	pipeline,	and	whole	genome	and	transcriptome	data	were	analysed	by	Dr.	Martin	Jones	to	identify	candidate	treatment	options.	Immunohistochemistry	analyses	were	conceived	by	Emma	Titmuss,	Katy	Milne	and	Dr.	Brad	Nelson,	and	were	performed	at	the	Deeley	Research	Centre	in	Victoria,	BC,	by	Katy	Milne.	Analyses	comparing	genomic	data	derived	from	serial	biopsies	were	performed	by	Emma	Titmuss.	Dr.	Emilia	Lim	and	James	Topham	provided	technical	advice	regarding	some	of	these	analyses.	Dr.	Erin	Pleasance	directed	identification	and	analysis	of	biomarkers	of	response	to	immune	checkpoint	inhibitors	within	the	POG	program,	and	Emma	Titmuss	and	Hillary	Pearson	conducted	patient	survival	analyses.	Scott	Brown	provided	direction	with	the	prediction	of	neoantigens,	and	Craig	Rive	provided	a	protocol	for	ELISPOT	testing.				 	 	 vii	Table	of	Contents		Abstract	....................................................................................................................................	iii	Lay	Summary	............................................................................................................................	v	Preface	......................................................................................................................................	vi	Table	of	Contents	..................................................................................................................	vii	List	of	Tables	.............................................................................................................................	x	List	of	Figures	.........................................................................................................................	xi	List	of	Abbreviations............................................................................................................	xii	List	of	Genes	..........................................................................................................................	xiv	Acknowledgements	...........................................................................................................	xviii	1	Introduction	.........................................................................................................................	1	1.1	 Cancer	is	a	heterogeneous	malignancy	..........................................................................1	1.1.1.	 Heterogeneity	at	the	primary	tumour	site	................................................................................	2	1.1.2.	 Heterogeneity	at	metastatic	sites	...................................................................................................	3	1.2	 The	role	of	the	immune	system	in	cancer	progression	..............................................4	1.2.1.	 Anti-tumour	immune	environments	............................................................................................	4	1.2.2.	 Tumour	promoting	immune	environments	.............................................................................	6	1.3	 Harnessing	the	anti-cancer	properties	of	the	immune	system	................................8	1.4	 Tumour	specific	features	can	influence	response	to	immunotherapies	............	10	1.4.1.	 Predictors	of	response	to	immunotherapy	............................................................................	10	1.4.2.	 Tumour	resistance	to	immunotherapies.................................................................................	15	1.5	 Genomics	as	a	tool	to	guide	treatment	personalisation..........................................	17	1.5.1.	 Genomics	methods	..............................................................................................................................	17	1.5.2.	 A	personalised	oncogenomics	case	study	...............................................................................	18	1.6	 Irbesartan	as	a	cancer	therapy	......................................................................................	21	1.6.1.	 Selection	of	irbesartan	as	a	therapy...........................................................................................	21		 	 	viii	1.6.2.	 Alternative	roles	of	irbesartan......................................................................................................	25	1.7	 Research	hypotheses	and	outline	.................................................................................	26	2	Materials	and	Methods	....................................................................................................	28	2.1	 Patient	samples	and	processing	....................................................................................	28	2.2	 PyClone	clustering	analysis	............................................................................................	29	2.3	 Gene	set	enrichment	analysis.........................................................................................	29	2.4	 Mass	spectrometry	data	analysis	..................................................................................	30	2.5	 RNA-Seq	deconvolution....................................................................................................	31	2.6	 Multiplex	immunohistochemistry	................................................................................	31	2.7	 T	cell	receptor	repertoire	analysis	...............................................................................	32	2.8	 HLA	typing	and	neoantigen	prediction	........................................................................	33	2.9	 Plots	and	visualisation	.....................................................................................................	34	2.10	 PBMC	processing	and	ELISPOT	protocol	.....................................................................	34	3	Results	..................................................................................................................................	37	3.1	 Clonal	Evolution	of	the	Resistant	Tumour	..................................................................	37	3.1.1.	 Detection	of	heterogeneous	clones	within	the	tumour	...................................................	38	3.2	 Gene	expression	changes	are	compatible	with	involvement	of	the	immune	system................................................................................................................................................	43	3.2.1.	 Genes	up-regulated	after	treatment	are	enriched	for	immune	related		pathways	..................................................................................................................................................	45	3.2.2.	 Marker	genes	for	immune	cells	are	differentially	expressed	.......................................	47	3.2.3.	 Mass	spectrometry	.............................................................................................................................	53	3.3	 Changes	in	cell	infiltration	correlate	with	an	active	immune	response	.............	56	3.3.1.	 Immunohistochemistry	identifies	increases	in	T	cell	infiltration	following	treatment	.................................................................................................................................................	56	3.3.2.	 Post	treatment	infiltrating	T	cells	have	a	more	diverse	T	cell	receptor		repertoire.................................................................................................................................................	63	3.3.3.	 High	neoantigen	load	is	predicted	within	the	tumour	.....................................................	68	4	Discussion	...........................................................................................................................	73	4.1	 The	response	observed	in	the	patient	is	consistent	with	an	immune		response	..................................................................................................................................	73		 	 	 ix	4.2	 The	mechanism	of	action	of	irbesartan	may	have	involved	off	target		pathways	...........................................................................................................................................	75	4.3	 Confounding	factors	and	limitations	............................................................................	77	4.4	 Concluding	remarks	and	future	directions	................................................................	78	Bibliography	..........................................................................................................................	81	Appendices	...........................................................................................................................100	Appendix	A:	Gene	set	enrichment	results	for	differentially	expressed	genes	......................	100	Appendix	B:	Immunohistochemistry	staining	from	patient	diagnosis	.....................................	120	Appendix	C:	Sequencing	coverage	for	biopsy	samples	.....................................................................	121	Appendix	D:	Reagents	and	resource	information	................................................................................	122	Appendix	E:	Mutations	increasing	in	variant	allele	frequency	following	irbesartan	treatment	(cluster	6)	...........................................................................................................................................	123	Appendix	F:	Top	differentially	expressed	genes	following	irbesartan	treatment	..............	135	Appendix	G:	100	peptides	tested	using	an	ELISPOT	..........................................................................	143	Appendix	H:	Candidate	neoantigens	...........................................................................................................	151	Appendix	I:	Copy	number	variations	in	both	biopsies	......................................................................	173	Appendix	J:	Enrichment	analysis	of	mass	spectrometry	data	.......................................................	179				 	 	 x	List	of	Tables		Table	2-1:	ELISPOT	pools	enable	testing	multiple	peptides	at	once	....................................	36	Table	3-1:	Diversity	metrics	for	infiltrating	T	cells	..................................................................	68			 	 	 xi	List	of	Figures		Figure	1–1:	Combination	of	T	cell	infiltration	and	high	mutational	load	predict	patient	response	to	immunotherapy	more	effectively	than	single	markers	...........................	14	Figure	1–2:	The	tumour	has	a	very	high	mutational	load	as	a	result	of	mismatch	repair	deficiency	.....................................................................................................................................	20	Figure	1–3:	Angiotensin	signalling	pathway	can	be	targeted	by	ARBs	and	ACE-I	............	23	Figure	1–4:	Patient	timeline	of	treatment,	and	clinical	response	to	irbesartan.	..............	24	Figure	3–1:	Mutational	clones	detected	in	the	patient	biopsies.	...........................................	40	Figure	3–2:	Mutational	changes	between	biopsies	suggest	irbesartan	has	targeted	more	than	a	single	pathway.	..............................................................................................................	41	Figure	3–3:	Angiotensin	II	receptor	I	expression	is	low	in	both	biopsy	samples	compared	to	other	colorectal	cancers	within	POG	and	TCGA.	.......................................	44	Figure	3–4:	Gene	expression	changes	between	biopsies	are	enriched	for	immune	system	processes........................................................................................................................	46	Figure	3–5:	Testing	immune	cell	deconvolution	software	demonstrates	that	CIBERSORT	is	most	accurate	for	POG	data.	................................................................................................	50	Figure	3–6:	Cell	infiltration	predictions	for	all	POG	CRC	patients	suggest	high	infiltration	for	the	post-treatment	biopsy.	.........................................................................	52	Figure	3–7:	Mass	spectrometry	data	reflect	changes	in	immune	proteins.	.......................	54	Figure	3–8:	Immunohistochemistry	staining	reveals	increased	immune	infiltration	following	treatment...................................................................................................................	61	Figure	3–9:	Cell	infiltration	counts	show	significant	increases	of	T	cell	subtypes	following	treatment	with	irbesartan.	...................................................................................	62	Figure	3–10:	RNA-Seq	read	alignment	to	T	cell	receptors	in	both	biopsy	samples.	........	64	Figure	3–11:	TCR	VJ	gene	usage	increases	in	the	post	treatment	biopsy............................	68	Figure	3–12:	High	confidence	neoantigens	are	predicted	for	common	HLA	types	and	are	detected	at	both	biopsies.	........................................................................................................	71	Figure	3–13:	ELISPOT	results	are	not	comprehensive	enough	to	identify	an	immunogenic	peptide	...............................................................................................................	72			 	 	 xii	List	of	Abbreviations		ACEI	 	 	 	 Angiotensin	converting	enzyme	inhibitor	ADH	 	 	 	 Antidiuretic	hormone	APC	 	 	 	 Antigen	presenting	cell	BB	 	 	 	 Beta	blockers	BCCGSC	 	 	 British	Columbia	Cancer	Genome	Sciences	Centre	CDK	 	 	 	 Cyclin	dependent	kinase	CG	antigens	 	 	 Cancer	germline	antigens	CLL	 	 	 	 Chronic	lymphocytic	leukaemia		COAD	 	 	 	 Colorectal	adenocarcinoma	CPK	 	 	 	 Clonotypes	per	thousand	CRC	 	 	 	 Colorectal	cancer	ctDNA	 	 	 	 Circulating	tumour	DNA	DNA	 	 	 	 Deoxyribonucleic	acid	EVD	 	 	 	 Ebola	virus	disease	GI	 	 	 	 Gastrointestinal	GO	 	 	 	 Gene	ontology	HLA	 	 	 	 Human	leukocyte	antigen	IHC	 	 	 	 Immunohistochemistry	indel	 	 	 	 Insertion	or	deletion	MHC-I	 	 	 	 Major	histocompatibility	complex	class	I	MHC-II		 	 	 Major	histocompatibility	complex	class	II	MMR-d		 	 	 Mismatch	repair	deficient		 	 	 xiii	MRT	 	 	 	 Malignant	rhabdoid	tumour	MSI	 	 	 	 Microsatellite	instable	MSS	 	 	 	 Microsatellite	stable	PET	 	 	 	 Positron	emission	tomography	PGE2	 	 	 	 Prostaglandin	E2	POG		 	 	 	 Personalised	oncogenomics	RNA		 	 	 	 Ribonucleic	acid	RNA-Seq	 	 	 RNA	sequencing	ROS	 	 	 	 Reactive	oxygen	species	RPKM	 	 	 	 Reads	Per	Kilobase	Million	SMI	 	 	 	 Small	molecule	inhibitor	SNV	 	 	 	 Single	nucleotide	variant	TAA	/	TSA	 	 	 Tumour	associated	/	specific	antigen	TCGA	 	 	 	 The	Cancer	Genome	Atlas	TCR	 	 	 	 T	cell	receptor	 	VAF	 	 	 	 Variant	allele	frequency	VDJdb	 	 	 	 VDJ	database		 	 	 xiv	List	of	Genes		AGTR1		 	 	 Angiotensin	II	receptor	type	I	APC	 	 	 	 Adenomatous	polyposis	coli	β2M	 	 	 	 Beta-2-microglobulin	Brachyury	(T)	 	 	 T	box	transcription	factor	T	CCL5	 	 	 	 C-C	motif	chemokine	ligand	5	CD11b	 (ITGAM)	 	 Integrin	subunit	alpha	M	 	 	 	CD163	 	 	 	 CD163	molecule	CD20	(MS4A1)		 	 Membrane	spanning	4-domains	A1	 	 	 	CD25	(IL2RA)	 	 	 Cluster	of	differentiation	25	CD28	 	 	 	 CD28	molecule	CD3	 	 	 	 Cluster	of	differentiation	3	CD33	 	 	 	 CD33	molecule	CD4	 	 	 	 Cluster	of	differentiation	4	CD74	 	 	 	 Major	histocompatibility	complex	class	II	invariant	chain	CD79A	 	 	 	 CD79A	molecule	CD8	 	 	 	 Cluster	of	differentiation	8	CD80	 	 	 	 Costimulatory	factor	CD80	CD86	 	 	 	 CD86	molecule	CDR3	 	 	 	 Complementarity-determining	region	CTLA4	 	 	 	 Cytotoxic	T-lymphocyte	associated	protein	4	CXCL8	 	 	 	 C-X-C	motif	chemokine	ligand	8	DAB2IP	 	 	 Disabled	homolog-2	interacting	protein	EGF	 	 	 	 Epidermal	growth	factor		 	 	 xv	FOS	 	 	 	 Fos	proto-oncogene,	AP-1	subunit	FOXP3	 	 	 	 Forkhead	Box	P3	GM-CSF	 	 	 Granulocyte-macrophage	colony	stimulating	factor	GrB	(GZMB)	 	 	 Granzyme	B	GZMK	 	 	 	 Granzyme	K	HLA-A	 	 	 	 Major	Histocompatibility	Complex,	Class	I,	A	HLA-B	 	 	 	 Major	Histocompatibility	Complex,	Class	I,	B	HLA-C	 	 	 	 Major	Histocompatibility	Complex,	Class	I,	C	HLA-DOA	 	 	 Major	Histocompatibility	Complex,	Class	II,	DO	alpha		HLA-DPA1	 	 	 Major	Histocompatibility	Complex,	Class	II,	DP	alpha	1	HLA-DPB1	 	 	 Major	Histocompatibility	Complex,	Class	II,	DP	beta	1	HLA-DQA1	 	 	 Major	Histocompatibility	Complex,	Class	II,	DQ	alpha	1	HLA-DQB2	 	 	 Major	Histocompatibility	Complex,	Class	II,	DQ	beta	2	HLA-DRA			 	 	 Major	Histocompatibility	Complex,	Class	II,	DR	alpha	IFNG	 	 	 	 Interferon	gamma	IL12	 	 	 	 Interleukin	12	IL2	 	 	 	 Interleukin	2	JUN	 	 	 	 Jun	proto-oncogene,	AP-1	subunit	KRAS	 	 	 	 KRAS	proto-oncogene	GTPase	LAG3	 	 	 	 Lymphocyte	activating	3	LITAF	 Lipopolysaccharide-induced	tumour	necrosis	factor,	alpha	MAGEA1	 Melanoma	antigen	family	A,	1	MAGEA2	 Melanoma	antigen	family	A,	2	MAPK	 Mitogen	activated	protein	kinase	MCP1	(CCL2)		 	 	 Monocyte	chemotactic	and	activating	factor	 	MLH1	 	 	 	 MutL	homolog	1		 	 	 xvi	MSH3	 	 	 	 MutS	homolog	3	MLH3	 	 	 	 MutL	homolog	3	MSH6	 	 	 	 MutS	homolog	6	PAP	 	 	 	 Prostatic	acid	phosphatase	PD-1	(PDCD1)	 	 	 Programmed	cell	death	1	PD-L1	(CD274)	 	 Programmed	cell	death	1	ligand	1	PD-L2	(PDCD1LG2)	 	 Programmed	cell	death	1	ligand	2	PIK3CA	 Phosphatidylinositol-4,5-bisphosphate	3-kinase	catalytic	subunit	alpha	PIK3R1	 	 	 Phosphoinositide-3-kinase	regulatory	subunit	1	POLN	 	 	 	 DNA	polymerase	Nu	PPARγ	 	 	 	 Peroxisome	proliferator	activated	receptor	gamma	PRF1	 	 	 	 Perforin	1	PRG2	 	 	 	 Bone	marrow	proteoglycan	PTEN	 	 	 	 Phosphatase	and	tensin	homolog	RAG1	 	 	 	 Recombination	activating	1	RAG2	 	 	 	 Recombination	activating	2	TAP	 	 	 	 Transporter	1	TGFB	 	 	 	 Transforming	growth	factor	beta	1	TIM3	 	 	 	 T	cell	immunoglobin	mucin	3	TNFα	 	 	 	 Tumour	necrosis	factor	alpha	TP53	 	 	 	 Tumour	protein	P53	TRAJ	 	 	 	 T	cell	receptor	alpha	joining	genes	TRAV	 	 	 	 T	cell	receptor	alpha	variable	genes	TRBJ	 	 	 	 T	cell	receptor	beta	joining	genes	TRBV	 	 	 	 T	cell	receptor	beta	variable	genes		 	 	xvii	VEGF	 	 	 	 Vascular	endothelial	growth	factor	WNT	 	 	 	 Proto-oncogene	wnt			 	 	xviii	Acknowledgements		First	and	foremost,	I	would	like	to	thank	Dr.	Marco	Marra	for	his	outstanding	support,	leadership	and	compassion	throughout	my	time	as	a	graduate	student.			I’d	also	like	to	thank	all	of	the	members	of	the	Marra	lab,	and	Lulu	Crisostomo,	for	their	encouragement	and	advice.	Thank	you	to	Dr.	Janessa	Laskin,	Dr.	Martin	Jones	and	Dr.	Erin	Pleasance	for	their	fantastic	leadership	of	the	POG	program,	and	the	rest	of	the	POG	team	for	all	of	their	hard	work	analysing	individual	cases.	Thank	you	to	Dr.	Howard	Lim	for	his	insight	and	guidance	with	the	clinical	aspects	of	this	thesis.		Thank	you	to	my	committee	members,	Dr.	Robert	Holt	and	Dr.	Isabella	Tai,	for	their	guidance	and	suggestions	during	my	time	in	graduate	school.			I	would	like	to	acknowledge	Dr.	Brad	Nelson	and	Katy	Milne	at	the	Deeley	Research	Centre	in	Victoria	for	conducting	immunohistochemistry	staining	and	advice	on	panels.	I	would	also	like	to	note	the	work	of	the	immunoPOG	team,	specifically	Hillary	Pearson	and	Scott	Brown	for	their	analysis	of	patient	survival	data	and	guidance	of	neoantigen	prediction	pipelines	respectively.		Thank	you	to	Dr.	Tony	Ng	and	Dr.	David	Schaeffer	for	providing	control	CRC	tissue	slides	for	immunohistochemistry	from	the	BC	GI	Biobank.			 	 	 xix	Lastly,	I	would	like	to	thank	all	of	the	patients	and	families	enrolled	in	the	POG	program,	the	BC	Cancer	Foundation	and	the	Canada	Foundation	for	Innovation	for	funding	of	the	POG	program,	as	without	them	this	research	would	not	have	been	possible.							 	 	 1	1 Introduction		Cancer	is	widely	considered	to	be	a	disease	of	the	genome:	complex	and	heterogeneous	on	both	a	cancer	type	and	individual	patient	level.	Treatments	for	cancer	are	continually	being	developed,	incorporating	new	understandings	of	the	disease,	and	many	therapies	now	target	specific	tumour	features.	This	chapter	will	begin	with	an	overview	of	the	heterogeneous	nature	of	tumours	in	1.1,	then	will	discuss	how	heterogeneity	in	the	surrounding	immune	microenvironment	can	influence	tumour	growth	in	1.2.	Section	1.3	will	discuss	the	concept	of	cancer	therapies	that	target	the	immune	system,	and	1.4	will	describe	emerging	features	of	tumours	that	can	predict	patient	response	to	these	types	of	therapies.	The	last	sections	of	this	chapter	will	then	provide	details	on	an	unusual,	yet	vastly	effective	medication	on	an	individual	cancer	patient	case	study,	and	the	goals	and	aims	of	this	thesis.			1.1 Cancer	is	a	heterogeneous	malignancy		Heterogeneity	in	cancers	can	be	described	at	many	different	levels:	the	primary	site	of	tumour	origin,	as	there	are	known	to	be	over	100	different	types	of	cancer	(National	Cancer	Institute,	2015);	heterogeneity	based	on	the	location	of	a	tumour	lesion,	which	can	be	found	in	primary	and	metastatic	lesions	of	the	same	primary	tumour	in	the	same	patient	(Lee	et	al.,	2014b),	and	even	heterogeneity	between	individual	tumour	cells	in	a	single	lesion	(Chung	et	al.,	2017;	Leung	et	al.,	2017).	For	these	reasons,	it	is	challenging	to	find	therapies	that	are	effective	for	many	patients.	As	such,	clinical	decision-making	is	beginning	to	incorporate	knowledge	of	tumour	genomic	features	in	order	to	rationalise	therapies	that	may	be	most	effective	for		 	 	 2	subsets	of,	or	individual,	patients	(Laskin	et	al.,	2015;	Jones	et	al.,	2016;	Jones	et	al.,	2017;	Zhao	et	al.,	2017).	1.1.1. Heterogeneity	at	the	primary	tumour	site		Malignant	cells	can	vary	in	their	features	and	characteristics	depending	upon	their	cell	of	origin,	as	these	cells	already	have	differing	properties.	Tumours	are	classified	based	upon	the	differentiated	cell	they	originated	from,	and,	mostly,	standard	of	care	therapies	are	approved	based	upon	this.	However,	tumours	derived	from	the	same	cell	of	origin	are	not	identical.	Every	individual’s	tumour	has	distinct	variation	from	others	of	the	same	cancer	type,	which	can	be	in	the	form	of	somatic	mutations,	copy	number	alterations,	gene	expression	levels	or	alterations	at	the	epigenetic	level	(Muzny	et	al.,	2012)	and	these	features	can	influence	how	patients	will	respond	to	various	therapies.		Tumours	that	arise	from	the	same	cell	type	have	common	somatic	aberrations	that	can	be	found	across	large	cohorts	of	patients	with	the	disease,	which	have	shaped	the	current	standard	therapies	for	each	tumour	type.	Classic	examples	of	this	are	colorectal	cancers	(CRCs),	where	the	majority	of	tumours	exhibit	a	tumour	initiating	mutation	in	the	gene	APC	and	frequent	mutations	can	be	found	in	PIK3CA,	KRAS	and	TP53	leading	to	disruption	of	PI3K	and	RAS	signalling	(Muzny	et	al.,	2012;	Vogelstein	et	al.,	2013).	These	CRCs	have	a	distinct	underlying	molecular	landscape	from	many	other	tumours,	for	example	malignant	rhabdoid	tumours	(MRTs),	which	are	characterised	by	a	loss	of	SMARCB1	and	virtually	no	other	recurrent	mutations	(Chun	et	al.,	2016).		In	addition	to	variation	attributed	to	the	primary	site	of	the	tumour,	individual	cells,	or	subsets	of	cells,	within	a	tumour	can	have	distinct	somatic	variation	(Leung	et	al.,	2017).	This		 	 	 3	intra-tumour	heterogeneity	is	able	to	facilitate	resistance	to	various	therapies,	as	certain	cells	may	possess	particular	properties	that	enable	evasion	from	therapy	and	these	cells	will	be	able	to	resist	treatment	and	repopulate	the	tumour	(Ding	et	al.,	2012b).	1.1.2. Heterogeneity	at	metastatic	sites		Metastasis	in	cancer	occurs	when	cells	of	the	primary	tumour	undergo	genetic	alterations	that	induce	transformation	into	a	more	mesenchymal	phenotype	(Seyfried	and	Huysentruyt,	2013).	These	cells	are	then	able	to	enter	the	circulatory	and	lymphatic	systems	to	populate	another	region	of	the	body	and	continue	tumour	progression.	Metastatic	tumours	can	differ	in	their	genomic	properties	compared	to	the	primary	site,	as	somatic	alterations	have	contributed	to	the	increased	invasive	potential	of	the	metastatic	cells.	In	fact,	about	50%	of	colorectal	cancer	metastatic	sites	have	distinct	variation	in	somatic	mutations	and	copy	number	alterations	compared	to	their	primary	counterparts	(Lee	et	al.,	2014b;	Leung	et	al.,	2017).			Due	to	the	presence	of	metastatic	sites,	patients	with	late	stage	disease	can	be	very	difficult	to	treat	and	generally	have	poorer	survival	than	those	with	early	stage	disease	(Noone	et	al.,	2018).	Over	50%	of	colorectal	cancer	patients	develop	metastases,	nearly	40%	of	which	are	at	sites	distant	from	the	primary	tumour,	and	patients	with	these	distant	metastases	are	associated	with	a	5	year	survival	rate	of	only	13.8%,	compared	to	a	localised	primary	tumour	survival	rate	of	nearly	90%	(Lee	et	al.,	2014b;	Noone	et	al.,	2018).		Aside	from	alterations	within	the	tumours	themselves,	such	as	somatic	mutations	or	gene	expression	dysregulation,	metastatic	sites	have	differing	surrounding	tissues	and	tumour	infiltrating	cells	(Jiménez-Sánchez	et	al.,	2017),	each	providing	the	tumour	with	varying		 	 	 4	selective	pressures	that	can	further	influence	the	growth	of	the	tumour.	As	understanding	has	developed	over	time	regarding	the	process	of	cancer	progression,	the	microenvironment	has	emerged	as	a	component	that	can	also	greatly	influence	tumour	growth	(Whiteside,	2008).	The	immune	component	of	the	tumour	microenvironment	will	be	discussed	in	the	next	section.	1.2 The	role	of	the	immune	system	in	cancer	progression		Tumour	hallmarks	have	traditionally	included	the	ability	of	tumour	cells	to	alter	cell	metabolism,	resist	cell	death	and	activate	invasion	mechanisms	(Hanahan	and	Weinberg,	2011).	However,	as	the	understanding	of	tumour	biology	has	developed,	tumour	characteristics	are	now	known	to	include	interactions	with	the	microenvironment,	particularly	in	the	context	of	inflammatory	environments	and	an	ability	to	avoid	immune	destruction.	Immune	infiltration,	however,	can	either	be	a	benefit	or	a	hindrance	to	the	tumour,	depending	on	the	composition	of	cells	that	are	present	and	characteristics	of	the	tumour	cells,	so	the	involvement	of	the	immune	system	with	cancer	progression	remains	a	complicated	topic	(Janssen	et	al.,	2017).			1.2.1. Anti-tumour	immune	environments		Somatic	aberrations	in	tumour	cells	distinguish	these	cells	from	the	population	of	healthy	cells,	and	so	can	be	recognised	as	abnormal	by	the	immune	system.	In	particular,	short	peptide	sequences	generated	from	degradation	of	proteins	can	be	presented	on	the	surface	of	an	antigen	presenting	cell	(APC)	by	the	major	histocompatibility	complex	(MHC)	class	I	(Hundal	et	al.,	2016;	Liu	and	Mardis,	2017;	Rock	et	al.,	2016).	Peptides	generated	from		 	 	 5	proteins	with	somatic	alterations	(neoantigens),	or	abnormal	expression	(tumour	associated	antigens),	can	be	recognised	by	various	immune	cells	as	antigenic	when	presented.	The	MHC	class	I,	expressed	in	all	differentiated	nucleated	cells,	are	heterodimeric	complexes	composed	of	a	heavy	chain	and	a	β2-microglobulin	(β2M)	protein	(Hewitt,	2003).	The	heavy	chain	of	the	MHC	class	I	complex	is	encoded	by	the	HLA-A,	-B	and	–C	genes	of	which	there	are	many	polymorphic	alleles	in	humans,	contributing	to	variation	in	sequence	and,	subsequently,	peptide	binding	preferences	of	the	MHC	class	I	complexes	across	the	population	(Rock	et	al.,	2016).			T	cells	have	the	ability	to	recognise	these	presented	tumour	antigens	through	the	T	cell	receptor	(TCR),	also	a	highly	variable	heterodimeric	protein,	comprised	of	either	an	αβ	or	γδ	pair	of	chains.	Variability	in	the	sequence	of	human	TCRs	is	attributed	to	somatic	rearrangement	of	V,	(D)	and	J	gene	segments	of	the	respective	T	cell	receptor	chain	during	T	cell	development	(Attaf	et	al.,	2015;	Li	et	al.,	2016).	This	process	of	TCR	gene	rearrangement,	driven	by	recombination	activating	genes	RAG1	and	RAG2,	can	theoretically	generate	a	repertoire	of	1x1016	unique	clonotypes	(TCR	sequences)	in	αβ	T	cells,	the	most	abundant	T	cells	in	humans	(Attaf	et	al.,	2015;	Li	et	al.,	2016).	Diversity	of	TCRs	is	at	its	highest	before	selection	occurs	in	the	thymus,	where	clonotypes	that	exhibit	strong	recognition	of	self-antigens	are	not	released	into	the	periphery	to	prevent	autoimmunity	(Attaf	et	al.,	2015).	Narrowing	of	the	clonotype	diversity	also	occurs	in	the	periphery	following	TCR	interactions	with	antigens,	as	the	cells	expressing	the	binding	TCR	will	expand	(Attaf	et	al.,	2015).	Interestingly,	tumours	with	higher	mutation	loads,	and	likely	higher	neoantigen	loads,	have	been	associated	with	an	increased	diversity	of	TCR	clonotypes	infiltrating	tumours	(Li	et	al.,	2016).			 	 	 6	CD8+	T	cells,	capable	of	recognising	peptides	presented	by	the	MHC	class	I,	have	been	associated	with	increased	survival	in	patients	with	many	cancer	types,	including	colorectal	and	ovarian	cancers	(Galon	et	al.,	2014;	Goode	and	Ovarian	Tumor	Tissue	Analysis	(OTTA)	Consortium,	2017;	Pagès	et	al.,	2005).	These	T	cells	are	capable	of	directly	driving	cytotoxic	immune	responses	against	foreign	antigens	through	mechanisms	involving	IFN-γ,	TNF-α,	perforin	or	granzymes	(Zhang	and	Bevan,	2011).		1.2.2. Tumour	promoting	immune	environments		As	previously	described,	tumour	cell	populations	are	heterogeneous	and	particular	subsets	of	cells	may	possess	characteristics	that	are	resistant	to	immune	detection.	One	well	described	example	of	evading	T	cell	recognition	is	through	increased	expression	of	immune	checkpoints,	such	as	PD-L1	or	PD-L2	on	the	tumour	cell	surface	(Pardoll,	2012).	Under	normal	physiological	conditions,	following	recognition	of	an	antigen	on	an	APC	by	a	TCR,	a	second	stimulatory	signal	must	be	detected	to	initiate	an	immune	response.	One	of	the	best	characterised	of	these	co-stimulatory	receptors	on	T	cells	is	CD28,	which	interacts	with	CD80/86	on	an	APC	(Lenschow	et	al.,	1996;	Pardoll,	2012).	CTLA-4,	another	receptor	present	on	many	T	cells,	shares	high	sequence	homology	and	the	same	ligands	as	CD28,	but	seems	to	dampen	the	immune	response.	Another	co-receptor,	PD-1,	works	in	a	similar	manner,	to	lower	the	activity	of	T	cells	in	the	periphery,	preventing	autoimmunity	(Pardoll,	2012).	During	chronic	infections,	or	cancer,	where	T	cells	are	continually	exposed	to	particular	antigens,	a	state	of	exhaustion	can	develop.	This	phenotype	is	associated	with	prolonged	expression	of	negative	checkpoints,	release	of	immunoregulatory	molecules	(including	IL-10	and	TGF-β)	and	decreased	effector	functions	(Wherry,	2011).	Therefore,	higher	expression	of	the	immune	checkpoint	ligands	on	tumour	cells	can	lead	to	effective	immune	inhibition	(Pardoll,	2012;		 	 	 7	Vinay	et	al.,	2015).	In	addition	to	over-expression	of	immune	checkpoints,	tumours	may	also	secrete	immune-suppressing	molecules	including	TGF-β	and	prostaglandin	E2	(PGE2),	further	contributing	to	immune	evasion	or	an	exhausted	environment	(Melero	et	al.,	2014).		Inflammatory	environments	are	well	known	to	promote	tumourigenesis,	particularly	in	the	context	of	colorectal	cancers	(Vinay	et	al.,	2015),	as	cells	present	in	this	environment	can	supply	the	tumour	with	growth	factors,	including	EGF	and	VEGF,	contributing	to	sustained	proliferation	and	evasion	of	cell	death	(Hanahan	and	Weinberg,	2011;	Qian	and	Pollard,	2010).	Cells	that	are	known	to	contribute	to	this	tumour-promoting	inflammatory	environment	include	subtypes	of	macrophages,	mast	cells,	neutrophils,	some	subsets	of	B	and	T	cells,	and	another	subset	of	cells	with	myeloid	origin:	the	myeloid	derived	suppressor	cells	(MDSCs).	Higher	presence	of	circulating	MDSCs	have	been	associated	with	poorer	patient	outcomes	in	a	number	of	cancers,	including	colorectal,	bladder,	cervical	cancer	and	melanomas	(Veglia	et	al.,	2018).	Furthermore,	these	cells	have	been	negatively	associated	with	response	to	cancer	therapies,	including	immunotherapies,	most	likely	due	to	their	immune-suppressing	role	through	release	of	pro-inflammatory	cytokines,	ER	stress	response	and	production	of	reactive	oxygen	species	(ROS),	leading	to	inhibition	of	an	adaptive	immune	response	(Parker	et	al.,	2015;	Veglia	et	al.,	2018).		Other	cells	that	are	well	known	to	contribute	to	an	immunosuppressive	tumour	environment	are	regulatory	T	cells	(Tregs).	These	CD4+CD25+FOXP3+	cells	are	able	to	suppress	the	immune	response	through	a	number	of	mechanisms	including	cytolysis	and	modulation	of	dendritic	cells	(Vignali	et	al.,	2008).	Tregs,	similarly	to	cytotoxic	T	cells	and	NKs,	have	the	ability	to	release	granzymes	A	or	B,	contributing	to	cytolytic	death	of	effector	T	cells.	Constitutive	expression	of	the	immune	checkpoint	receptor	CTLA-4	on	Tregs	provides	these		 	 	 8	cells	with	another	mechanism	of	inactivating	an	immune	response;	interaction	with	its	ligand	CD80/CD86	on	dendritic	cells	(DCs)	can	stimulate	release	of	IDO	(indoleamine	2,3-dioxygenase),	generating	pro-apoptotic	metabolites	of	tryptophan,	such	as	kynurenine,	which	can	promote	differentiation	of	effect	CD4+	T	cells	into	Tregs,	or	through	inhibition	of	IL-2	signalling	leading	to	impaired	memory	T	cell	survival	(Routy	et	al.,	2016;	Vignali	et	al.,	2008).			1.3 Harnessing	the	anti-cancer	properties	of	the	immune	system			As	described	in	the	previous	section,	the	presence	of	immune	cells	within	the	tumour	microenvironment	can	encourage	tumour	growth.	However,	the	interaction	between	tumour	and	immune	cells	can	be	exploited	in	order	to	control	tumour	progression.	This	concept	of	using	the	immune	system	as	a	therapy	for	cancer	has	been	in	existence	for	over	a	century,	although	it	is	only	more	recently	that	a	greater	understanding	of	the	role	of	the	immune	system	in	cancer	has	enabled	development	of	numerous	immunotherapies,	including	checkpoint	blockade	and	vaccine	based	therapies	(Hanahan	and	Weinberg,	2011;	Liu	and	Mardis,	2017;	Pardoll,	2012;	Siniard	and	Harada,	2017).	One	of	the	earliest	reported	cancer	treatments	that	are	now	known	to	act	through	activation	of	the	immune	system	was	a	vaccine-based	therapy,	pioneered	by	William	Coley	in	the	late	1800s.	This	treatment	involved	injecting	bone	sarcoma	patients	with	bacterial	organisms,	leading	to	immune	stimulation	and	subsequent	tumour	eradication,	reviewed	in	(McCarthy,	2006).		Vaccine	therapies	are	still	used	today	in	cancer	therapies,	although	they	are	somewhat	different	to	those	Coley	experimented	with	(Hundal	et	al.,	2016).	Tumour	associated	antigens	can	be	used	to	develop	vaccine	therapies	capable	of	initiating	IFNγ	driven	immune	responses	against	tumour	cells	stimulated	by	a	tumour	specific	immunogenic	peptide	(Hundal	et	al.,		 	 	 9	2016;	Liu	and	Mardis,	2017;	Schumacher	et	al.,	2014).	Sipuleucel-T	is	an	example	of	a	prostate	cancer	vaccine,	which	uses	antigen	presenting	cells	(APCs)	activated	with	a	prostate	antigen,	prostatic	acid	phosphatase	(PAP),	bound	to	a	stimulating	adjuvant	GM-CSF	(granulocyte-macrophage	colony	stimulating	factor)	(Kantoff	et	al.,	2010;	Melero	et	al.,	2014).			Other	immunotherapies	have	been	developed	to	interact	with	immune	regulatory	receptors	to	initiate	activation	of	the	immune	system	and	control	cancer	proliferation.	One	particular	type	of	immunotherapy,	immune	checkpoint	inhibitors	(ICIs),	utilise	a	monoclonal	antibody	that	targets	a	negative	immune	checkpoint,	described	in	the	previous	section.	Inhibition	of	the	checkpoint	interaction	prevents	its	function	of	immune	inhibition	and	consequently	exposes	the	tumour	cells	to	the	activity	of	the	immune	system	(Khalil	et	al.,	2016;	Pardoll,	2012).	This	type	of	therapy	has	demonstrated	profound	clinical	benefit	for	subsets	of	cancer	patients,	including	some	with	colorectal	cancer	or	melanoma,	with	some	responses	being	described	as	rapid	and	long	lasting	(Le	et	al.,	2015;	Wolchok	et	al.,	2013).	Examples	of	ICIs	that	have	demonstrated	clinical	responses	include	nivolumab	and	pembrolizumab	(targetting	PD-1	on	T	cells)	(Le	et	al.,	2015,	2015;	Wolchok	et	al.,	2013).		Despite	the	clinical	improvements	reported	with	immune	modulating	agents	(Le	et	al.,	2015;	Wolchok	et	al.,	2013),	there	are	many	challenges	with	using	these	therapies.	Checkpoint	inhibition	can	cause	immune	related	adverse	effects	(IRAEs)	in	up	to	90%	of	patients	(Michot	et	al.,	2016).	These	often	include	less	severe	grade	I-II	reactions	such	as	skin	rashes,	but	severe	reactions	(grade	3+),	such	as	colitis,	have	been	reported	in	55%	of	patients	treated	with	ipilimumab	(anti-CTLA-4)	/	nivolumab	(anti-PD-1)	combination	therapy	(Emens	et	al.,	2017).	There	is	also	a	significant	cost	associated	with	immunotherapy	treatments;	it	has	been	estimated	that	immune	checkpoint	agents	can	exceed	USD$1,000,000	annually	for	a	patient		 	 	 10	on	a	higher	dosage	(Andrews,	2015),	and	sipuleucel-T	vaccine	therapy	can	cost	USD$93,000	for	three	injections	(Geynisman	et	al.,	2014).	Some	of	the	severe	side	effects	of	immune	checkpoint	blockade	need	to	be	managed	with	steroids	and	often	immunosuppressive	agents	such	anti-TNFα,	which	further	adds	to	the	cost	of	this	therapy	(Michot	et	al.,	2016;	Thallinger	et	al.,	2017).		1.4 Tumour	specific	features	can	influence	response	to	immunotherapies		Despite	some	responses	to	immunotherapies	being	rapid,	profound	and	durable	across	a	number	of	malignancies,	including	colorectal,	lung	and	melanoma	(Farkona	et	al.,	2016;	Le	et	al.,	2015),	the	majority	of	patients	treated	with	these	drugs	do	not	respond	(overall	response	rates	for	single	immune	checkpoint	inhibitors	are	often	less	than	30%	(Le	et	al.,	2015;	Alexander,	2016).	Many	tumours	have	primary	resistance	to	these	therapies,	as	they	do	not	initially	respond,	and	even	for	tumours	that	do	respond,	resistance	is	often	acquired	over	the	course	of	treatment	(Sharma	et	al.,	2017).	In	order	to	avoid	treating	patients	unnecessarily	using	a	high	cost	therapy	that	may	not	work,	and	may	result	in	toxic	side	effects,	it	is	necessary	to	identify	tumour	features	that	can	predict	a	good	clinical	response,	or	alternatively	resistance,	to	immunotherapies.		1.4.1. Predictors	of	response	to	immunotherapy		Whilst	predictive	features	of	tumours	can	be	context	and	immunotherapy	treatment	dependent,	many	are	emerging	as	highly	informative	markers	for	response	across	multiple	tumour	types.	Tumour	cells	often	express	antigens	capable	of	recognition	by	the	host	immune		 	 	 11	system,	which	can	then	be	targeted	by	an	immunotherapy,	as	described	in	the	previous	sections.	Tumour	specific,	or	associated,	antigens	(TSA/TAAs)	can	be	generated	through	aberrant	expression	of	germline	(CG	antigens)	or	tissue	specific	genes	(differentiation	antigens)	(Vigneron,	2015),	as	well	as	through	presentation	of	peptides	derived	from	somatically	altered	proteins	(neoantigens),	including	SNVs,	indels	and	gene	fusions	(Akers	et	al.,	2010;	Brown	et	al.,	2014;	Liu	and	Mardis,	2017).	Antigens	are	exposed	to	the	host	immune	system	through	presentation	on	the	tumour	cell	surface	by	proteins	of	the	major	histocompatibility	complex	class	I	(MHC-I),	for	binding	and	recognition	by	T	cell	receptors	(TCRs)	on	CD8+	cytotoxic	T	cells	(Liu	and	Mardis,	2017).	Given	that	protein	expression	of	the	MHC	class	I	is	undetectable	in	male	germ	cells	(Guillaudeux	et	al.,	1996),	and	is	very	low	in	human	embryonic	stem	cells	(Drukker	et	al.,	2002),	genes	that	are	uniquely	expressed	during	embryonic	development	are	not	recognised	as	foreign	by	the	immune	system.	However,	if	aberrantly	expressed	in	a	tumour	cell	with	functional	MHC-I	protein	expression,	these	genes	can	become	antigenic	and	capable	of	activating	an	immune	response.	Notable	examples	of	genes	that	may	be	antigenic	due	to	their	aberrant	expression	in	tumours	include	members	of	the	MAGE	family	(e.g.	MAGEA1,	MAGEA2)	(Akers	et	al.,	2010;	Vigneron,	2015)	and	Brachyury	(T)	(Palena	et	al.,	2007).		The	MHC	class	I	molecules	are	heterodimers	comprised	of	a	heavy	𝛂	chain	glycoprotein	and	a	𝛃2	microglobulin	chain	(Hewitt,	2003).	HLA	genes	HLA-A,	-B	and	-C	in	humans	encode	the	heavy	chain	of	the	MHC	class	I.	The	𝛂	chain	in	humans	is	highly	variable,	allowing	for	broad	presentation	of	antigens,	due	to	polymorphic	HLA	(Human	Leukocyte	Antigen)	genes	located	on	chromosome	6.	Many	individuals	are	heterozygous	at	the	MHC	loci	for	these	genes,	allowing	for	more	variability	in	HLA	sequence	and	a	higher	number	of	candidate	peptides	to	be	presented	on	the	cell	surface	(Janeway,	2001).	Homozygosity	within	MHC	class	I	alleles	has		 	 	 12	been	shown	to	influence	response	to	cancer	immunotherapy,	as	patients	with	at	least	one	homozygous	HLA	allele	have	poorer	overall	survival	when	treated	with	immune	checkpoint	blockade	(hazard	ratio	(HR)	=	1.40),	as	these	patients	have	less	variation	in	the	HLA	alleles,	and	so	less	variation	in	the	neoantigens	that	can	be	presented	to	the	immune	system	(Chowell	et	al.,	2017).			As	neoantigens	found	in	tumour	cells	are	generated	from	somatic	alterations	in	protein	coding	genes,	tumours	with	high	mutational	loads	typically	have	higher	neoantigen	loads	and	thus	are	more	susceptible	to	recognition	by	the	host	immune	system.	High	mutational	load	is	often	found	in	tumours	that	have	microsatellite	instability	(MSI),	caused	by	somatic	alterations	often	occurring	in	mismatch	repair	genes,	including	MLH1,	MSH3	and	MSH6,	generating	deficiency	in	DNA	mismatch	repair	processes	within	the	tumour	(MMR-d)	(Muzny	et	al.,	2012).	Microsatellite	instability	is	commonly	observed	in	a	subtype	of	colorectal	cancers	(CRC),	consistent	with	the	hypermutated	phenotype.	Microsatellite	instability	has	proven	to	be	an	indicative	marker	for	response	to	immunotherapy,	particularly	in	the	context	of	checkpoint	blockade,	where	patients	with	mismatch	repair	processes	intact	had	objective	response	rates	of	0%,	compared	with	>40%	that	had	mismatch	repair	deficiency	(Le	et	al.,	2015).	One	particular	PD-1	inhibitor,	pembrolizumab,	has	recently	gained	FDA	approval	for	solid	tumours	with	MSI	status	that	have	progressed	on	prior	therapy	and	have	no	alternative	therapy	options	(Stark,	2017).	This	is	now	the	first	molecular	feature	of	a	tumour	approved	as	a	biomarker	of	response	to	a	therapy	regardless	of	the	tumour	primary	origin.			Unlike	somatic	mutation	loads,	somatic	copy	number	alterations	(SCNAs),	particularly	arm	and	whole	chromosome	alterations,	appear	to	have	a	negative	influence	on	patient	response	to	checkpoint	inhibition	(Davoli	et	al.,	2017).	Tumours	with	higher	levels	of	copy	number		 	 	 13	alterations	displayed	down	regulation	of	gene	signatures	associated	with	the	immune	system	compared	to	those	with	lower	copy	number	alterations.	Furthermore,	the	same	study	demonstrated	that	melanoma	patients	treated	with	anti-CTLA4	therapy	that	displayed	higher	SCNAs	had	poorer	survival,	compared	to	those	with	lower	SCNAs	(Davoli	et	al.,	2017).	The	contrast	between	how	mutational	loads	and	copy	number	alterations	may	influence	a	patient’s	response	to	immune	checkpoint	blockade	demonstrates	the	need	for	complete	genomic	information	of	tumours	to	best	predict	how	a	patient	will	respond	to	a	particular	therapy,	as	individual	molecular	features	do	provide	enough	information	to	accurately	determine	response.		As	immunotherapies	drive	cancer	eradication	through	stimulation	of	the	host	immune	system,	it	stands	to	reason	that	an	infiltration	of	anti-tumour	immune	cells	at	the	site	of	the	tumour	would	influence	patient	response.	Infiltration	of	adaptive	immune	cells	at	the	site	of	the	tumour	has	been	shown	to	influence	patient	overall	survival	and	response	to	checkpoint	blockade,	particularly	in	the	context	of	CRC	and	melanomas	(Galon	et	al.,	2016;	Topalian	et	al.,	2016).	It	is	also	of	note	that	location	of	infiltrating	immune	cells	can	influence	response,	with	infiltrating	T	cells	within	the	tumour	improving	survival	and	patient	response	to	immune	checkpoint	blockade,	compared	to	infiltration	of	the	surrounding	stroma	(Mlecnik	et	al.,	2016;	Tumeh	et	al.,	2014).		Interestingly,	colorectal	tumours	with	an	MSI	phenotype	have	been	shown	to	have	higher	expression	of	marker	genes	for	infiltrating	T	cells	(CD8,	GZMB)	and	soluble	immune	factors	(IFNG)	than	MSS	(microsatellite	stable)	tumours,	including	markers	for	cytotoxic	and	CD8	cells	(Mlecnik	et	al.,	2016).	This	supports	the	notion	that	MSI	tumours	have	an	increased	response	to	immune	checkpoint	inhibition	(Le	et	al.,	2015).			 	 	 14		Figure	1–1:	Combination	of	T	cell	infiltration	and	high	mutational	load	predict	patient	response	to	immunotherapy	more	effectively	than	single	markers		Kaplan	Meier	plots	showing	the	time	on	treatment	(days)	for	a	subset	of	POG	(Personalised	Oncogenomics)	patients	treated	with	immune	checkpoint	blockade	(ICB)	(n=53).	(A)	Patients	with	higher	predicted	T	cell	infiltration	appear	to	do	better	on	ICB	than	patients	with	lower	scores.	(B)	Patients	with	a	combination	of	markers	(high	T	cell	infiltration	and	high	mutational	load)	had	a	more	prolonged	response	to	immune	checkpoint	blockade,	with	patients	able	to	stay	on	treatment	for	a	longer	duration	than	those	with	low	biomarker	scores.	T	cell	infiltration	alone	is	not	as	effective	in	predicting	patient	response	with	a	combination	of	markers.	High	T	cell	infiltration	was	defined	as	above	the	80th	percentile	of	all	patients	in	the	POG	cohort	(n=549)	as	predicted	by	CIBERSORT.	T	cell	scores	were	defined	as	the	total	score	for	all	T	cell	subtypes	excluding	regulatory	T	cells.	High	mutation	count	was	defined	as	above	the	median	for	the	POG	cohort	(n=549).	Figure	created	by	Hillary	Pearson	and	Emma	Titmuss,	and	Emma	Titmuss	generated	data.	Immune	checkpoint	inhibitors	include	nivolumab,	pembrolizumab,	tremelimumab,	ipilimumab,	atezolizumab,	durvalumab	and	BMS-98605.	P	values	were	generated	using	a	log-rank	test,	and	the	value	displayed	in	B	corresponds	to	differences	between	the	best	and	worst	performing	groups	(high	T	cells,	high	mutation	and	low	T	cells,	low	mutation).				+ + + + ++ + + ++ ++++++++++++++ ++++ ++0.000.250.500.751.00 ++High T cells++++ ++ +++ +++++++0 100 200 300 400TimeLow T cellsHigh T cells, High MutationHigh T cells, Low MutationLow T cells, High MutationLow T cells, Low Mutation0 100 200 300 400TimeProbability of Continued Treatment0.000.250.500.751.00Probability of Continued Treatmentp = 0.031p = 0.21A BNumber at risk++Number at risk++++0 100 200 300 400Time0 100 200 300 400Time14399132301007263811100041647210000	 	 	 15	Many	biomarkers	of	response	to	immune	checkpoint	inhibition	have	been	demonstrated	to	be	informative	in	specific	tumour	types,	but	this	can	make	it	hard	to	decipher	their	application	in	other	situations.	Furthermore,	some	biomarkers	(mutational	load	or	MSI	status	and	T	cell	infiltration)	have	been	shown	to	be	more	effective	at	predicting	patient	survival	when	used	in	combination,	particularly	in	the	case	of	colorectal	cancers	(Mlecnik	et	al.,	2016),	and	we	have	also	demonstrated	this	association	with	the	immunotherapy	treated	patients	from	the	Personalised	Oncogenomics	(POG)	cohort	(Figure	1–1).			1.4.2. Tumour	resistance	to	immunotherapies		Just	as	specific	tumour	characteristics	can	increase	tumour	sensitivity	to	immunotherapies,	many	can	confer	resistance	to	these	therapies.	Resistance	to	immunotherapies	may	be	inherent	to	tumours,	or	can	emerge	during	treatment	as	a	subset,	or	sub	clone,	of	tumour	cells	possessing	a	resistant	phenotype	can	expand	during	the	selective	pressure	administered	by	therapy	(Sharma	et	al.,	2017).			The	process	of	acquired	resistance	to	immunotherapy,	known	as	immunoediting,	involves	three	stages;	elimination,	equilibrium	and	evasion	(Mittal	et	al.,	2014).	The	elimination	stage	occurs	following	activation	of	an	immune	response,	where	tumour	cells	are	targeted	and	destroyed.	Equilibrium	then	occurs,	where	the	tumour	may	appear	to	be	dormant	for	some	time,	but	individual	cells,	or	sub	clones	of	cells,	that	were	resistant	to	the	immune	response	begin	to	expand,	leading	to	the	evasion	stage.	Mechanisms	by	which	tumour	are	resistant	to	immune	elimination	may	be	intrinsic	to	the	tumour	cells	themselves,	or	may	be	due	to	surrounding	cells	and	the	microenvironment	(Mittal	et	al.,	2014;	Sharma	et	al.,	2017).			 	 	 16	As	described	previously,	immunogenic	peptides	presented	by	tumour	cells	enable	recognition	by	the	immune	system.	Tumours	lacking	in	mutation	burden,	and	neoantigen	load,	do	not	respond	as	well	to	immune	based	therapies	(Goodman	et	al.,	2017;	Lauss	et	al.,	2017;	Sharma	et	al.,	2017).	Equivalently,	disruption	of	the	neoantigen	presentation	machinery	is	a	mechanism	of	resistance	to	immunotherapies	that	has	been	described	in	the	literature.	Disruptive	somatic	mutations	in	genes	associated	with	the	MHC	class	I	antigen	presentation	machinery,	including	HLA-A,	-B	and	-C,	B2M	and	TAP,	have	been	associated	with	resistance	to	immunotherapies	(Chang	et	al.,	2005;	Rooney	et	al.,	2015).	Loss	of	heterozygosity	at	the	HLA	loci	has	also	been	associated	with	poorer	outcome	for	patients	treated	with	checkpoint	inhibitors	(Chowell	et	al.,	2017).			As	T	cells	can	drive	immune	responses	through	activation	of	the	IFNγ	pathway,	disruption	of	this	signalling	cascade	has	been	described	in	the	literature	as	a	mechanism	of	resistance	to	CTLA-4	checkpoint	blockade	in	melanomas	(Gao	et	al.,	2016).	Other	resistance	mechanisms	inherent	to	tumour	cells	that	have	been	noted	include	enhanced	signalling	of	the	MAPK	and	PI3K	pathways	as	these	are	inhibitory	to	immune	responses	(Sharma	et	al.,	2017).		Alterations	in	tumour	cells	can	also	affect	the	phenotypes	of	various	immune	cells	within	the	microenvironment,	particularly	those	that	contribute	towards	a	“pro-tumour”	phenotype.	Overexpression	of	inhibitory	immune	checkpoint	ligands	on	tumour	cells,	such	as	PD-L1	and	-L2	can	prevent	tumour	cell	recognition	by	T	cells	(Sharma	et	al.,	2017).	Furthermore,	higher	expression	of	inhibitory	receptors,	including	PD-1,	CTLA-4	and	TIM-3,	can	be	found	on	T	cells	as	they	become	exhausted	during	an	extended	immune	response,	such	as	that	seen	with	immunotherapy	in	cancer,	as	discussed	in	section	1.2	(Jiang	et	al.,	2015;	Sharma	et	al.,	2017).				 	 	 17	Production	of	numerous	chemokines	by	tumour	cells	can	further	contribute	to	changes	in	the	microenvironment	via	recruitment	of	inflammatory,	pro-tumour	cells.	For	example,	secretion	of	ligands	CCL5	and	CXCL8	by	malignant	cells	are	known	to	recruit	myeloid	derived	suppressive	cells	(MDSCs)	to	the	site	of	the	tumour	(Sharma	et	al.,	2017).	These	cells,	and	Tregs,	are	then	able	to	down	regulate	immune	activities	and	shift	the	overall	environment	to	one	that	is	more	favourable	to	the	tumour.			1.5 Genomics	as	a	tool	to	guide	treatment	personalisation		In	order	to	use	biomarkers	to	guide	immunotherapy	treatment	to	its	highest	potential,	methods	of	assessing	tumour	mutational	and	neoantigen	loads,	gene	expression	and	immune	infiltration	need	to	be	both	efficient	and	accurate.	Efficiency	is	of	particular	importance	in	this	scenario,	as	the	costs	of	immunotherapies	are	high	(up	to	USD$1,000,000	annually	(Andrews,	2015)),	compared	to	a	chemotherapy	agent	such	as	paclitaxel	(~USD$80,000	annually	in	2012)	(Siddiqui	and	Rajkumar,	2012).	Traditional	genomic	analysis	tools,	including	whole	genome	and	transcriptome	sequencing,	can	be	readily	implemented	for	immunogenomics	(Hackl	et	al.,	2016),	enabling	the	use	of	common	techniques	to	analyse	biomarkers	for	immunotherapy.			1.5.1. Genomics	methods			Mutation	calling	using	whole	genome	sequencing	(WGS)	data	can	provide	details	on	the	mutational	load	of	the	tumour,	along	with	the	MSI	status	and	potential	driver	mutations	of	the	cancer.	The	introduction	of	next	generation	sequencing	methods	has	significantly	reduced	the		 	 	 18	costs	of	genome	sequencing,	from	$340,000	to	$4200	between	2008	and	2015	(Muir	et	al.,	2016).	Continuation	along	this	trajectory	further	supports	the	use	of	whole	genome-based	approaches,	as	the	comprehensive	knowledge	obtained	is	unrivalled.			RNA	sequencing	(RNA-Seq)	enables	characterisation	of	the	transcriptome	of	the	tumour,	identifying	up	or	down	regulated	genes	when	compared	to	normal	tissues	or	other	cancers.	This	data	can	further	the	analysis	for	assessing	tumour	drivers	and	suggestion	of	therapies	to	target	tumour	abnormalities.	Furthermore,	WGS	and	RNA-Seq	data	can	be	utilised	by	immunogenomics	tools	(Hackl	et	al.,	2016)	in	order	to	identify	patient	specific	HLA	alleles	(and	subsequently	patient	MHC-neoantigen	binding	predictions	(Nielsen	and	Andreatta,	2016;	Szolek	et	al.,	2014)),	predictions	of	immune	cell	infiltration	within	the	tumour	biopsy	sample	(Newman	et	al.,	2015)	and	also	the	diversity	of	the	TCR	sequences	of	infiltrating	T	cells	(Bolotin	et	al.,	2015).	Bioinformatics	methods	can	also	be	supplemented	with	traditional	laboratory	techniques,	including	immunohistochemistry,	to	confirm	predictions	of	immune	infiltration	and	obtain	spatial	information	of	infiltrating	cells	relevant	for	therapy	decisions.		1.5.2. A	personalised	oncogenomics	case	study		The	Personalised	Oncogenomics	program	at	BC	Cancer	uses	whole	genome	and	transcriptome	data	to	align	late	stage	metastatic	cancer	patients	to	treatments	that	target	a	specific	driver	of	the	individual’s	tumour,	thus	aiming	to	address	the	issue	of	intra-tumoural	heterogeneity.	Personalised	approaches	to	cancer	therapy	aim	to	inactivate	the	cancer-driving	mechanism	or	apply	a	selective	pressure	to	a	particularly	sensitive	feature	of	the	tumour,	and	so	have	the	potential	to	be	highly	effective.	Serial	analysis	of	tumours	also	provides	scope	to	detect		 	 	 19	resistance	mechanisms	to	therapies	and	suggest	a	next	line	of	treatment	to	exploit	another	vulnerability	in	the	tumour.		One	particular	patient	enrolled	in	the	POG	program,	with	advanced	metastatic	colorectal	cancer	(CRC)	had	extraordinarily	high	levels	of	FOS	and	JUN,	components	of	the	AP-1	complex,	as	detected	using	RNA-Seq	(Jones	et	al.,	2016).	This	patient,	known	as	POG130,	had	undergone	numerous	therapies	prior	to	the	POG	biopsy,	including	chemo-	and	radiotherapies	during	which	severe	neutropaenia	occurred.	Metastatic	lesions	were	present	across	the	body,	including	the	chest	cavity	and	the	L3	spinal	region	(middle	lumbar	region	of	the	spine).	The	lesion	at	the	L3	position	of	the	spine	was	resected	to	relieve	pain	in	the	patient,	and	a	biopsy	was	submitted	to	the	POG	program	in	late	2014.	Analysis	on	this	first	biopsy	sample	revealed	an	exceptionally	high	mutational	load,	consisting	of	1578	non-synonymous	protein	coding	single	nucleotide	variants	(SNVs)	and	806	protein	coding	indels	(insertions	and	deletions),	similar	to	mutational	loads	previously	associated	with	colorectal	tumours	(Vogelstein	et	al.,	2013).	This	hypermutated	phenotype	was	attributed	to	mutational	inactivation	of	numerous	mismatch	repair	genes	(MLH1,	MSH3,	MLH3,	MSH6	and	POLN).	As	mentioned	previously,	this	high	mutational	burden	and	microsatellite	instability	can	be	predictive	of	both	overall	patient	survival	(Gryfe	et	al.,	2000)	and	responses	to	immune	checkpoint	blockade	(Le	et	al.,	2015),	particularly	in	CRCs.			 	 	 20		Figure	1–2:	The	tumour	has	a	very	high	mutational	load	as	a	result	of	mismatch	repair	deficiency	Scatter	plot	showing	the	original	POG	biopsy	of	this	patient	compared	to	all	other	adult	patients	enrolled	in	the	program	at	the	time,	with	available	data	(n=667).	X	axis	–	total	number	of	single	nucleotide	variants	(SNVs).	Y	axis	–	Microsatellite	instability	score,	as	predicted	by	MSIsensor	(Niu	et	al.,	2014),	based	upon	the	somatic	status	of	microsatellites	in	the	genome.	Red	dot	–	indicates	the	patient	in	this	study.			Other	features	of	the	tumour	were	highly	consistent	with	CRC	literature,	namely	an	APC	inactivation	event,	a	known	initiation	event	for	colorectal	tumours,	and	further	disruption	of	the	WNT	and	cell	cycle	pathways	through	alterations	in	gene	expression	(Fearon	and	Vogelstein,	1990;	Muzny	et	al.,	2012).	Also	consistent	with	the	known	landscape	of	CRCs	was	altered	signalling	through	the	AKT	pathway,	with	mutations	identified	in	PIK3CA,	PIK3R1	and	PTEN	(Muzny	et	al.,	2012).	Transcriptomic	analyses	of	the	tumour	revealed	high	outlier	expression	of	two	genes,	FOS	and	JUN,	which	are	part	of	the	AP-1	(activator	protein	1)		 	 	 21	transcription	factor	complex.	Gene	expression	levels	(RPKM)	for	c-FOS	and	c-JUN	were	in	the	98th	and	100th	percentiles	respectively	when	compared	to	all	POG	patients	(n=667).		Identification	of	gene	expression	outliers	and	somatic	mutation	variants	provided	a	number	of	possible	targets	for	therapy,	including	CDK	(cyclin	dependent	kinase)	inhibitors	(for	targetting	the	disrupted	cell	cycle),	or	AXIN	inhibitors	to	target	the	WNT	pathway,	however,	targetting	the	high	expression	of	FOS	and	JUN,	two	well-known	oncogenes	that	together	form	the	AP-1	complex,	was	decided	upon,	which	will	be	described	in	the	next	section.		1.6 Irbesartan	as	a	cancer	therapy	1.6.1. Selection	of	irbesartan	as	a	therapy		The	AP-1	complex	is	known	to	have	many	roles	within	a	cell,	including	activation	of	proliferation	pathways	(Shaulian	and	Karin,	2002).	Unsurprisingly,	members	of	the	AP-1	complex,	particularly	c-JUN,	are	overexpressed	in	some	cancers,	and	disruption	of	the	functional	complex	has	resulted	in	cell	cycle	arrest	in	colorectal	cancers	(Gurzov	et	al.,	2008;	Suto	et	al.,	2004;	Zhang	et	al.,	2005).	Interestingly,	the	AP-1	complex	also	has	roles	in	immune	cells;	it	is	up	regulated	in	T	cells	following	the	interaction	of	the	TCR	with	an	antigen,	leading	to	T	cell	activation	and	production	of	IL-2,	a	cytokine	that	is	required	for	T	cell	proliferation	and	differentiation	(Foletta	et	al.,	1998).			Upstream	of	FOS	and	JUN,	a	heterozygous	frame-shift	mutation	was	detected	in	AGTR1	(angiotensin	II	receptor	I)	at	a	C	terminal	phosphorylation	site	(S338)	(Jones	et	al.,	2016).	This	receptor,	which	is	a	core	component	of	the	angiotensin	II	signalling	pathway,	signals	through	up-regulation	of	signalling	kinases,	including	JNK	and	ERK,	leading	to	phosphorylation	and		 	 	 22	activation	of	the	AP-1	complex	components	FOS	and	JUN	(Miller	et	al.,	2010).	The	AGTR1	receptor	had	very	low	gene	expression	level	detected	in	the	RNA-Seq	(9th	percentile	of	all	POG	cases	with	detectable	levels	of	AGTR1).	Angiotensin	receptor	blockers	(ARBs)	are	one	family	of	antihypertensive	drugs	able	to	negatively	regulate	the	angiotensin	II	pathway,	eliciting	their	effects	through	non-competitive	inhibition	of	the	angiotensin	II	receptor	(AGTR1)	(Barreras	and	Gurk-Turner,	2003).	Inhibition	of	the	target	receptor	results	in	disruption	of	angiotensin	II	ligand	binding,	leading	to	reduced	catecholamine	and	aldosterone	release	and	vasodilation	of	blood	vessels,	alleviating	hypertension	(Hsueh	and	Wyne,	2011).	Severe	side	effects	are	very	rarely	seen	with	ARBs,	with	only	~2%	of	patients	experiencing	gastrointestinal	symptoms	(Michel	et	al.,	2013).							 	 	 23		Figure	1–3:	Angiotensin	signalling	pathway	can	be	targeted	by	ARBs	and	ACE-I	A	pathway	diagram	showing	the	key	components	of	the	angiotensin-signalling	pathway,	and	where	ARBs	interact	with	the	pathway.	ACE	–	Angiotensin	converting	enzyme,	ACE-I	–	Angiotensin	converting	enzyme	inhibitor,	AGTR1/2	–	Angiotensin	receptor	I/2,	ARB	–	Angiotensin	receptor	blocker,	ADH	–	Antidiuretic	hormone.		Blockade	of	angiotensin	signalling,	through	inhibition	of	AGTR1,	has	previously	been	demonstrated	in	the	literature	to	have	benefit	in	a	cancer	setting,	both	in	vitro	and	in	a	clinical	setting	(Engineer	et	al.,	2013;	Lee	et	al.,	2014a).	Of	particular	relevance	to	this	thesis,	colorectal	cancer	patients	on	treatment	with	ARBs	or	ACE	(angiotensin	converting	enzyme)	inhibitors	(ACE-I)	in	combination	with	beta	blockers	(BBs)	(Figure	1–3)	have	demonstrated	increased	survival	(a	median	of	1341	days	for	patients	on	these	drugs,	compared	to	695	days	for	the	unexposed	group)	(Engineer	et	al.,	2013).	Furthermore,	one	particular	ARB,	irbesartan,	has	demonstrated	the	ability	in	vitro	to	down	regulate	the	AP-1	complex	in	T	cells	(Cheng	et	AngiotensinogenAngiotensin IAngiotensin II+Renin+ACEAGTR1 AGTR2-ACE-I-ARBNon-ACEpathwaysVasoconstrictionIncrease in blood pressureRenal retention of Na+ and H20VasodilationADH secretion	 	 	 24	al.,	2004),	which	is	the	intended	effect	in	tumour	cells	within	this	POG	case.	For	these	reasons,	irbesartan	was	selected	as	a	personalised	therapeutic	for	this	POG	case	(Jones	et	al.,	2016).		Figure	1–4:	Patient	timeline	of	treatment,	and	clinical	response	to	irbesartan.		(A)	Patient	timeline	of	therapies	since	initial	diagnosis	in	2010.	Coloured	bars	indicate	different	therapies;	purple	circles	represent	POG	biopsy	dates	and	triangles	indicate	tumour	resections.	Red	arrows	along	the	timeline	indicate	blood	draws.	Both	PET	(positron	emission	tomography)	scans	on	the	left	are	from	the	patient	before	treatment	with	irbesartan,	and	the	images	on	the	right	are	from	the	patient	after	5	weeks	on	irbesartan.	Figure	is	adapted	from	(Jones	et	al.,	2016).	Malignant	lesions	are	visible	on	the	PET	scans	as	yellow	dots.	White	dashed	boxes	indicate	lesions	that	have	disappeared	following	irbesartan	treatment.					 	 	 25	The	patient	response	to	irbesartan	therapy	was	remarkable;	metastatic	lesions	were	rapidly	eradicated	within	a	short	period	of	only	5	weeks	(Figure	1–4).	Moreover,	the	response	observed	was	durable.	A	complete	response	lasted	for	approximately	18	months	before	the	patient	relapsed	with	a	mass	in	the	L3	spinal	region.	At	this	point,	a	second	biopsy	sample	for	POG	analysis	was	taken,	and	genomic	analyses	were	conducted	to	compare	the	two	biopsies	(Figure	1–4).		1.6.2. Alternative	roles	of	irbesartan		Aside	from	their	target	receptor,	AGTR1,	members	of	the	ARB	drug	family	have	been	demonstrated	to	have	off-target	effects,	the	most	well-studied	through	interaction	with	PPARγ,	a	nuclear	receptor	involved	in	regulation	of	insulin	sensitivity	(Lee	et	al.,	2014a;	Marshall	et	al.,	2006;	Michel	et	al.,	2013;	Schupp	et	al.,	2005).	Irbesartan	and	telmisartan	have	displayed	selective	PPARγ	modulation	at	a	level	comparable	to	that	of	a	full	agonist,	pioglitazone	(Schupp	et	al.,	2005).	Interestingly,	PPARγ	has	been	proposed	as	a	ligand	activated	tumour	suppressor,	as	activation	of	the	receptor	can	inhibit	proliferation	and	induce	apoptosis	in	a	range	of	in	vitro	studies	and	some	mouse	models	(Campbell	et	al.,	2008;	2008).			Both	AGTR1	and	PPARγ	are	known	to	have	roles	in	inflammatory	processes	(Benigni	et	al.,	2010;	Szeles	et	al.,	2007),	with	opposing	actions.	Angiotensin	II	signalling	through	AGTR1	has	pro-inflammatory	effects	through	production	of	inflammatory	chemokines,	such	as	MCP-1,	IL-12	and	TGFB	(Suzuki	et	al.,	2003).	Conversely,	PPARγ	exhibits	an	anti-inflammatory	effect	(Szeles	et	al.,	2007).	PPARγ	is	expressed	in	cells	of	the	immune	system,	including	macrophages,	dendritic	cells,	T	cells	and	B	cells	(Szeles	et	al.,	2007).	In	vivo	models	of	chronic	inflammatory	conditions,	colitis	and	rheumatoid	arthritis,	have	displayed	a	reduction	in		 	 	 26	symptoms	when	treated	with	PPARγ	modulators,	rosiglitazone	and	troglitazone	(Kawahito	et	al.,	2000;	Su	et	al.,	1999;	Szeles	et	al.,	2007).	Interestingly,	there	are	also	literature	reports	of	unconventional	uses	of	irbesartan,	and	notable	side	effects.		In	combination	with	statins,	irbesartan	has	demonstrated	clinical	improvement	in	patients	with	Ebola	virus	disease	(EVD),	in	a	trial	in	Sierra	Leone	(Fedson	et	al.,	2015).	This	response	is	believed	to	be	a	result	of	restoring	endothelial	barrier	integrity,	which	is	damaged	during	infection	with	EVD,	leading	to	severe	loss	of	fluid.	The	ability	of	ARBs	to	alter	endothelial	cell	permeability	has	also	been	demonstrated	in	vitro	(Bodor	et	al.,	2012).	Side	effects	of	irbesartan	treatment	have	been	reported	in	a	single	patient	case	study,	where	the	drug	had	caused	a	hypersensitivity	rash,	a	condition	caused	by	immune	recognition	of	non-infectious	antigens	(Cardoso	et	al.,	2016;	Janeway,	2001).	Some	drugs	have	been	reported	to	cause	these	types	of	reactions	through	interaction	with	patient	MHC	proteins,	or	binding	to	presented	peptides,	thus	altering	their	appearance	to	TCRs	and	rendering	them	immunogenic	(Bharadwaj	et	al.,	2012).			1.7 Research	hypotheses	and	outline		The	rapid	and	sustained	response	observed	in	this	patient,	combined	with	the	high	microsatellite	instability	and	mutational	load	detected	in	the	original	tumour	biopsy	led	to	the	hypothesis	that	an	activation	of	the	immune	system	may	have	occurred	between	the	two	biopsies,	which	was	responsible	for	the	reduction	in	tumour	burden	across	the	body.	The	methods	used	to	address	this	hypothesis	are	listed	in	Chapter	2,	and	a	detailed	discussion	of	results	follows	in	Chapter	3.				 	 	 27	Chapter	3.1	focuses	on	the	broad	genomic	differences	between	the	two	serially	obtained	tumour	biopsies	as	part	of	the	POG	program	(depicted	in	Figure	1–4),	with	the	aim	of	identifying	changes	that	may	reflect	the	resistance	observed	in	the	patient.	The	microsatellite	instable	phenotype	of	this	particular	case	created	difficulties	when	trying	to	distinguish	between	mutations	that	may	be	driving	a	resistance	mechanism,	and	those	that	are	likely	passenger	mutations,	however,	the	vast	majority	of	mutations	were	shared	at	comparable	frequencies	between	the	two	biopsies.		In	Chapter	3.2,	I	investigated	differences	in	gene	expression	between	the	two	time	points,	in	an	attempt	to	identify	particular	pathways	involved	with	the	astounding	response	observed	in	the	patient.	Through	this	analysis,	I	revealed	that	an	enrichment	of	immune	related	pathways	was	present	following	treatment	with	irbesartan,	alluding	to	a	possible	role	of	immune	activation.	These	observations	supported	the	hypothesis	that	there	had	been	an	increase	in	immune	cell	infiltration	between	the	two	biopsies,	which	was	further	investigated	in	Chapter	3.3.		Further	investigation	into	immune	infiltration	is	reported	in	Chapter	3.3,	where	immunohistochemistry	confirmed	changes	in	the	tumour	microenvironment	following	irbesartan	therapy.	This	was	followed	up	with	bioinformatics	analyses	to	further	assess	the	changes	in	the	microenvironment,	assessing	alterations	in	the	repertoire	of	infiltrating	T	cells,	and	potential	neoantigens.		The	final	section	of	this	thesis	includes	a	discussion	of	the	findings	and	limitations	of	this	study,	as	well	as	hypotheses	that	have	emerged	during	this	project.			 	 	 28	2 Materials	and	Methods	2.1 Patient	samples	and	processing		Informed	written	consent	was	obtained	from	the	patient	for	tumour	whole	genome	and	transcriptome	sequencing	as	part	of	the	Personalised	Oncogenomics	(POG)	research	study	(Jones	et	al.,	2016;	Laskin	et	al.,	2015).	The	age	of	the	patient	at	the	time	of	initial	diagnosis	(2010)	was	67.	Samples	were	embedded	and	frozen	in	OCT	(optimal	cutting	temperature)	compound	for	DNA	and	RNA	extractions.	Sections	were	also	frozen	for	subsequent	histochemistry.	Control	normal	DNA	was	obtained	from	patient	peripheral	blood	(Jones	et	al.,	2016).		Whole	genome	sequencing	was	conducted	at	BC	Cancer	Genome	Sciences	Centre	(BCGSC)	using	the	standard	POG	pipeline,	described	in	Jones	et	al.,	2016.	Briefly,	HiSeq2500	instruments	were	used	to	sequence	genome	and	transcriptome	libraries	with	125	base	paired-end	reads.	Tumour	whole	genomes	were	sequenced	to	a	coverage	of	more	than	80X	and	normal	blood	whole	genomes	to	~40X	(Appendix	C).	Libraries	were	aligned	to	hg19	using	Burrows-Wheeler	Alignment	(BWA)	tool	(v0.5.7)	(Li	and	Durbin,	2010).	CNAseq	(v0.0.6)	and	APOLLOH	(v0.1.1)	(Ha	et	al.,	2012;	Jones	et	al.,	2010)	were	used	to	detect	copy	number	variation	and	regions	of	loss	of	heterozygosity.	Somatic	mutations	were	called	based	upon	an	overlap	of	methods,	using	SAMtools	(v0.1.17),	MutationSeq	(v1.0.2)	and	Strelka	(v0.4.6.2)	(Ding	et	al.,	2012a;	Li	et	al.,	2009;	Saunders	et	al.,	2012).	ABySS	and	Trans-ABySS	(v1.4.8)	(Birol	et	al.,	2009;	Ding	et	al.,	2012a;	Li	et	al.,	2009;	Saunders	et	al.,	2012;	Simpson	et	al.,	2009)	were	used	to	detect	structural	variants	in	genome	and	transcriptome	data	respectively.				 	 	 29	Reads	from	RNA-Seq	data	were	aligned	onto	the	same	genomic	reference,	using	Jaguar	(v2.0.3)	(Butterfield	et	al.,	2014),	and	counts	were	normalised	to	reads	per	kilobase	per	million	mapped	reads	(RPKM).	RNA-Seq	provided	at	least	159M	mapped	RNA-Seq	reads	for	each	of	the	two	tumour	biopsies	(Appendix	C).		2.2 PyClone	clustering	analysis		Distinct	populations	of	cells	with	similar	mutation	patterns,	referred	to	as	clones,	were	inferred	using	SNV	allele	frequencies	and	copy	number	profiles	for	each	biopsy	using	PyClone	(v0.13.0)	(Roth	et	al.,	2014).	As	PyClone	was	designed	for	deeply	sequenced	data	(1000x)	a	higher	number	of	iterations	(n=100,000	with	the	first	10,000	discarded)	and	a	binomial	density	model	were	used	for	the	coverage	from	whole	genome	sequencing	(Appendix	C),	as	recommended	in	the	documentation.	Tumour	content	was	also	input	for	each	of	the	biopsies	to	obtain	the	predicted	cellular	prevalence	of	each	mutation,	and	only	clones	predicted	to	contain	more	than	one	mutation	were	considered	for	the	clonal	evolution	model.	The	phylogeny	of	the	clonal	evolution	model	was	predicted	using	ClonEvol	(v1.0)	(Dang	et	al.,	2017).			2.3 Gene	set	enrichment	analysis		Functional	enrichment	analyses	were	completed	using	differentially	expressed	genes	from	the	RNA-seq	data,	defined	by	a	fold	change	of	2	or	more	in	either	direction	(n=148	lower	abundance	and	n=855	higher	abundance	in	the	second,	post-treatment,	biopsy	compared	to	the	first).	Metascape	(v3.0)	(Tripathi	et	al.,	2015)	was	utilised	to	calculate	the	gene	set		 	 	 30	enrichment	scores	for	the	differentially	abundant	genes	between	the	two	biopsies,	implementing	a	hyper-geometric	test	to	identify	enriched	pathways.	The	functional	groups	that	were	tested	for	included	all	GO	(Gene	Ontology)	(Ashburner	et	al.,	2000;	The	Gene	Ontology	Consortium,	2017)	groups,	which	are	clustered	into	master	groups,	shown	in	Figure	3–4,	in	order	to	reduce	redundancy	between	overlapping	functional	processes.			2.4 Mass	spectrometry	data	analysis		Tandem	mass	spectrometry	was	run	using	3	technical	replicates	for	each	biopsy.	Analysis	of	HPLC	separated	peptide	fragments	was	conducted	using	an	Orbitrap	fusion	(Thermo	Scientific),	and	analysed	using	proteome	discoverer	software,	where	MS	spectra	were	compared	to	a	UniProt	human	proteome	database.		Downstream	differential	expression	analysis	of	the	data	was	conducted	in	R	(v3.3.2)	(R	Core	Team,	2016),	using	the	vsn	(v3.36.0)	(Huber	et	al.,	2002)	and	limma	(v3.30.13)	(Ritchie	et	al.,	2015)	packages	for	normalisation	and	differential	expression,	respectively.			Gene	set	enrichment	was	also	conducted	for	the	mass	spectrometry	data	to	identify	if	similar	patterns	of	functional	changes	between	the	two	biopsies	were	observed	at	the	protein	level.	Deregulated	proteins	were	selected	by	a	fold	change	of	>2	in	either	direction	and	an	adjusted	p	value	<0.05,	providing	a	list	of	n=68	less	abundant	and	n=44	more	abundant	proteins	in	the	second	biopsy	compared	to	the	first.				 	 	 31	2.5 RNA-Seq	deconvolution			Gene	expression	deconvolution	was	tested	using	two	packages,	CIBERSORT	R	(v1.04)	(Newman	et	al.,	2015)	and	MCP	Counter	(v1.1.0)	(Becht	et	al.,	2016),	to	identify	the	best	performing	software	for	POG	RNA-Seq	data.	Both	methods	were	tested	using	control	samples	that	included	POG	cases	with	known	high	B	or	T	cell	infiltration	(from	pathology	review),	and	a	cohort	of	15	purified	T	cell	subset	samples	(CD8+,	CD4+	helper,	CD4	+	follicular	helper),	sequenced	using	the	same	pipeline	as	the	POG	samples,	described	in	section	2.1.	Cell	infiltration	predictions	from	CIBERSORT	were	more	accurate	with	the	control	data	than	MCP	Counter,	shown	in	Figure	3–5,	and	so	CIBERSORT	was	used	for	inferring	cell	infiltration	for	the	two	patient	biopsies,	as	well	as	the	rest	of	the	POG	cohort.	The	CIBERSORT	R	script	was	run	using	the	standard	LM22	signature,	a	gene	expression	matrix	of	547	genes	for	the	predicted	22	immune	cell	types,	and	1000	permutations	in	order	to	generate	p	values	(achieved	through	Monte	Carlo	sampling	of	random	genes	in	the	input	gene	expression	matrix).	In	order	to	provide	comparisons	for	the	patient	sample	in	this	study,	I	ran	CIBERSORT	on	all	adult	(n=549)	and	paediatric	(n=58)	POG	samples,	as	well	as	numerous	TCGA	tumour	samples	in	order	to	provide	background	data	and	comparisons	for	data	from	this	patient.			2.6 Multiplex	immunohistochemistry		Multiplex	immunohistochemistry	(IHC)	was	conducted	by	Katy	Milne	(Nelson	Lab)	at	the	Deeley	Research	Centre	in	Victoria.	Slides	were	stained	with	antibodies	described	in	Appendix	D.	All	slides	were	then	scanned,	using	a	Vectra	automated	imaging	system	(Perkin	Elmer,	Waltham,	MA),	at	4x	and	10	fields	were	randomly	captured	for	each	slide	at	20x.	Images	were		 	 	 32	then	processed	using	inForm	image	analysis	software	(Perkin	Elmer,	Waltham,	MA),	running	tissue	segmentation	algorithms	to	distinguish	cells	present	in	the	tissue	stroma	(stromal	compartment)	from	cells	found	within	the	tumour	tissue	(epithelial	compartment)	and	generating	cell	phenotype	counts	for	each	slide.	A	cell	phenotype	is	defined	as	the	combination	of	markers	that	each	cell	expresses	(for	example,	CD3+CD8+	cells	have	a	different	phenotype	from	CD3+	only	cells).	Counts	were	inspected	visually	to	ensure	results	were	reliable.	Tissue	segmented	regions	(epithelial	or	stromal)	for	each	slide	were	converted	from	the	number	of	pixels	to	the	area	of	tissue	(mm2).	The	average	cell	counts	for	each	phenotype	were	then	normalised	by	the	segmented	area	to	obtain	the	cell	density	counts	for	each	region,	which	were	then	compared	across	the	biopsy	samples.	Normalised	cell	counts	for	each	biopsy	were	plotted	in	R,	and	significance	values	between	the	first	and	second	biopsies	were	calculated	using	a	paired	t-test.			Tissue	slides	of	MSI	CRC	tumours	(MLH1	deficient)	obtained	from	the	Gastrointestinal	Biobank	(GIBB)	at	Vancouver	General	Hospital	(VGH)	were	analysed	along	with	the	patient	tissue	samples	to	provide	comparison	cell	infiltration	levels	for	the	MSI	subtype	of	CRC,	and	to	determine	if	there	was	a	high	level	of	immune	infiltration	at	the	time	of	the	first	biopsy.			2.7 T	cell	receptor	repertoire	analysis		T	cell	receptor	(TCR)	sequences	were	detected	using	transcriptome	data	from	both	patient	biopsies,	processed	using	MiXCR	(v2.1)	(Bolotin	et	al.,	2015).	RNA	FASTQ	files	were	generated	from	paired-end	bam	files	using	Picard	(v2.17)	(Broad	Institute,	2018),	which	were	then	used	with	MiXCR.	MiXCR	was	run	using	the	RNA-Seq	workflow,	described	in	the	package	documentation.	Briefly,	reads	are	aligned	against	reference	V,	D,	J	and	C	genes,	and	full	CDR3		 	 	 33	regions	are	assembled	and	exported	as	clonotype	sequences	for	TCR	α	or	β	chains.	For	reads	that	only	partially	align	to	the	CDR3	region,	MiXCR	attempts	to	build	contigs	from	these	reads	to	align	them	to	a	full	CDR3	sequence.	TCR	α	or	β	clonotypes	were	then	exported	to	VDJtools	(v1.1.4)	(Shugay	et	al.,	2015)	for	comparisons	between	the	two	biopsies	and	data	visualisation.	VDJdb	(Shugay	et	al.,	2017),	a	curated	database	of	T	cell	receptor	sequences,	was	used	to	obtain	further	information	about	clonotypes	of	interest	detected	in	the	patient	samples,	such	as	those	that	increased	or	decreased	in	abundance	in	the	second	biopsy	compared	to	the	first.		2.8 HLA	typing	and	neoantigen	prediction		Patient	HLA-I	alleles	were	genotyped	to	their	four-digit	code	using	Optitype	(v1.3.1)	(Szolek	et	al.,	2014),	using	both	the	biopsy	1	and	2	RNA	FASTQs,	to	ensure	consistency	of	results.			All	potential	peptide	sequences	that	included	SNVs	in	the	first	biopsy,	between	8-11	amino	acids	in	length	were	considered	as	potential	MHC	class	I	neoantigens.	Binding	affinities	for	all	of	these	peptides	against	the	patient	specific	HLA	alleles	were	generated	using	NETMHCpan	(v3.0)	(Nielsen	and	Andreatta,	2016).	The	same	predictions	were	made	for	the	wild	type	peptides	to	provide	comparisons	for	mutation	specific	binding	affinities.	These	were	then	filtered	to	identify	potential	high	confidence	neoantigens,	based	upon	a	cut	off	of	<50nM,	which	has	been	demonstrated	in	the	literature	(Wick	et	al.,	2014)	to	be	a	useful	marker	for	selectively	testing	neoantigens.				 	 	 34	2.9 Plots	and	visualisation			The	majority	of	plots	were	generated	using	ggplot2	(v2.2.1)	(Wickham,	2009),	with	use	of	RColorBrewer	(v1.1.2)	(Neuwirth,	2014)	in	R	(v3.3.2)	(R	Core	Team,	2016).		T	cell	repertoire	circular	plots	were	generated	using	VDJtools	(Shugay	et	al.,	2015),	and	the	clonal	evolution	model	was	created	using	timescape	(Smith,	2016).				2.10 PBMC	processing	and	ELISPOT	protocol		Blood	draws	(heparin	and	EDTA	collection	tubes)	were	taken	at	every	patient	follow	up	appointment	from	March	2017	(currently	4	blood	draws)	(Figure	1–4)	and	processed	in	the	Stem	Cell	Assay	Lab,	at	BC	Cancer.	Peripheral	blood	mononuclear	cells	(PBMCs)	were	extracted	using	standard	Ficoll	density	gradient	centrifugation	and	cryogenically	frozen	until	they	were	used	in	ELISPOT	assays.	Cell	recovery	from	this	protocol	gave	a	median	cell	count	of	2.1	million	cells	per	vial	(4	vials	per	blood	draw:	2	from	heparin	tubes	and	2	from	EDTA	tubes).		The	top	100	peptides	with	the	strongest	predicted	binding	affinities	were	ordered	from	Peptide	2.0	(https://www.peptide2.com/)	for	testing	in	vitro.	Initial	peptide	screening	was	completed	using	20	unique	pools	of	10	peptides	(Table	2-1),	such	that	each	peptide	was	present	in	only	2	pools.	ELISPOT	plates	were	coated	with	2𝛍g/ml	IFN-𝝲	1-D1K	antibody	and	stored	overnight	at	4°C.	PBMCs	were	thawed	and	rested	overnight,	before	plating	onto	media	blocked	wells	at	a	final	concentration	of	100,000	cells/well.	Peptide	pools	were	added	at	a	concentration	of	10𝛍g/ml,	with	anti-CD3	and	a	CEF	pool	acting	as	positive	controls.	On	day	3,		 	 	 35	the	plate	was	washed	with	PBS	and	coated	with	1𝛍g/ml	biotinylated	IFN-𝝲	7-B6-1	antibody	for	2	hours.	The	plates	were	then	washed,	coated	with	Streptavidin	horseradish	peroxidase	(Strep-HRP),	and	developed	using	filtered	TMB	substrate	for	12	minutes	in	the	dark.	IFN-𝝲	spots	were	then	read	and	counted	using	an	AID	ELISPOT	reader.			ELISPOT	assays	conducted	using	irbesartan	were	completed	in	the	same	manner,	with	a	drug	concentration	of	10𝛍M	per	well.	In	the	case	of	individual	peptide	testing,	these	were	plated	at	a	concentration	of	10𝛍g/ml.																			 	 	 36	Pools	 C1	 C2	 C3	 C4	 C5	 C6	 C7	 C8	 C9	 C10	R1	 P1	 P2	 P3	 P4	 P5	 P6	 P7	 P8	 P9	 P10	R2	 P11	 P12	 P13	 P14	 P15	 P16	 P17	 P18	 P19	 P20	R3	 P21	 P22	 P23	 P24	 P25	 P26	 P27	 P28	 P29	 P30	R4	 P31	 P32	 P33	 P34	 P35	 P36	 P37	 P38	 P39	 P40	R5	 P41	 P42	 P43	 P44	 P45	 P46	 P47	 P48	 P49	 P50	R6	 P51	 P52	 P53	 P54	 P55	 P56	 P57	 P58	 P59	 P60	R7	 P61	 P62	 P63	 P64	 P65	 P66	 P67	 P68	 P69	 P70	R8	 P71	 P72	 P73	 P74	 P75	 P76	 P77	 P78	 P79	 P80	R9	 P81	 P82	 P83	 P84	 P85	 P86	 P87	 P88	 P89	 P90	R10	 P91	 P92	 P93	 P94	 P95	 P96	 P97	 P98	 P99	 P100		Table	2-1:	ELISPOT	pools	enable	testing	multiple	peptides	at	once		This	table	shows	how	peptide	pools	were	designed	in	order	to	test	multiple	peptides	at	once.	Pools	are	shown	along	the	top	and	the	left-hand	side,	labelled	as	either	a	column	(C)	or	row	(R)	pool.	The	100	tested	peptides	are	numbered	from	P1	to	P100,	and	each	peptide	is	present	in	exactly	2	pools,	as	indicated	for	peptide	P33,	which	is	present	in	pools	C3	and	R4.			 	 	 37	3 Results		The	locally	relapsed	tumour	sample	taken	from	the	patient	18	months	after	the	profound	response	to	irbesartan	provided	a	unique	opportunity	to	study	the	response	and	resistance	observed	in	the	patient.	As	the	patient	is	enrolled	in	the	POG	program,	a	variety	of	data	were	obtained	for	each	biopsy,	including	whole	genome	sequencing	(WGS)	and	RNA-Seq,	providing	the	opportunity	to	study	the	effects	of	the	drug	from	more	than	one	perspective.	Analysis	of	the	data	generated	results	that	all	converge	on	one	particular	theme	-	involvement	of	the	immune	system.			3.1 Clonal	Evolution	of	the	Resistant	Tumour		The	mutational	load	of	the	tumour	at	the	time	of	the	first	POG	biopsy	was	high,	with	2152	non-synonymous	coding	single	nucleotide	variants	(SNVs)	and	frame	shift	indels	(insertions	and	deletions),	increasing	to	2306	non-synonymous	coding	SNVs	and	frame-shift	indels	at	the	second	biopsy,	consistent	with	high	mutation	rates	found	in	colorectal	tumours	with	a	high	degree	of	microsatellite	instability	(MSI)	(Muzny	et	al.,	2012;	Vogelstein	et	al.,	2013).			Many	cancers,	including	colorectal,	are	driven	by	clonal	evolution	(Vogelstein	et	al.,	2013);	mutations	or	other	genomic	events	are	selected	for	over	time	that	confer	a	selective	advantage	to	the	tumour.	Tumour	progression	in	colorectal	cancers	(CRCs)	has	been	relatively	well	studied,	and	is	often	initiated	with	a	mutation	in	the	APC	gene	(Kinzler	and	Vogelstein,	1997;	Vogelstein	et	al.,	2013).	This	progression	model	is	supported	by	the	large	number	of	CRCs	that	harbour	a	mutation	in	this	gene	(>50%	in	hypermutated,	and	>80%	in	non-	 	 	 38	hypermutated)	(Muzny	et	al.,	2012).	Cells	containing	this	driver	can	then	proliferate	and	gain	new	mutations,	allowing	them	to	outgrow	surrounding	cells,	which	can	lead	to	development	of	distinct	mutation	clones	within	a	tumour.	Particular	clones	may	have	acquired	mutations	that	enable	resistance	to	certain	therapies,	and	so	are	able	to	continue	to	proliferate	and	repopulate	a	tumour	during	treatment,	whereas	other	clones	may	be	eradicated	due	to	certain	mutations	that	may	confer	treatment	sensitivity.	3.1.1. Detection	of	heterogeneous	clones	within	the	tumour		To	study	the	clonal	evolution	of	the	relapsed	tumour	following	irbesartan	treatment,	PyClone	(Roth	et	al.,	2014)	was	utilised	to	infer	dominant	and	low	frequency	sub-clones,	based	on	somatic	mutations	and	copy	number	changes	from	whole	genome	sequencing	data.	The	high	number	of	clones	detected	in	the	tumour	may	be	reflective	of	various	therapies	that	the	patient	had	before	enrolling	in	POG	(Figure	1-1).		Half	of	the	clones	predicted	are	comprised	of	a	single	mutation,	and	shift	frequencies	independently	of	the	others.	The	biological	implications	of	a	single	mutation	clone	are	unclear,	as	this	may	be	a	result	of	a	highly	heterogeneous	tumour	that	is	difficult	to	predict	clonal	populations	for,	rather	than	truly	a	unique	sub-clone.	Unfortunately,	bulk	sequencing	cannot	provide	information	regarding	the	mutation	status	of	individual	cells	and	so	clustering	analyses	have	limitations	in	this	situation.	However,	inferring	subpopulations	of	cells	with	similar	mutation	frequencies	from	bulk	sequencing	data	(Roth	et	al.,	2014),	can	be	useful	as	mutations	with	biologically	similar	phenotypes	may	shift	during	disease	development,	or	in	response	to	treatment	(Kridel	et	al.,	2016).			Clones	seen	to	emerge,	or	increase	in	cellular	prevalence,	following	treatment	with	irbesartan	are	grouped	into	clone	6	(Figure	3-1	and	Figure	3–2).	Many	genes	in	this	clone	already	had		 	 	 39	mutations	detected	in	the	first	biopsy,	suggesting	that	many	of	the	mutations	that	emerged	following	treatment	are	passenger	mutations	and	are	a	result	of	microsatellite	instability,	and	are	not	a	direct	consequence	of	treatment.	Mutations	clustered	into	clone	2	(n=36)	appear	to	be	selected	against	following	treatment	and	reduce	in	frequency.	The	cellular	prevalence	of	these	mutations	before	treatment	is	already	low,	at	a	frequency	of	0.2.	This	could	suggest	that	either	these	mutations	are	no	longer	required	by	the	tumour	to	survive,	or	that	these	cells	are	more	sensitive	to	treatment	than	others.	There	is	also	the	possibility	that	the	frequency	of	these	mutations	has	decreased	to	a	level	no	longer	detectable	by	mutation	calling	software	we	used.									 	 	 40		Figure	3–1:	Mutational	clones	detected	in	the	patient	biopsies.		Predicted	frequency	of	clones	within	the	two	tumour	biopsy	samples.	Each	clone	consists	of	SNVs	that	shift	in	variant	allele	frequency	in	a	similar	pattern	to	one	another	between	the	two	biopsies.	The	top	panel	(light	blue)	shows	the	cellular	prevalence	(frequency)	of	mutations	in	the	first,	pre-treatment	biopsy,	grouped	by	the	clone	the	mutation	is	predicted	to	belong	to.	The	bottom	panel	(dark	blue)	shows	the	cellular	frequency	of	the	same	mutations,	and	clones,	for	the	post-treatment	sample.		Despite	some	alterations	in	frequency	of	mutations	following	treatment	with	irbesartan,	the	majority	of	mutations	have	a	very	comparable	prevalence	between	the	two	biopsies	(clones	1,	3,	8	and	9).	Low	overall	fluctuation	of	mutation	prevalence	across	the	two	biopsies	may	indicate	that	mutations	present	in	the	tumour	at	the	time	of	treatment	initiation	with	irbesartan	do	not	provide	any	selective	advantage,	or	disadvantage.	This	raises	the	possibility	that	the	drug	is	not	targeting	a	single	pathway,	or	gene,	within	the	tumour,	but	rather	could	be	acting	in	a	different	manner.			0.00.20.40.60.81.00.00.20.40.60.81.01 2 3 4 5 6 7 8 9 10 11 12ClusterFrequencyClone 	 	 	 41		Figure	3–2:	Mutational	changes	between	biopsies	suggest	irbesartan	has	targeted	more	than	a	single	pathway.		(A)	Somatic	SNVs	shared	across	the	two	biopsies	sequenced	from	the	patient.	(B)	Mutation	frequencies	between	both	biopsies,	for	clones	containing	more	than	one	mutation,	coloured	by	the	clone	the	mutation	is	predicted	to	belong	to	(as	inferred	by	PyClone	(Roth	et	al.,	2014)).	The	majority	of	mutations	(n=1676)	are	shared	at	a	similar	frequency	between	biopsies	and	thus	lie	on	the	diagonal	between	the	two	axes.	(C)	Clonal	evolution	between	the	two	biopsies,	predicted	by	ClonEvol	(Dang	et	al.,	2017).	Clone	1,	found	at	very	low	frequencies	in	both	biopsy	1	and	2	in	panel	B,	is	not	included	in	the	evolutionary	model.	(D)	A	timescape	rendition	of	the	evolution	of	the	clones	during	treatment	with	irbesartan.	The	time	the	patient	was	in	remission	is	represented	by	a	bottleneck	in	the	figure.	The	relative	frequency	of	each	clone	at	both	time	points	is	shown	by	the	lines	for	biopsy	1	and	2,	and	alterations	in	frequencies	between	these	two	time	points	is	unknown	but	represented	here	for	illustrative	purposes.			 	 	 42	The	evolutionary	model	of	the	clones	was	inferred	using	ClonEvol	(Figure	3-2C)	(Dang	et	al.,	2017)	and	a	rendition	of	possible	clonal	expansion	is	visualised	using	timescape,	shown	in	Figure	3-2D.	As	the	clone	8	appears	to	be	dominant	in	both	biopsies,	it	is	likely	that	most	other	clones	have	emerged	from	it,	as	is	predicted	by	ClonEvol.	The	evolutionary	model	predicts	that	clone	6,	consisting	of	mutations	that	increase	in	frequency,	is	a	descendent	of	clone	9.	These	two	clones	share	very	similar	cellular	frequencies	in	the	second	biopsy,	providing	evidence	that	the	original	clone	from	the	first	biopsy	is	still	present,	but	a	subset	of	these	cells	have	gained	new	mutations,	leading	to	generation	of	a	new	clone.	A	large	proportion	of	the	mutations	gained	following	irbesartan	treatment	were	in	genes	that	already	had	mutations	(Appendix	E),	which	could	imply	that	these	are	passenger	mutations,	perhaps	arising	as	a	result	of	the	high	microsatellite	instability	in	the	tumour	and	not	as	driver	mutations	of	resistance	to	irbesartan.		Another	contributing	factor	to	adapting	properties	and	tumour	evolution	is	alterations	in	copy	number.	It	has	been	shown	that	the	level	of	copy	number	alterations	can	influence	patient	response	to	immunotherapies,	with	fewer	somatic	copy	number	alterations	(arm	and	whole	chromosome	events	in	particular)	providing	a	survival	benefit	to	patients	(Davoli	et	al.,	2017).	The	original	POG	analysis,	conducted	before	on	the	patient	biopsy	before	treatment	with	irbesartan,	revealed	a	quiet	copy	number	profile	(Appendix	I),	which	remains	the	case	in	the	second	biopsy,	following	irbesartan	treatment,	with	very	little	copy	number	changes	between	the	two	biopsies.		In	addition,	the	low	amount	of	copy	number	alterations	detected	in	the	patient	is	consistent	with	reports	of	CRC	cases	that	have	mismatch	repair	deficiency	and	high	mutational	loads	(Davoli	et	al.,	2017).			 	 	 43	Considering	the	minimal	alterations	in	SNVs	and	CNVs	following	treatment	with	irbesartan,	it	is	possible	that	the	resistance	observed	in	the	tumour	is	not	a	direct	result	of	these	features.	Whilst	these	types	of	alterations	have	been	demonstrated	in	the	literature	to	have	the	ability	of	conferring	resistance	to	a	tumour,	there	are	other	mechanisms	that	may	be	driving	resistance	in	this	patient.	Alternative	mechanisms	capable	of	providing	tumour	resistance	to	therapy	include	changes	in	gene	expression	and	thus	regulation	of	particular	cellular	pathways,	or	changes	in	the	tumour	microenvironment	(Pardoll,	2012;	Sharma	et	al.,	2017).	Investigation	into	changes	in	gene	expression	and	the	tumour	microenvironment	before	and	after	treatment	with	irbesartan	is	described	in	the	following	chapters.		3.2 Gene	expression	changes	are	compatible	with	involvement	of	the	immune	system		Irbesartan,	used	as	a	blood	pressure	medication,	acts	by	down-regulating	the	angiotensin	II	pathway,	through	non-competitive	(alternative	to	the	ligand	binding	region)	inhibition	of	the	angiotensin	II	receptor	(AGTR1)	(Barreras	and	Gurk-Turner,	2003).	Gene	expression	of	AGTR1	in	the	patient	samples,	compared	to	other	CRC	POG	cases	and	data	from	the	Cancer	Genome	Atlas	(TCGA),	is	very	low	(Figure	3-3)	potentially	as	a	result	of	the	heterozygous	frame-shift	mutation	detected	in	the	receptor.	Such	low	expression	of	the	gene	may	suggest	that	the	response	observed	in	the	patient	does	not	require	there	to	be	high	expression	of	the	canonical	target	of	irbesartan.	The	expression	level	of	AGTR1	is	very	similar	in	the	post-treatment	biopsy,	compared	to	the	first	pre-treatment	biopsy	(0.04	to	0.01	RPKM)	detected	in	the	RNA-Seq	data	between	the	two	biopsies,	however,	the	accuracy	of	detecting	changes	of	expression	values	so	low	is	questionable.	The	frame-shift	mutation	detected	in	the	AGTR1	receptor	decreases	in	frequency	following	irbesartan	treatment	(Figure	3–3),	which	suggests		 	 	 44	that	the	mutation	does	provide	the	tumour	with	a	survival	benefit	while	treatment	with	irbesartan.	If	the	frame-shift	mutation	is	deleterious	to	the	function	of	the	receptor,	as	previously	thought,	then	this	may	suggest	that	irbesartan	has	some	off-target	effects.			Figure	3–3:	Angiotensin	II	receptor	I	expression	is	low	in	both	biopsy	samples	compared	to	other	colorectal	cancers	within	POG	and	TCGA.		(A)	Density	plot	showing	gene	expression	(RPKM)	of	AGTR1	for	all	colorectal	cancer	biopsies	within	the	POG	program	(green)	and	all	COAD	samples	in	TCGA	(grey).	AGTR1	expression	for	the	patient	biopsies	are	indicated	by	the	vertical	lines	with	the	solid	light	blue	showing	expression	for	the	first,	pre-treatment,	biopsy	and	dashed	dark	blue	for	the	second,	post-treatment,	biopsy.	(B)	Read	counts	for	normal	and	frame	shift	AGTR1	detected	in	WGS	for	both	biopsies.				 	 	 45	3.2.1. Genes	up-regulated	after	treatment	are	enriched	for	immune	related	pathways			To	study	the	impact	of	irbesartan	treatment	on	the	global	gene	expression	landscape,	a	gene	set	enrichment	analysis	was	completed.	This	method	considers	changes	in	gene	expression	of	multiple	genes	that	converge	on	similar	pathways	and	functions,	to	understand	the	global	impact	of	the	changes.	Genes	determined	to	be	differentially	expressed	(see	Methods)	in	the	second	biopsy	compared	to	the	first	were	analysed	for	gene	set	enrichment.	Over	half	of	the	top	20	gene	sets	found	to	be	enriched	with	up-regulated	genes	were	directly	related	to	immune	processes	(Figure	3-4),	strongly	correlating	an	involvement	of	the	immune	system	with	the	dramatic	response	observed	in	the	patient.	Many	significant	gene	sets	encapsulate	broad	areas	of	the	immune	system,	such	as	‘immune	effector	process’,	or	‘inflammatory	response’.	However,	there	are	some	particularly	interesting	specific	gene	sets	that	emerge,	namely	‘antigen	processing	and	presentation’,	and	‘myeloid	leukocyte	activation’.	These	gene	sets	suggest	the	gene	expression	changes	between	the	two	biopsies	are	linked	to	active	leukocytes,	possibly	recognising	tumour	cells	through	antigen	presentation.	This	is	particularly	relevant	due	to	the	high	mutational,	and	likely	neoantigen,	load	of	the	tumour.	Enrichment	of	gene	sets	related	to	leukocytes	is	likely	to	be	coming	from	cells	infiltrating	the	tumour.	Tumour	contents	between	the	two	biopsies	are	comparable,	with	the	first	biopsy	pathology	estimated	tumour	content	being	43%,	and	the	second	biopsy	being	45%	(Appendix	C).	Therefore,	the	fact	that	these	gene	sets	are	enriched	in	the	second	biopsy	compared	to	the	first	might	suggest	that	there	is	higher	immune	infiltration	present	in	the	sequenced	samples	following	treatment	with	irbesartan.						 	 	 46		Figure	3–4:	Gene	expression	changes	between	biopsies	are	enriched	for	immune	system	processes.		(A)	Top	20	enriched	GO	summary	gene	sets	for	up	regulated	genes	in	the	second	biopsy	compared	to	the	first.	P	values	are	indicated	along	the	x-axis.	The	red	vertical	line	indicates	a	p	value	of	≤0.05.	(B)	Changes	in	gene	expression	of	immune	marker	genes.	The	colour	of	the	box	refers	to	the	fold	change	of	the	gene	expression	compared	to	the	median	expression	of	the	gene	across	the	POG	cohort	(n=570).	Biopsy	one,	pre-treatment,	expression	is	in	the	left	columns,	and	biopsy	2,	post-treatment,	expression	is	in	the	right-hand	columns.	A	darker	red	colour	indicates	a	gene	that	has	a	higher	fold	change	compared	to	the	POG	cohort,	whereas	a	darker	blue	colour	indicates	a	gene	that	is	lowly	expressed	compared	to	the	POG	cohort.	White	boxes	are	those	that	are	equal	to	the	median	of	the	POG	cohort.	Marker	genes	are	grouped	into	sets	of	genes	with	similar	functions.	(C)	Top	20	enriched	GO	summary	gene	sets	for	down	regulated	genes	in	the	second	biopsy	compared	to	the	first.	All	significant	gene	sets	are	in	Appendix	A.	A Bpositive regulation of defense responseNABA MATRISOME ASSOCIATEDNABA CORE MATRISOMEHemostasisCytokine−cytokine receptor interactionresponse to bacteriumextracellular structure organizationnegative regulation of immune system processregulation of immune effector processcytokine productionImmunoregulatory interactions between a Lymphoid and a non−Lymphoid cellSystemic lupus erythematosusleukocyte migrationStaphylococcus aureus infectioncytokine−mediated signaling pathwayadaptive immune responsecell activation involved in immune responseinflammatory responsepositive regulation of immune responselymphocyte activation0 20 40−log10 P.valuePPAR signaling pathwayregulation of DNA bindingnitric oxide biosynthetic processskeletal muscle cell differentiationpositive regulation of response to external stimuluscellular response to lipidresponse to extracellular stimuluscanonical Wnt signaling pathwayanterior/posterior pattern specificationpositive regulation of cell deathbiomineral tissue developmentresponse to growth factoroxygen transportPID ATF2 PATHWAYregulation of DNA−templated transcription in response to stresspositive regulation of epithelial cell proliferationnegative regulation of cell proliferationnegative regulation of transcription by RNA polymerase IIresponse to inorganic substancePID AP1 PATHWAY0.0 2.5 5.0 7.5−log10 P.valueCCCL4CCL5CD163CD247CD274CD28CD3ECD3GCD74CD8ACTLA4CXCL10CXCL9FOXP3GNLYGZMAGZMHGZMKHLA−AHLA−BHLA−CHLA−DOAHLA−DPA1HLA−DPB1HLA−DQA1HLA−DQB2HLA−DRAIFNGLAG3PDCD1LG2PRF1Biopsy 1Biopsy 21510Fold change against POG samples (n=570) PDCD1Antigen presentationT cell activation Immune checkpointsOther cell markers-4Biopsy 1Biopsy 2Biopsy 1Biopsy 2	 	 	 47	Down	regulated	gene	sets	following	treatment	with	irbesartan	do	reflect	the	initial	reason	for	use	of	the	drug,	the	AP-1	complex	members,	FOS	and	JUN	(Figure	3–4),	as	these	genes	are	in	the	most	down	regulated	genes	in	the	post-treatment	biopsy	(Appendix	F).	Other	down-regulated	gene	sets	in	the	post-treatment	biopsy	include	cell	processes	such	as	regulation	of	cell	death	and	stress	responses	(Figure	3–4).	The	enrichment	of	these	processes	may	be	indicative	that	irbesartan	has	induced	these	processes	during	treatment.	Down	regulation	of	these	processes	in	the	relapsed	sample	is	also	interesting,	as	this	may	be	a	resistance	mechanism	to	the	drug.	It	is	also	of	note	that	PPAR	signalling	appears	to	be	down-regulated	following	irbesartan	treatment,	however,	PPARg	itself	is	not	down-regulated	in	the	second	biopsy	(1.16	fold	change),	which	is	known	to	interact	with	irbesartan	in	vitro	(Schupp	et	al.,	2005).			Overall,	there	are	many	more	genes	that	are	up	regulated	following	treatment	with	irbesartan	than	down	regulated	(section	2.3,	Appendix	F),	which	can	be	seen	in	the	significance	of	enriched	processes	for	each	group	(Figure	3–4,	Appendix	A).			3.2.2. Marker	genes	for	immune	cells	are	differentially	expressed			The	most	up	regulated	genes	following	treatment	with	irbesartan	included	marker	genes	for	immune	cells	(Figure	3–4),	consistent	with	the	notion	that	there	has	been	an	involvement	of	the	immune	system	in	the	response	observed	in	the	patient,	during	treatment	with	irbesartan.	In	particular,	T	cell	marker	genes	CD3-E	and	–G,	as	well	as	many	immune	checkpoint	genes	(PD-1,	PD-L1,	PD-L2,	CTLA-4,	CD28),	increase	to	levels	higher	than	the	majority	of	all	POG	samples	(Figure	3–4).	Genes	involved	in	T	cell	activation	also	increase	in	expression	following	treatment	with	irbesartan,	and	many	of	these	(CXCL9,	CD8A,	GZMH,	GZMA,	GNLY,	IFNG,	CCL5,		 	 	 48	CCL4	and	CD247)	have	been	associated	with	increased	expression	in	melanomas	that	respond	to	nivolumab,	a	PD-1	inhibitor	(Riaz	et	al.,	2017).			Substantial	increases	are	also	detected	in	many	antigen	presentation	genes	(Figure	3–4),	including	HLAs	involved	in	the	MHC	class	I,	required	for	immune	recognition	of	foreign	peptides	(Calis	et	al.,	2013).	Following	treatment	with	irbesartan,	MHC	class	I	genes	(HLA-A,	-B,	-C)	are	expressed	at	a	moderate	level	compared	to	all	POG	samples,	whereas	before	therapy	they	were	lowly	expressed	compared	to	this	cohort.	One	gene	of	particular	interest,	involved	in	antigen	presentation	of	MHC	class	II,	is	CD74	as	this	gene	has	been	shown	to	also	interact	directly	with	the	angiotensin	II	receptor	1	(AGTR1)	(Szaszak	et	al.,	2008),	the	target	of	irbesartan,	and	promote	its	degradation	before	the	receptor	is	localised	at	the	cell	surface.	The	fold	change	of	CD74	is	within	the	top	50	most	up	regulated	genes,	in	the	second	biopsy	compared	to	the	first,	detected	in	the	RNA-Seq	data	(Appendix	F).	The	interaction	of	CD74	with	AGTR1	is	compelling,	and	could	imply	a	resistance	mechanism	to	irbesartan,	through	reducing	the	expression	of	the	drug’s	canonical	target,	AGTR1.	If	CD74	was	acting	in	this	manner,	then	this	could	also	explain	the	cause	of	the	increase	in	expression	of	MHC	class	II	genes	following	treatment	(Figure	3–4),	as	the	canonical	function	of	CD74	is	aiding	formation	of	the	MHC	class	II	(Schröder,	2016).	Increases	in	the	expression	of	these	marker	genes	(Figure	3–4)	is	suggestive	of	an	increase	in	infiltration	and	activation	of	immune	cells	at	the	site	of	the	tumour,	supporting	the	observations	made	with	the	gene	set	enrichment	analysis.		An	increasing	number	of	bioinformatics	tools,	including	CIBERSORT	and	MCP	Counter	(Becht	et	al.,	2016;	Hackl	et	al.,	2016;	Newman	et	al.,	2015),		are	designed	to	use	gene	expression	data	to	predict	immune	infiltration	in	tumour	samples	based	upon	gene	expression	signatures	for	each	distinct	immune	cell	type.	After	conducting	control	tests,	as	described	in	the	methods		 	 	 49	section,	I	determined	CIBERSORT	to	be	the	most	accurate	tool	for	the	POG	samples	(Figure	3–5).	CIBERSORT	was	able	to	differentiate	between	different	T	cell	subsets	in	a	dataset	of	purified	T	cells,	whereas	MCP	Counter	predicted	there	to	be	some	cells	of	B	cell	lineage	in	these	samples.	CIBERSORT	predicted	there	to	be	a	small	presence	of	activated	natural	killer	(NK)	cells	in	the	purified	CD8+	T	cell	samples,	which	is	likely	due	to	the	overlapping	cytotoxic	functions,	and	thus	similar	gene	expression	profiles	of	NK	cells	and	CD8+	T	cells	(Rooney	et	al.,	2015),	however,	CD8+	cells	were	still	predicted	to	be	the	dominate	cell	type.	I	then	implemented	CIBERSORT	with	RNA-Seq	data	for	both	patient	biopsies,	as	well	as	with	data	from	all	other	CRC	patients	enrolled	in	the	POG	program,	to	predict	the	immune	cell	infiltration	in	the	sequenced	samples.			 	 	 50		Figure	3–5:	Testing	immune	cell	deconvolution	software	demonstrates	that	CIBERSORT	is	most	accurate	for	POG	data.			CIBERSORT	and	MCP	counter	were	tested	on	control	samples	(purified	T	cell	samples	(n=5	per	subtype),	and	POG	cases	with	known	high	B	(n=2)	or	T	(n=2)	cell	infiltration).	Panels	A	and	B	are	B.cells.naive B.cells.memory Plasma.cells T.cells.CD8 T.cells.CD4.naiveB cellsT cellsB cellsT cellsB cellsT cellsB cellsT cellsB cellsT cells0.00.20.40.60.00.20.40.60.00.20.40.60.00.20.40.60.00.20.40.6ValueExpected Cell typeB cellsT cells NK cells T cellsFibroblasts Monocytic lineage Myeloid dendritic cells NeutrophilsB lineage CD8 T cells Cytotoxic lymphocytes Endothelial cellsB cellsT cellsB cellsT cellsB cellsT cellsB cellsT cells010002000300040005000010002000300040005000010002000300040005000ValueExpected Cell typeB cellsT cells     B.cells.naive B.cells.memory Plasma.cells T.cells.CD8 T.cells.CD4.naiveT.cell.CD4.MR T.cells.CD4.MA T.cells.FH T.cells.GDNK.cells.R NK.cells.A MonocytesDC.R DC.AEosinophils NeutrophilsT.cells.Regulatory0.00.250.50.00.250.50.00.250.50.00.250.50.00.250.5Macrophage M0 Macrophage M1Macrophage M2 Mast.cells.R Mast.cells.AT.cell.CD4.MR T.cells.CD4.MA T.cells.FH T.cells.GDT.cells.RegulatoryNK.cells.R NK.cells.A Monocytes Macrophage M0 Macrophage M1DC.R DC.AMacrophage M2 Mast.cells.R Mast.cells.AEosinophils NeutrophilsCD4 TFHCD4 THCD8CD4 TFHCD4 THCD8CD4 TFHCD4 THCD8CD4 TFHCD4 THCD8CD4 TFHCD4 THCD8Expected Cell typeCD4 TFHCD4 THCD8ValueA BCD4 TFHCD4 THCD8CD4 TFHCD4 THCD8CD4 TFHCD4 THCD8CD4 TFHCD4 THCD8B lineage CD8 T cells Cytotoxic lymphocytes Endothelial cellsFibroblasts Monocytic lineage Myeloid dendritic cells NeutrophilsNK cells T cells010020030040001002003004000100200300400ValueExpected Cell typeCD4 TFHCD4 THCD8C D	 	 	 51	outputs	from	CIBERSORT,	where	it	is	able	to	separate	the	POG	cases	(A),	and	purified	T	cells	(B)	into	the	correct	subtypes.	Panels	C	and	D	show	MCP	counter	outputs,	where	it	is	unable	to	determine	the	presence	of	T	cells	in	the	POG	cases	(C)	and	predicts	the	presence	of	B	cells	within	the	purified	T	cell	samples	(D).	Abbreviations	for	cell	types	are	as	follows:	MR	–	Memory	resting,	MA	–	Memory	activated,	FH	–	Follicular	helper,	GD	–	Gamma	delta,	NK	–	Natural	killer,	R	–	Resting,	A	–	Activated,	DC	–	Dendritic	cells,	TFH	–	T	cell	follicular	helper,	TH	–	T	cell	helper.			Data	from	the	immune	cell	infiltrate	predictions,	generated	by	CIBERSORT,	supports	the	notion	that	there	are	a	higher	proportion	of	immune	cells	in	the	patient’s	tumour	following	treatment	(Figure	3-6).	It	is	of	particular	interest	that	CD8+	T	cells	are	amongst	the	cells	predicted	to	increase	following	treatment,	and	are	predicted	to	be	one	of	the	cell	types	with	highest	infiltration,	as	these	generally	drive	anti-tumour	immune	responses,	due	to	their	cytotoxic	phenotype	and	ability	to	kill	cells	(Rooney	et	al.,	2015).	If	there	is	a	higher	infiltration	of	anti-tumour	immune	cells	following	irbesartan	treatment,	this	further	supports	the	idea	that	there	has	been	an	active	immune	response	between	the	two	biopsies.	Resting	CD4+	memory	T	cells	and	regulatory	T	cells	are	also	predicted	to	increase	following	treatment	with	irbesartan,	which	also	suggests	an	increase	in	immune	processes	between	the	two	biopsies,	and	also	that	there	is	an	inhibition	of	the	immune	system	at	the	second	biopsy	(regulatory	T	cells).		Also	contributing	to	the	notion	of	an	inhibition	of	immune	processes	in	the	second	biopsy	is	the	increase	in	M0	and	M2	macrophages,	compared	to	the	pre-treatment	biopsy.	M2	macrophages	are	of	particular	interest	as	these	cells	are	known	to	have	differing	properties	to	other	subsets	of	macrophages	(M0,	M1),	as	they	can	contribute	to	a	pro-tumour	microenvironment	(Bertani	et	al.,	2017).				 	 	 52	The	cell	infiltration	predictions	also	show	that	infiltration	scores	for	the	second	biopsy	are	higher	for	a	number	of	cell	types,	including	CD8+	T	cells	and	macrophages,	than	other	CRC	patients	enrolled	in	the	POG	(Figure	3-6).	This	is	of	note,	as	it	suggests	that	the	presence	of	immune	cells	in	the	patient	samples	after	irbesartan	treatment	is	higher	than	would	normally	be	expected	for	a	late	stage	metastatic	colorectal	cancer.			Figure	3–6:	Cell	infiltration	predictions	for	all	POG	CRC	patients	suggest	high	infiltration	for	the	post-treatment	biopsy.		Cell	types	are	listed	along	the	x-axis,	with	each	boxplot	representing	the	CRC	POG	cohort	infiltration	prediction	for	that	cell	type,	as	predicted	by	CIBERSORT	(Newman	et	al.,	2015).	Light	blue	dots	indicate	the	position	of	the	first	biopsy	sample	for	this	case,	and	dark	blue	indicate	the	post-treatment	sample.				 	 	 53	3.2.3. Mass	spectrometry		To	investigate	changes	in	the	proteome	of	the	tumour	and	identify	if	changes	in	the	proteome	displayed	the	same	trend	as	observed	from	the	transcriptome,	mass	spectrometry	was	utilised.	The	overall	complexity	of	proteins	detected	was	low	(less	than	5000	proteins),	possibly	due	to	the	RNA	and	DNA	protocols	of	the	POG	sample	processing	removing	many	proteins.	This	meant	that	many	proteins	of	particular	interest	were	not	present	in	the	data,	including	AGTR1,	FOS	and	most	of	the	immune	marker	genes	detected	to	increase	in	post-treatment	biopsy	from	the	RNA-Seq	data	(Figure	3–4).				One	peptide	that	corresponded	to	JUNB	(JUN	family	member)	was	detected	in	the	mass	spectrometry	data,	with	a	log2	fold	change	of	0.21	in	the	post-treatment	biopsy	compared	to	the	pre-treatment	and	so	did	not	pass	the	threshold	to	be	considered	differentially	expressed.	Furthermore,	this	JUN	family	member	is	known	to	have	some	differing	interactions	and	cell	functions	to	that	of	the	canonical	c-JUN	(Mechta-Grigoriou	et	al.,	2001).	Of	the	immune	marker	genes	shown	in	Figure	3-4,	some	were	detected	but	were	not	differentially	expressed	between	the	two	biopsies;	HLA-B	B40a,	HLA-B	B7a,	CD74,	CD3E,	HLA-A	A2a,	HLA-A	A3a,	HLA-C	Cw-2a	and	CD274	(PD-L1).	These	proteins	were	not	detected	to	be	differentially	expressed,	based	on	the	filter	described	in	the	methods,	however,	all	but	HLA-A	A3a	do	increase	in	expression	in	the	post-treatment	biopsy	compared	to	the	pre-treatment.					 	 	 54		Figure	3–7:	Mass	spectrometry	data	reflect	changes	in	immune	proteins.		Volcano	plot	depicting	the	fold	change	of	proteins	in	the	post-treatment	sample	compared	to	pre-treatment.	Proteins	highlighted	by	red	or	blue	are	those	that	have	a	log	fold	change	of	more	than	2	and	an	adjusted	p	value	less	than	0.05.		Despite	a	low	overall	yield	of	proteins,	there	are	still	many	that	are	differentially	expressed	between	the	biopsies,	and	some	of	the	proteins	that	increase	the	most	post-treatment	have	functions	that	are	associated	with	immune	involvement	(Figure	3-7).	In	particular,	up	                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         LITAFNDST1NUTM2FPLCD1ABCA12ASXL3DNAJC5TSNAXIP1ANKRD16ZNF804APARNPRG2AURKCFOLR2PIK3IP1AXLBRD2LRRC71DENND2DKIAA1614AGAP3POTECPDE9ASLCO1B7 DAB2IPCREBBPRHAGSPTBGOLGA1SERPINF2SLC14A1FGGKLHL24SLC5A10ZNF215 RNASE13TOR3A ZNF648GRIN2CSNCACTSHMAP2K6IGFN1AQR FAT4HBE1ABCB9 MCMBPTMEM45A ACACBTRIM7SLPIGPN1CMTM5 RWDD2BKLHL13FAM27D1PLCE1NAPSASH3YL101234−5.0 −2.5 0.0 2.5Log fold change−log10 P. valueUp post treatmentUp pre treatment	 	 	 55	regulated	proteins	LITAF	(Lipopolysaccharide-induced	tumour	necrosis	factor,	alpha)	and	tumour	suppressor	DAB2IP	(Disabled	homolog-2	interacting	protein)	are	involved	in	stimulation	of	innate	immune	and	inflammatory	pathways	(Tang	et	al.,	2006).	Bone	marrow	proteoglycan	(PRG2),	an	eosinophil	major	basic	protein	(MBP),	is	also	up	regulated	following	treatment	with	irbesartan.	This	is	of	note	as	eosinophils	are	thought	to	be	key	players	in	hypersensitivity	reactions	(Acharya	and	Ackerman,	2014),	and	there	has	been	a	report	of	this	type	of	reaction	occurring	in	response	to	irbesartan	treatment	(Cardoso	et	al.,	2016).		Of	the	significantly	down-regulated	proteins,	some	are	also	known	to	be	involved	in	innate	immune	and	inflammatory	processes,	including	SLPI	(Secretory	leukocyte	peptidase	inhibitor)	and	fibrinogen	α	and	γ	chains.	Whilst	the	exact	role	of	these	proteins	is	somewhat	unclear	in	the	context	of	the	patient	response	and	resistance	to	irbesartan,	it	is	compelling	that	the	notion	of	immune	related	changes	carries	through	to	the	proteome	of	the	tumour.			Gene	set	enrichment	analysis	was	also	conducted	for	de-regulated	proteins,	as	described	in	the	methods	section.	However,	the	low	yield	of	proteins	detected	in	the	mass	spectrometry	data	meant	that	there	were	fewer	differentially	expressed	proteins	to	try	and	infer	a	common	functional	process	for.	Despite	this,	pathways	relating	to	cytokine	production	emerge	as	being	enriched	in	the	up-regulated	proteins	(Appendix	J),	which	include	LITAF	and	PRG2,	suggesting	that	there	may	be	a	correlation	between	the	gene	expression	and	protein	alterations	following	treatment	with	irbesartan,	but	the	yield	of	proteins	is	too	low	to	determine	the	extent	of	the	correlation.						 	 	 56	3.3 Changes	in	cell	infiltration	correlate	with	an	active	immune	response		The	presence	of	infiltrating	T	cells	in	a	tumour	microenvironment	is	known	to	correlate	with	overall	patient	survival	(Goode	and	Ovarian	Tumor	Tissue	Analysis	(OTTA)	Consortium,	2017;	Mlecnik	et	al.,	2016),	and	can	be	prognostic	for	patient	response	to	certain	therapies,	including	immune	checkpoint	blockade	(Tumeh	et	al.,	2014).	As	the	observed	changes	in	gene	expression	and	cell	infiltration	predictions	are	highly	suggestive	of	an	altered	infiltration	of	immune	cells	at	the	tumour	site	between	biopsies,	multiplex	immunohistochemistry	(IHC)	panels	were	performed	on	the	biopsy	tissues	to	confirm	these	changes	and	characterise	the	tumour	microenvironment	pre-	and	post-irbesartan	therapy.		3.3.1. Immunohistochemistry	identifies	increases	in	T	cell	infiltration	following	treatment		IHC	panels	were	configured	to	test	for	presence	of	cells	of	interest,	including	cytotoxic	CD8+	T	cells	as	predicted	by	CIBERSORT	and	gene	expression	data.	Firstly,	a	panel	was	run	using	a	combination	of	CD3	and	FOS,	in	order	to	distinguish	whether	the	high	FOS	gene	expression	reported	in	the	first	biopsy	could	be	confirmed	at	the	protein	level,	and	whether	expression	was	detected	in	the	tumour,	and	in	tumour	infiltrating	T	cells.	This	was	of	particular	interest	as	irbesartan	has	been	shown	to	have	an	inhibitory	effect	on	T	cells	in	vitro	through	down	regulation	of	the	AP-1	complex	(Cheng	et	al.,	2004).		Staining	of	the	post-treatment	biopsy	confirms	a	reduction	in	FOS,	as	detected	in	the	RNA-Seq	data	(Figure	3–4	and	Appendix	F),	although	the	change	was	not	significant	(Figure	3-8	and		 	 	 57	Figure	3-9).	A	more	compelling	change	was	detected	in	CD3+	cells,	which	increased	dramatically	following	treatment	(Figure	3-8	and	Figure	3-9)	indicating	a	large	increase	of	T	cells	in	the	tumour	microenvironment.	In	all	of	the	tissue	slides	stained,	there	were	not	enough	CD3+FOS+	cells	detected	on	which	to	train	the	counting	algorithm	(Figure	3-8),	suggesting	that	the	FOS	signal	detected	in	the	RNA-Seq	is	not	present	in	infiltrating	T	cells.	These	results	are	consistent	with	the	changes	in	immune	gene	expression,	demonstrating	that	the	response	observed	may	not	solely	be	a	result	of	down-regulation	of	FOS	and	JUN	through	inhibition	of	the	angiotensin	II	pathway.	We	had	also	attempted	to	stain	for	irbesartan’s	target	receptor,	AGTR1,	to	determine	if	there	was	functional	protein	present	in	the	first	biopsy,	as	this	had	not	been	detected	in	the	mass	spectrometry	data	and	gene	expression	levels	were	low.	However,	there	were	technical	difficulties	with	the	antibody	staining	where	signal	was	too	strong,	possibly	a	result	of	non-specific	binding.		Panels	were	then	run	to	detect	if	cytotoxic	T	cells	were	present	as	these	were	predicted	to	increase	in	the	second	biopsy	(Figure	3-6),	are	heavily	involved	in	driving	responses	in	immunotherapies	and	correlate	with	better	overall	survival	in	patients	(Mlecnik	et	al.,	2016;	Rooney	et	al.,	2015;	Zhang	and	Bevan,	2011).	Cytotoxic	T	cells,	identified	using	a	panel	of	GrB+CD8+CD3+,	were	shown	to	increase	substantially	following	treatment	(Figure	3-8	and	Figure	3-9),	consistent	with	the	notion	that	these	anti-tumour	T	cells	may	have	been	involved	in	the	response	of	the	patient.	It	is	noteworthy	that	the	increase	of	these	cells	is	detected	within	the	tumour	tissue	(epithelial	compartment),	as	well	as	the	surrounding	stroma,	supporting	a	tumour-immune	interaction.	Also,	as	predicted	by	CIBERSORT,	there	are	many	more	CD8+GrB+	T	cells	present	in	the	patient	sample	post	treatment	than	other	control	CRC	tumour	samples	(Figure	3-9),	whereas	the	first	biopsy	is	more	comparable	to	the	other		 	 	 58	samples,	which	further	supports	to	the	notion	of	immune	interaction	with	the	tumour	following	treatment.		Resistance	mechanisms	to	an	active	immune	response	are	common	in	tumours	(Sharma	et	al.,	2017),	the	most	well-studied	being	the	up	regulation	of	immune	checkpoints	(Pardoll,	2012).	Up	regulation	of	the	immune	checkpoint	PDL-1	is	one	particular	mechanism	of	immune	tolerance	that	has	been	implicated	in	a	number	of	solid	cancers	(Curran	et	al.,	2017;	Kim	et	al.,	2016).	An	increase	in	the	percentage	of	cells	expressing	PD-L1	is	detected	in	the	second	biopsy,	at	the	tumour	epithelial	compartment,	implying	that	the	immune	system	is	selecting	against	tumour	cells	that	are	not	expressing	PD-L1.	There	is	also	a	significant	increase	of	PD1	expressing	cytotoxic	T	cells	following	treatment,	further	supporting	that	the	PD1-PDL1	interaction	may	be	utilised	by	the	tumour	as	a	resistance	mechanism	in	the	post	treatment	sample	(Figure	3-9).	The	large	increase	of	PDL-1	expressing	cells	in	the	stroma	likely	includes	other	antigen-presenting	cells	(APCs),	such	as	macrophages	or	dendritic	cells	(Kim	et	al.,	2016),	which	are	also	involved	in	an	active	immune	response.			As	regulatory	T	cells	can	also	contribute	to	an	immune	inhibitory	environment,	and	there	is	predicted	to	be	an	increase	in	these	cells	in	the	second	biopsy	compared	to	the	first	(Figure	3-6),	we	had	planned	to	stain	for	these	cells	in	a	panel	consisting	of	CD3+,	CD8-	(therefore	CD4+),	CD25+	and	FoxP3+,	however	this	panel	has	still	not	been	properly	worked	up	and	there	are	many	issues	currently	with	dilutions	of	the	individual	antibodies.			In	addition	to	detecting	infiltrating	T	cells,	panels	were	run	to	identify	B	cells	(CD20+CD79A+)	and	myeloid	derived	suppressive	cells	(MDSCs,	HLA-DR-	CD33+CD11b+),	as	these	can	also	have	roles	in	tumour	microenvironments	(Sharma	et	al.,	2017).	Both	of	these	cell	types		 	 	 59	display	a	small	increase	in	the	stromal	compartment	of	the	second	biopsy,	although	these	are	not	significant	and	are	minimal	compared	to	the	differences	observed	with	T	cells.	These	results	are	consistent	with	results	from	the	gene	expression	analysis,	as	there	were	not	any	clear	changes	in	expression	that	implicated	involvement	of	these	cell	types.	It	is	therefore	likely	that	T	cells	are	the	cells	that	are	primarily	involved	in	the	response	observed	in	this	case	study.					 	 	 60		Biopsy 1 (2014) Biopsy 2 (2016)PDL1 and PD1 expressing CD8 cytotoxic cellsCD3+FOS+CD3+GZMB+CD8+PDL1+PD1+CD8+CD11b+HLA-DR+CD33+CD79A+CD20+Cytotoxic T cellsFOS and T cellsMyeloid derived suppressive cells (MDSCs)B cells	 	 	 61	Figure	3–8:	Immunohistochemistry	staining	reveals	increased	immune	infiltration	following	treatment.		This	figure	demonstrates	the	immunohistochemistry	panels	used	on	both	patient	biopsies	to	detect	infiltration	of	various	immune	cell	types.	The	pre-irbesartan	biopsy	slides	are	in	the	left	column,	and	post-irbesartan	on	the	right.	Individual	markers	used	in	each	panel	are	shown	on	the	right-hand	side	of	the	row,	along	with	the	cell	types	that	were	being	targeted	by	the	panel.	All	images	are	composite	images,	with	the	haematoxylin	stain	digitally	removed	to	highlight	antibody	specific	colours	more	clearly.	IHC	for	the	earlier	diagnostic	sample	slides	(2010)	are	in	Appendix	B.																														 	 	 62		Figure	3–9:	Cell	infiltration	counts	show	significant	increases	of	T	cell	subtypes	following	treatment	with	irbesartan.		Cell	infiltration	counts	for	pre-	and	post-treatment	samples,	also	including	the	earlier	2010	diagnostic	sample	(Figure	1–4).	Control	samples	were	CRC	tissues	from	the	BC	GI	Biobank,	all	with	MSI	high	status,	and	are	coloured	in	a	shade	of	red/orange.	Each	sample	from	this	patient	is	coloured	in	a	shade	of	blue.	(A)	Normalised	infiltration	counts	for	listed	cell	types,	and	the	percentage	of	cells	expressing	PDL1.	Differences	between	pre-	and	post-treatment	are	significant	for	CD3+,	cytotoxic	and	PD1+CD8+	T	cells,	as	well	as	the	percentage	of	PDL1	expressing	cells,	in	both	epithelial	and	stromal	compartments.	B Cells MDSCs CD3+ Cells Cytotoxic T Cells PD1+CD8+ CellsEpithelialStromal025050075010001250025050075010001250SampleCells / mm2020406080FOS H ScoreStromalEpithelialPDL1+% Cells expressing PDL10204002040SampleSampleAB******** **Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2Control 1Control 2Control 3DiagnosisBiopsy 1Biopsy 2	 	 	 63	(B)	H	scores	for	FOS	protein	expression	across	all	samples.	The	difference	between	pre-	and	post-treatment	is	not	significantly	different	in	this	case.	Asterisks	(*)	indicate	a	significant	difference	(p	value	<0.05)	between	biopsy	1	and	2.			3.3.2. Post	treatment	infiltrating	T	cells	have	a	more	diverse	T	cell	receptor	repertoire			In	order	to	further	explore	the	changes	in	tumour	microenvironment	between	the	two	biopsies,	changes	in	T	cell	antigen	receptors	(TCRs)	were	characterised	using	RNA-Seq	data.	TCRs	are	membrane	bound	heterodimeric	proteins	capable	of	recognising	foreign	peptides	presented	on	the	MHC	complex	of	an	antigen-presenting	cell	(APC)	(Davis	and	Bjorkman,	1988).	TCR	sequences	are	highly	variable,	generated	by	rearrangement	of	the	V(D)J	regions	at	the	CDR3	region,	with	a	theoretical	diversity	in	the	region	of	1016	unique	clonotypes	for	αβ	T	cells	(Li	et	al.,	2016).	Through	having	a	diverse	repertoire	of	distinct	TCRs,	the	immune	system	is	able	to	recognise	a	large	range	of	foreign	antigens	and	neoantigens,	to	elicit	an	immune	response.		Monitoring	changes	in	TCR	repertoires	following	treatment	allows	identification	of	expanding	or	contracting	TCR	clonotypes,	which	could	then	be	associated	with	immune	responses	to	specific	antigens.	To	investigate	changes	in	the	TCR	repertoire	following	irbesartan	treatment,	I	assembled	CDR3	clonotypes	from	RNA-seq	data,	using	MiXCR	(Bolotin	et	al.,	2015).	As	one	would	expect,	if	there	was	higher	infiltration	of	T	cells	at	the	tumour	site	following	irbesartan	treatment,	a	larger	proportion	of	sequencing	reads	aligned	to	TCR	specific	regions	of	the	genome	in	the	post	treatment	biopsy	(Figure	3-10).	Antigens	presented	on	the	major	histocompatibility	complexes	(MHCs)	are	primarily	recognised	by	TCRs	expressed	on	αβ	T		 	 	 64	cells,	the	most	abundant	T	cell	subset	in	humans,	which	is	also	reflected	in	the	read	alignments	from	MiXCR	(Figure	3-10).	Due	to	the	more	extensive	role,	and	higher	aligned	read	counts,	changes	were	focused	on	the	repertoire	of	αβ	T	cells.			Figure	3–10:	RNA-Seq	read	alignment	to	T	cell	receptors	in	both	biopsy	samples.		Total	RNA	sequencing	reads	and	the	number	and	proportion	of	these	that	aligned	to	TCR	regions	are	in	the	green	and	blue	circles	respectively.	The	number	and	proportion	of	aligned	reads	for	each	biopsy	for	α,	β,	γ	and	δ	chains	are	depicted	in	the	purple	circles	below.	The	pre-treatment	biopsy	(biopsy	1)	is	on	the	left	side,	and	post-treatment	(biopsy	2)	on	the	right.			82, 071, 263 Total sequencing reads 97, 713, 752Biopsy 1(Pre-treatment)Biopsy 2(Post-treatment)2974(0.036%) Aligned reads5801(0.06%)11(0.4%)0(0.0%)82(1.4%)3(0.0%)2760(92.8%)4481(77.2%)TRA203(6.8%)1235(21.3%)TRBTRDTRG	 	 	 65	A	recent	study	identified	particular	T	cell	receptor	alpha	and	beta	variable	genes	(TRAV	and	TRBV)	as	being	the	most	commonly	used	in	TCR	clonotypes	across	a	large	cohort	of	29	cancers	(Li	et	al.,	2016)	and	expectedly,	many	of	these	(including	TRAV13-1,	TRAV12-2,	TRBV20-1)	are	also	found	at	high	frequencies	in	both	biopsies	from	the	patient	(Figure	3-11).	Comparison	of	the	usage	of	V	and	J	genes	in	clonotypes	detected	in	the	two	biopsies	reveals	that	clonotypes	present	in	the	second	biopsy	are	created	from	a	much	wider	variety	of	genes	(Figure	3-11).	Utilisation	of	a	larger	number	of	genes	creates	CDR3	regions	with	greater	variability	in	sequence,	providing	more	opportunity	for	the	receptors	to	recognise	an	antigen.	TCRs	from	the	first	biopsy	can	be	described	as	more	clonal;	they	utilise	a	smaller	variety	of	V/J	genes,	but	at	a	higher	frequency.	To	understand	if	the	larger	diversity	in	the	post-treatment	biopsy	is	independent	of	increased	presence	of	T	cells	at	the	tumour	site,	I	calculated	diversity	scores	for	the	detected	clonotypes	at	each	biopsy,	which	take	the	number	of	total	TCRs	into	consideration	(Table	3-1).	The	CPK	(clonotypes	per	thousand	reads)	and	Shannon	entropy	scores	(a	measure	for	variance	in	the	detected	sequences)	were	higher	for	TCRs	in	the	second	biopsy	compared	to	the	first,	indicating	that	the	higher	diversity	generated	through	different	V/J	gene	usage	is	independent	of	the	higher	infiltration	detected	in	the	second	biopsy.		The	majority	of	clonotypes	decrease	in	proportion	in	the	second	biopsy	compared	to	the	first,	which	can	likely	be	attributed	to	the	increase	in	diversity	found	in	the	second	biopsy;	the	inclusion	of	many	more	unique	clonotypes	mean	that	those	already	present	would	now	be	found	at	a	lower	frequency.	However,	there	is	one	clonotype	that	increases	in	proportion	post-treatment	(Figure	3-11).	This	clonotype	sequence,	CASSSRTGELFF,	has	previously	been	reported	in	the	literature	as	being	linked	to	CD8+	T	cell	recognition	of	the	HIV-1	protein	gagKK10	epitope	presented	by	the	human	leukocyte	antigen	HLA-B*27:05	(Ladell	et	al.,		 	 	 66	2013).	As	the	only	clonotype	to	expand,	or	increase	in	proportion	following	treatment,	it	may	have	been	involved	directly	with	the	response	observed	in	the	patient,	through	recognition	of	a	tumour	specific	neoantigen.	Interestingly,	there	is	no	evidence	of	HIV-1	in	either	of	the	patient	samples,	nor	does	the	patient	have	the	same	HLA	allele	presenting	the	epitope	described	in	the	literature	(Ladell	et	al.,	2013).																								TRAJ10TRAJ12TRAJ15TRAJ29TRAJ32TRAJ39TRAJ41TRAJ47TRAJ49TRAJ56TRAJ28TRAJ3TRAJ31TRAJ43TRAJ50TRAJ54TRAJ57TRAJ6TRAJ16TRAJ38TRAJ45TRAJ9TRAJ52TRAJ8TRAV17    TRAV23DV6 TRAV29DV5 TRAV3     TRAV34    TRAV6     TRAV8−1   TRAV8−2   TRAV9−2   TRAV1−1   TRAV14DV4 TRAV19    TRAV21    TRAV26−1  TRAV30    TRAV13−1  TRAV8−3   TRAV16    TRAV12−2  TRAV24    TRAV12−1  TRAV5    TRAJ5TRAJ16TRAJ18TRAJ26TRAJ35TRAJ41TRAJ43TRAJ24TRAJ28TRAJ31TRAJ32TRAJ39TRAJ40TRAJ44TRAJ47TRAJ48TRAJ33TRAJ34TRAJ36TRAJ37TRAJ38TRAJ53TRAJ6TRAJ22TRAJ17TRAJ29TRAJ4TRAJ58TRAJ12TRAJ13TRAJ27TRAJ3TRAJ15TRAJ20TRAJ42TRAJ49TRAJ45TRAJ5TRAJ23TRAJ30TRAJ54TRAJ57TRAJ11TRAJ9TRAJ52TRAJ8TRAV10      TRAV25      TRAV27      TRAV30      TRAV34      TRAV38−1    AV38−2DV8 TRAV40      TRAV41      TRAV8−1     TRAV8−7     TRAV20      TRAV22      TRAV23DV6   TRAV29DV5   TRAV4       TRAV16      TRAV39      TRAV6       TRAV8−2     TRAV1−2     TRAV14DV4   TRAV19      TRAV3       TRAV8−4     TRAV12−3    TRAV13−2    TRAV21      TRAV35      TRAV1−1     TRAV26−2    TRAV8−6     TRAV2       TRAV9−2     TRAV17      TRAV36DV7   TRAV8−3     TRAV5       TRAV24      TRAV26−1    TRAV13−1    TRAV12−2    TRAV12−1    ATRBJ2−5TRBJ1−2TRBJ1−6TRBJ1−1TRBJ2−7TRBJ2−2TRBJ1−5TRBJ2−1TRBJ2−3TRBV10−3TRBV12−3TRBV21−1TRBV29−1TRBV4−2TRBV6−1TRBV6−2 TRBV7−2 TRBV7−8 TRBV7−9 TRBV2 TRBV27 TRBV7−6TRBV18 TRBV11−2TRBV13TRBV5−1TRBV19TRBV20−1TRBJ1−3TRBJ2−4TRBJ2−6TRBJ1−4TRBJ1−2TRBJ2−5TRBJ1−6TRBJ1−5TRBJ1−1TRBJ2−2TRBJ2−1TRBJ2−7TRBJ2−3TRBV14TRBV23−1TRBV30TRBV4−1TRBV6−7TRBV7−1TRBV12−4TRBV25−1TRBV6−1TRBV6−2TRBV7−3TRBV7−9 TRBV9TRBV24−1 TRBV7−7 TRBV10−2 TRBV11−1 TRBV15 TRBV6−6 TRBV11−2 TRBV5−4 TRBV7−8TRBV7−2TRBV10−3TRBV4−2TRBV29−1TRBV7−6TRBV27TRBV18TRBV28TRBV19TRBV6−5TRBV3−1TRBV12−3TRBV5−1TRBV5−6TRBV2TRBV13TRBV20−1C0.00.20.4Biopsy1 Biopsy2FrequencyTRA Clonotype SequenceCAARPSGNTPLVFCAASPPYSSASKIIFCACPTGGFKTIFCAETLEYGNKLVFCAFSGGTSYGKLTFCALSGGTSYGKLTFCAPSGGSYIPTFCAVIPGGGFKTIFCAVNPYGNQFYFCAVPNQAGTALIFCAVRPRDGGATNKLIFCGANYNAGNNRKLIWCGKNTGFQKLVFCVMNTGFQKLVFCVVNTGFQKLVF0.00.10.20.30.40.5Biopsy1 Biopsy2FrequencyTRB Clonotype SequenceCASSLALGTRDNEQFFCASSPDRRNQPQHFCASSPLYNEQFFCASSSDRGNEQFFCASSSRTGELFFCSAPDLPKSTDTQYFCSATHLAVSTDTQYFEBDFTRB - J genesTRB - J genesTRB - V genesTRB - V genesTRA - V genesTRA - V genesTRA - J genesTRA - J genes	 	 	 68	Figure	3–11:	TCR	VJ	gene	usage	increases	in	the	post	treatment	biopsy.	V	and	J	gene	usage	in	detected	TCR	clonotypes	is	shown	in	this	figure.	Panels	A	and	C	show	the	gene	usage	for	biopsy	1	(pre-treatment),	and	panels	B	and	D	show	the	gene	usage	for	biopsy	2	(post-treatment).	TCR	α	chains,	generated	from	TRA	genes	are	in	panels	A	and	B,	and	β	chains,	generated	from	TRB	genes	are	in	panels	C	and	D.	Individual	V	and	J	genes	are	represented	along	the	circle	edge,	with	V	genes	in	the	top	half,	and	J	in	the	bottom.	Lines	between	genes	indicate	a	TCR	chain	clonotype	generated	by	recombination	of	the	two.	The	thickness	of	each	line	is	proportional	to	the	number	of	reads	supporting	the	clonotype.	Panels	E	and	F	shows	the	change	in	frequency	of	each	clonotype	that	is	detected	in	both	biopsies.	The	α	(TRA)	chains	are	shown	in	panel	E,	and	β	chains	in	panel	F.	The	β	(TRB)	clonotype	indicated	in	red	in	panel	F	is	the	only	clonotype	to	increase	in	frequency	following	treatment	with	irbesartan.					 Biopsy	1	(pre-treatment)	 Biopsy	2	(post-treatment)	Clonotypes	per	thousand	reads	(CPK)	25.89	 62.06	Shannon	Entropy	 3.442862	 4.409166			Table	3-1:	Diversity	metrics	for	infiltrating	T	cells		CPK	–	the	total	number	of	all	chain	TCR	clonotypes	detected	in	each	biopsy,	normalised	against	the	total	number	of	reads	aligning	to	the	TCR	region	of	the	genome.	Shannon	entropy	–	a	divergence	measure	of	clonotype	sequences,	where	a	high	variance	in	the	sequence	has	a	higher	Shannon	entropy	score.		3.3.3. High	neoantigen	load	is	predicted	within	the	tumour		In	order	for	T	cells	in	the	tumour	microenvironment	to	elicit	a	response	against	the	malignant	cells,	they	must	first	be	activated,	through	recognition	of	a	neoantigen.	In	a	tumour	with	such	a	high	mutational	load,	it	is	probable	that	mutations	are	present	that	are	capable	of	generating	immunogenic	peptides,	or	neoantigens.	For	these	peptides	to	be	recognised	by	cytotoxic	T	cells,	they	must	be	processed	and	presented	on	the	tumour	cell	surface	by	the	MHC	(Major	histocompatibility	complex)	class	I.	As	previously	described,	genes	associated	with	this		 	 	 69	complex	are	more	abundant	following	treatment	with	irbesartan	(Figure	3-4),	and	there	is	a	higher	infiltration	of	cytotoxic	T	cells	in	the	post-treatment	biopsy	suggesting	that	there	may	have	been	recognition	of	a	presented	neoantigen	during	treatment.		In	an	attempt	to	identify	potential	immunogenic	peptides,	binding	predictions	were	generated	for	the	patient	specific	HLA	alleles,	using	NetMHCpan	(Nielsen	and	Andreatta,	2016)	for	all	mutations	detected	in	the	first	biopsy	(see	methods).	This	process	generated	over	300,000	peptide-binding	predictions	for	the	patient	specific	6	HLA	alleles.	Peptide	predictions	with	binding	affinities	less	than	50nM	were	filtered	as	candidate	immunogenic	peptides,	as	this	value	has	been	used	previously	in	the	literature	as	a	filter	for	strong	binding	(Rajasagi	et	al.,	2014;	Wick	et	al.,	2014),	which	provided	a	list	of	502	high	confidence	candidate	peptides	(Appendix	H).	The	majority	of	predicted	binding	affinities	were	with	HLA	types	HLA-A*02:01	and	HLA-B*07:02	(Figure	3-12),	which	are	known	to	be	very	common	alleles	in	the	Caucasian	population	(Gonzalez-Galarza	et	al.,	2015).	This	may	be	due	to	the	fact	that	there	is	more	data	available	for	these	HLA	types,	in	terms	of	sequence,	structure	and	binding	preferences,	rather	than	a	higher	number	of	neoantigens	interacting	with	these	alleles.	As	described	earlier	in	section	3.1,	the	majority	of	mutations	are	shared	at	a	similar	frequency	between	biopsies,	suggesting	that	there	is	a	broader	selective	pressure	present	during	irbesartan	than	the	targetting	of	a	single	pathway,	which	is	also	reflected	in	the	variant	allele	frequency	of	high	confidence	neoantigens	(Figure	3-12).			Due	to	small	amounts	of	peripheral	blood	mononuclear	cell	(PBMC)	samples	from	the	patient,	and	the	cost	of	custom	peptide	synthesis	(~$2	per	amino	acid,	https://www.peptide2.com/),	it	was	not	possible	to	test	all	candidate	peptides	for	T	cell	recognition.	Even	so,	I	tested	the	top	100	peptides	(Appendix	G)	using	an	ELISPOT	(Enzyme-linked	immunosorbent)	assay	to		 	 	 70	identify	any	immunogenic	activity	with	the	patient	specific	PBMCs,	as	these	had	substantially	strong	computationally	predicted	binding	affinities.	Previous	studies	have	shown	that	peptides	with	similar	predicted	binding	affinities	to	the	selected	candidates	have	been	detected	as	immunogenic	using	the	ELISPOT	assay	(Figure	3–12)	(Rajasagi	et	al.,	2014;	Wick	et	al.,	2014)	in	ovarian	cancer	and	chronic	lymphocytic	leukaemia	(CLL)	studies.			In	order	to	test	the	largest	number	of	peptides	with	the	limited	number	of	cells	I	had	available,	I	grouped	peptides	into	pools,	where	each	pool	contained	10	unique	peptides	and	each	peptide	was	only	represented	in	2	pools	(Table	2-1).	Therefore,	if	cells	became	activated	when	treated	with	pools	and	the	assay	developed	IFNg	spots	on	the	plate,	it	could	be	traced	back	to	one	particular	peptide.		ELISPOT	results,	shown	inFigure	3–13,	do	not	show	any	peptide	pools	that	stimulate	IFNγ	production	at	a	level	higher	than	the	control	stimulant	CEF	pool	(23	viral	peptides	shown	to	activate	CD8+	T	cells),	and	not	higher	than	baseline	(Figure	3-13A).	When	testing	individual	peptides	with	or	without	irbesartan,	the	same	result	is	achieved;	there	is	no	strong	IFNg	response	with	any	of	the	peptides.	This	may	indicate	that	the	immune	activation	detected	between	the	two	biopsies	is	not	directed	towards	a	single	neoantigen,	however,	the	number	of	peptides	tested	is	very	small	and	there	are	currently	not	enough	cells	to	conduct	multiple	replicates.	Blood	samples	are	still	being	collected	periodically	from	the	patient,	so	there	is	scope	for	more	neoantigens	to	be	tested	in	future.						 	 	 71		Figure	3–12:	High	confidence	neoantigens	are	predicted	for	common	HLA	types	and	are	detected	at	both	biopsies.		(A)	Rank	of	mutant	predicted	strong	binding	peptides	(1-502)	plotted	against	their	reciprocal	predicted	binding	affinity.	Green	dots	highlight	the	binding	affinity	of	confirmed	immunogenic	peptides	in	other	studies.	The	horizontal	line	indicates	the	cut	off	of	peptides	considered	for	ELISPOT	testing.	(B)	Distribution	of	HLA	alleles	that	are	predicted	to	bind	the	100	high	confidence	peptide	candidates.	(C)	Scatter	plot	depicting	the	change	in	variant	allele	frequencies	of	the	100	high	confidence	candidate	peptide	mutations.			 	 	 72		Figure	3–13:	ELISPOT	results	are	not	comprehensive	enough	to	identify	an	immunogenic	peptide	(A)	Testing	of	peptide	pools,	as	described	in	methods.	Peptide	pools	carried	forward	for	testing	individually	are	indicted	by	blue	boxes	over	the	ID.	Each	dot	for	each	peptide	is	a	single	replicate.	Each	peptide	pool	contains	10	peptides.	(B)	ELISPOT	images	from	a	control	CD3	well,	and	from	peptide	pool	R2,	which	was	selected	for	individual	testing.	(C)	Testing	of	individual	peptides	with	or	without	irbesartan	treatment.	Horizontal	blue	lines	indicate	the	number	of	spots	required	to	be	a	positive	result,	based	on	what	has	been	used	in	the	literature.	In	both	panels,	ELISPOT	controls	are	coloured	in	red	/	orange,	and	patient	specific	peptides	are	in	blue.		 	 	 73	4 Discussion	4.1 The	response	observed	in	the	patient	is	consistent	with	an	immune	response		Collectively,	changes	in	the	genomic	and	transcriptomic	features	of	the	tumour	between	the	two	biopsies	are	largely	consistent	with	the	notion	of	immune	involvement	during	treatment	with	irbesartan.	In	addition,	this	is	supported	by	the	clinical	response	seen	in	the	patient;	after	only	5	weeks,	tumour	burden	was	greatly	reduced	across	the	whole	body,	and	remained	so	for	approximately	18	months	(Jones	et	al.,	2016),	comparable	to	case	studies	of		patient	responses	to	immune	checkpoint	blockade	(Farkona	et	al.,	2016;	Le	et	al.,	2015).		Specifically,	differences	in	mutation	frequencies	(VAF)	between	the	two	biopsies	are	unremarkable	(Figure	3–2),	suggesting	that	the	selective	pressure	accompanying	treatment	with	irbesartan	was	not	directly	targetting	a	dominant	clone	within	the	tumour.	This	implies	that	the	drug	has	resulted	in	a	broader	cytotoxic	effect	on	the	tumour	cells,	perhaps	more	comparable	with	a	chemo-	or	radiotherapy	agent,	which	also	supports	the	disappearance	of	multiple	metastatic	sites	across	the	body	(Figure	1–4).	These	types	of	systemic	therapies,	however,	frequently	cause	severe	side	effects	and	toxicity	in	patients	(Denlinger	and	Barsevick,	2009)	as	there	is	little	discrimination	between	healthy	and	malignant	cells.	Interestingly,	during	the	treatment	period	with	irbesartan,	the	patient	did	not	exhibit	any	toxicity	effects,	or	poor	tolerance,	with	the	exception	of	low	blood	pressure,	which	was	to	be	expected	due	to	the	canonical	function	of	irbesartan	as	an	antihypertensive	(Barreras	and	Gurk-Turner,	2003).				 	 	 74	In	addition,	differentially	expressed	genes	between	the	two	biopsies	were	highly	enriched	for	many	processes	directly	related	to	the	immune	system	(many	of	these	GO	terms	have	a	q	value	<	0.001),	including	the	regulation	of	lymphocyte	activation	and	immune	effector	processes	(Figure	3–4	and	Appendix	A).	Supported	by	some	alterations	in	mass	spectrometry	data	(Figure	3–7),	and	the	immunohistochemistry	data	(Figure	3-8	and	Figure	3-9),	it	is	clear	that	there	are	differences	in	the	immune	microenvironment	between	the	two	biopsies,	independent	of	the	small	variation	in	predicted	tumour	content	(Appendix	C).		It	is	also	compelling	that	the	pre-treatment	POG	biopsy	is	highly	representative	of	a	tumour	that	is	likely	to	respond	to	immunotherapy,	based	upon	current	literature.	Multiple	disruptions	of	DNA	repair	genes	(Chapter	1.5.2)	generated	a	tumour	with	high	microsatellite	instability,	and	a	very	high	mutational	load	of	1578	non-synonymous	protein	coding	SNVs	(in	the	highest	1%	of	POG	cases).	Also,	from	these	mutations,	over	500	high	confidence	neoantigens	were	predicted,	based	on	HLA	binding	affinity.	All	of	these	characteristics	are	associated	with	patient	responses	to	immunotherapies	(Lauss	et	al.,	2017;	Le	et	al.,	2015).	In	addition	to	this,	the	patient	is	heterozygous	at	all	3	loci	for	class	I	HLA	alleles	(HLA-A,	-B,	-C),	which	is	also	known	to	correlate	with	patient	response	to	immune	checkpoint	blockade,	as	this	contributes	to	a	larger	repertoire	of	neoantigens	that	can	be	presented	to	the	immune	system	(Chowell	et	al.,	2017),	as	described	in	Chapter	1.4.1.		As	described	in	the	introduction,	activation	of	AGTR1	is	involved	in	some	pro-inflammatory	actions	(Benigni	et	al.,	2010;	Suzuki	et	al.,	2003),	which	are	often	associated	with	a	pro-tumour	environment	(Vinay	et	al.,	2015).	Based	on	this,	the	anti-tumour	effects	from	irbesartan	observed	in	this	case	study	might	have	been	due	to	a	reduction,	or	inactivation,	of	tumour	promoting	inflammation	in	the	tumour	microenvironment.	However,	the		 	 	 75	immunohistochemistry	(IHC)	data	(Figure	3-8	and	Figure	3-9)	do	not	show	evidence	of	high	infiltration	of	suppressive	cells,	which	are	known	to	be	inflammatory,	at	the	time	of	the	first	biopsy.	There	are	also	some	results	from	this	study,	described	in	the	next	section,	which	may	indicate	the	effects	of	irbesartan	may	not	have	been	achieved	through	direct	inhibition	of	AGTR1.		4.2 The	mechanism	of	action	of	irbesartan	may	have	involved	off	target	pathways		Angiotensin	receptor	blockers	(ARBs)	elicit	effects	through	down	regulation	of	the	angiotensin	II	signalling	pathway	by	inhibition	of	the	angiotensin	II	receptor	1	(AGTR1)	(Figure	1–3)	(Michel	et	al.,	2013).	In	the	case	of	this	patient,	AGTR1	gene	expression	was	lower	than	the	majority	of	CRC	patients	enrolled	in	the	POG	program	(Figure	3–3),	possibly	due	to	the	presence	of	the	S338fs	mutation	in	the	tumour.	Low	expression	of	the	target	receptor	may	have	enabled	irbesartan	to	interact	with	non-canonical	targets,	as	this	drug	and	other	ARB	family	members	have	been	reported	to	have	off	target	effects	in	vitro	(Marshall	et	al.,	2006;	Schupp	et	al.,	2005).	Interestingly,	the	frame-shift	mutation	was	detected	in	both	the	pre-	and	post-treatment	biopsies,	although	at	a	lower	frequency	in	the	post-treatment	sample	(0.32	and	0.14	VAF	respectively).	It	is	unclear	if	tumour	cells	carrying	the	AGTR1	mutation	were	selected	against	during	tumour	progression,	as	the	mutation	is	thought	to	be	deleterious	to	the	function	of	the	receptor,	and	one	might	expect	these	cells	to	increase	in	frequency	if	this	mutation	could	prevent	irbesartan	signalling	through	the	receptor.	Furthermore,	the	IHC	data	does	not	report	a	significant	change	in	FOS	protein	levels	between	the	two	biopsies	(Figure	3-8	and	Figure	3-9).	This	is	of	note,	as	a	decrease	in	FOS	expression	was	the	original	rationale		 	 	 76	for	this	treatment	following	POG	analysis,	as	literature	has	previously	described	down	regulation	of	the	AP-1	complex	with	irbesartan	treatment	in	vitro	(Cheng	et	al.,	2004).			As	described	in	the	introduction,	there	are	known	off	target	effects	for	ARBs,	including	irbesartan,	particularly	through	agonistic	modulation	of	PPARγ	(Michel	et	al.,	2013;	Schupp	et	al.,	2005),	which	may	cause	synergistic	phenotypes	as	blocking	AGTR1,	such	as	inhibition	of	inflammatory	pathways	(Benigni	et	al.,	2010;	Suzuki	et	al.,	2003;	Szeles	et	al.,	2007).	PPARγ	is	also	described	to	have	a	role	as	a	tumour	suppressor	(Campbell	et	al.,	2008),	which	may	be	induced	when	activated	by	ARBs.	However,	gene	expression	changes	(Figure	3–4)	and	IHC	data	(Figure	3-8	and	Figure	3-9)	indicate	the	involvement	of	the	immune	system	in	the	response	observed	in	this	patient,	so	an	anti-inflammatory	role	appears	to	be	more	plausible	here.			Another	off-target	effect	of	irbesartan	has	been	noted	in	a	case	study	of	a	patient	developing	a	hypersensitivity	reaction	to	the	drug,	with	an	onset	of	an	erythematous	maculopapular	rash.	The	effects	of	irbesartan	in	this	case	were	alleviated	after	the	patient	was	treated	with	an	alternative	ARB,	candesartan	(Cardoso	et	al.,	2016).	Therefore,	it	is	worth	considering	the	notion	that	irbesartan	could	be	driving	a	similar	reaction	in	this	case;	possibly	interacting	with	the	MHC	and	generating	a	hypersensitivity	involving	a	antigen	specific	to	the	tumour	(Bharadwaj	et	al.,	2012).	Another	POG	patient	with	both	melanoma	and	lung	cancer	was	also	given	irbesartan	therapy	on	the	basis	of	high	FOS	and	JUN	expression	(91st	and	99th	percentile	respectively	for	all	adult	POG	cases),	and	high	mutational	load	(435	SNVs),	but	therapy	was	discontinued	after	a	few	weeks	due	to	progression.	This	may	support	the	theory	of	hypersensitivity	as	none	of	the	6	MHC	class	I	HLA	alleles	detected	in	these	two	cases	were	the		 	 	 77	same,	and	this	type	of	response	may	be	dependent	on	patient	specific	MHC.	While	this	is	an	interesting	possibility,	it	is	difficult	to	test,	and	is	beyond	the	scope	of	this	thesis.				4.3 Confounding	factors	and	limitations		One	major	limitation	of	this	thesis	is	that	this	is	a	case	study	of	only	one	patient,	and	thus	it	is	hard	to	draw	conclusions	about	the	mechanism	of	action	of	the	drug.	This	also	limits	the	ability	to	predict	possible	biomarkers	of	response	that	may	be	applicable	to	other	patients.	Previous	literature	has	retrospectively	associated	irbesartan,	and	other	blood	pressure	regulators	(ACEI	and	BBs),	with	increased	patient	survival	in	colorectal	cancers	(Engineer	et	al.,	2013),	but	without	definitive	biomarkers	of	response,	it	is	challenging	to	suggest	treatment	with	irbesartan	to	other	patients	within	the	setting	of	a	clinical	trial.	The	fact	that	this	is	a	single	case	study	also	created	a	limitation	for	testing	immunogenic	peptides	with	the	ELISPOT	assay,	as	there	is	only	a	small	number	of	PBMCs	available	to	conduct	the	assay.	The	lack	of	IFNγ	response	observed	with	the	tested	peptides	does	not	mean	that	there	isn’t	an	immunogenic	peptide	present	in	the	tumour,	as	it	may	be	an	untested	peptide.	Furthermore,	the	use	of	peptide	pools	ensured	that	a	larger	number	of	peptides	could	be	tested	using	the	limited	number	of	cells,	but	it	may	have	also	lowered	the	potential	response	from	a	peptide	as	less	of	each	peptide	was	used	in	each	well.			Whilst	the	location	of	the	biopsies	is	comparable,	as	both	are	taken	from	a	lesion	in	the	lower	spine,	there	are	slight	differences	in	the	predicted	tumour	contents	of	the	samples	(Appendix	C).	Comparisons	between	the	two	biopsies	were	conducted	with	attempts	to	address	the	difference	in	tumour	content.	For	instance,	cell	infiltration	scores,	as	detected	by	multiplex	immunohistochemistry,	were	counted	per	mm2	of	tissue	region,	and	multiple	images	were		 	 	 78	taken	for	each	slide	to	provide	technical	replicates.	Also,	when	investigating	differences	in	T	cell	receptors,	the	repertoire	diversity	for	each	sample	was	assessed	using	a	CPK	method,	normalising	to	the	number	of	reads	aligned	to	the	TCR	region	in	each	biopsy.			At	the	time	of	the	relapse,	and	second	POG	biopsy	for	this	case,	18	months	had	elapsed	on	treatment	(Figure	1–4),	and	so	alterations	between	the	two	biopsies	may	be	a	combination	of	signatures	of	tumour	response	and	resistance.	For	example,	many	of	the	enriched	GO	terms	in	the	RNA	seq	data	describe	processes	that	are	associated	with	immune	activation,	a	sign	of	response,	(Figure	3–4),	which	is	corroborated	by	the	increased	infiltration	of	cytotoxic	T	cell	in	the	IHC	data	(Figure	3-8	and	Figure	3-9).	Conversely,	there	is	also	evidence	of	a	resistance	mechanism	to	an	immune	response	detected	in	the	analysis	with	the	increased	expression	of	immune	checkpoints	(Figure	3–4	and	Figure	3-9)	following	treatment.	Changes	in	mutation	frequency	also	support	a	resistance	phenotype,	as	the	majority	of	mutations	remain	at	a	similar	frequency	between	the	two	biopsies	(Figure	3–2).	This	is	comparable	to	a	progressive	or	stable	disease	response	to	immunotherapy	(nivolumab,	PD-1	inhibitor)	in	melanoma	patients,	with	most	mutations	remaining	at	a	high	frequency	pre-	and	post-	treatment,	whereas	patients	that	have	a	complete	or	partial	response	tend	to	have	a	large	shift	in	mutation	frequencies,	as	most	decrease	on	treatment	(Riaz	et	al.,	2017).		4.4 Concluding	remarks	and	future	directions		This	unusual	case	provides	an	example	of	a	successful	use	of	personalised	genomics	to	inform	treatment	decision	making,	although	the	response	observed	may	have	not	been	through	the	mechanism	expected.	Furthermore,	whole	genome	and	transcriptome	sequencing	in	this	case	have	been	very	useful	in	attempting	to	understand	the	response	to	irbesartan.			 	 	 79		Multiple	biopsies	from	patients,	whether	responding	or	progressing	on	therapy,	can	be	useful	to	understand	tumour	behaviour	to	particular	treatments,	enabling	generation	of	potential	biomarkers	that	could	be	used	to	identify	patients	that	may	benefit	from	a	particular	therapy,	or	patients	that	may	be	resistant.	The	mixture	of	resistance	and	response	phenotypes	seen	in	this	case	also	raises	the	question	about	the	utility	of	tracking	responses	to	therapies	from	an	easily	obtainable	sample,	such	as	blood.	In	this	case,	with	the	time	frame	between	the	two	biopsies	being	18	months,	it	is	difficult	to	infer	a	mechanism	of	response	to	irbesartan.	For	patients	that	are	responding	to	treatments,	blood	samples	may	help	to	elucidate	specific	mechanisms	of	response	to	therapies,	possibly	utilising	circulating	tumour	DNA	(ctDNA)	to	detect	shifts	in	mutation	frequencies	(Alcaide	et	al.,	2017),	or	possibly	TCR	sequencing	to	identify	expanding	clones,	as	has	been	demonstrated	with	a	vaccine	therapy	(Sheikh	et	al.,	2016).	Regular	blood	samples	during	treatment	may	have	been	useful	in	this	case	study	as	mutations	that	arose	first	in	response	to	treatment,	may	be	associated	with	a	resistance	mechanism.	Also,	as	the	response	to	irbesartan	appeared	to	be	systemic	(lesions	all	over	the	body	disappeared),	there	may	have	been	opportunity	to	detect	TCR	clonotypes	that	expanded	during	treatment	in	the	blood.		Irbesartan,	used	in	combination	with	beta	blockers	(BBs),	has	previously	been	linked	to	higher	survival	rates,	and	less	hospitalisations	in	colorectal	cancer	patients	(Engineer	et	al.,	2013).	This	retrospective	study	of	predominantly	elderly	patients	found	that	patients	taking	blood	pressure	medications	(combinations	of	ARBs/ACEIs	with	BBs)	had	better	overall	survival	(median	increase	of	646	days)	and	less	tumour	progression	than	those	not	taking	these	drugs,	but	this	study	was	largely	observational	and	did	not	directly	link	the	drug	to	tumour	regression.	Whilst	an	exact	mechanism	of	drug	action	is	still	not	clear	from	this	study,		 	 	 80	the	results	from	this	thesis	have	led	to	the	generation	of	further	hypotheses;	the	effect	of	irbesartan	may	not	be	directly	associated	with	AGTR1	inhibition	in	tumour	cells,	and	that	high	FOS	and	JUN	expression	alone	may	not	be	biomarkers	predicting	response	to	this	treatment,	as	originally	thought.	In	order	to	fully	understand	the	mechanism	of	response	observed	in	this	case,	there	will	need	to	be	further	investigation	into	these	hypotheses	using	an	in	vitro,	or	possibly,	in	vivo,	model.	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Term	 Description	 Log	q.value	 Overlap	GO:0046649	 lymphocyte	activation	 -48.750619	 128/681	GO:0046649	 lymphocyte	activation	 -48.750619	 128/681	GO:0050865	 regulation	of	cell	activation	 -44.178536	 111/560	GO:0002694	 regulation	of	leukocyte	activation	 -42.364608	 105/522	GO:0050778	 positive	regulation	of	immune	response	 -42.364608	 129/794	GO:0050778	 positive	regulation	of	immune	response	 -42.364608	 129/794	GO:0042110	 T	cell	activation	 -39.262942	 95/458	GO:0051249	 regulation	of	lymphocyte	activation	 -39.071298	 94/451	GO:0006954	 inflammatory	response	 -39.008937	 121/755	GO:0006954	 inflammatory	response	 -39.008937	 121/755	GO:0002263	 cell	activation	involved	in	immune	response	 -38.557567	 115/691	GO:0002263	 cell	activation	involved	in	immune	response	 -38.557567	 115/691	GO:0002366	 leukocyte	activation	involved	in	immune	response	 -38.103422	 114/687	GO:0050867	 positive	regulation	of	cell	activation	 -38.074345	 83/357	GO:0030155	 regulation	of	cell	adhesion	 -36.715212	 108/639	GO:0002274	 myeloid	leukocyte	activation	 -36.004701	 106/627	GO:0002696	 positive	regulation	of	leukocyte	activation	 -35.66545	 79/345	GO:0007159	 leukocyte	cell-cell	adhesion	 -35.328127	 77/330	GO:0002253	 activation	of	immune	response	 -34.068933	 105/647		 	 	101	Term	 Description	 Log	q.value	 Overlap	GO:0002250	 adaptive	immune	response	 -33.549359	 92/505	GO:0002250	 adaptive	immune	response	 -33.549359	 92/505	GO:0045785	 positive	regulation	of	cell	adhesion	 -32.935694	 80/386	GO:0019221	 cytokine-mediated	signaling	pathway	 -32.935694	 115/793	GO:0019221	 cytokine-mediated	signaling	pathway	 -32.935694	 115/793	GO:0051251	 positive	regulation	of	lymphocyte	activation	 -32.911187	 72/309	GO:0045055	 regulated	exocytosis	 -32.415665	 111/752	hsa05150	 Staphylococcus	aureus	infection	 -32.410857	 35/56	hsa05150	 Staphylococcus	aureus	infection	 -32.410857	 35/56	GO:1903037	 regulation	of	leukocyte	cell-cell	adhesion	 -32.319657	 70/297	GO:0050900	 leukocyte	migration	 -32.316233	 87/469	GO:0050900	 leukocyte	migration	 -32.316233	 87/469	hsa05322	 Systemic	lupus	erythematosus	 -31.948535	 49/133	hsa05322	 Systemic	lupus	erythematosus	 -31.948535	 49/133	GO:0002764	 immune	response-regulating	signaling	pathway	 -31.627777	 98/606	GO:0050863	 regulation	of	T	cell	activation	 -31.447287	 71/316	GO:0043299	 leukocyte	degranulation	 -30.226707	 90/534	R-HSA-198933	 Immunoregulatory	interactions	between	a	Lymphoid	and	a	non-Lymphoid	cell	 -29.815224	 47/132	R-HSA-198933	 Immunoregulatory	interactions	between	a	Lymphoid	and	a	non-Lymphoid	cell	 -29.815224	 47/132	GO:1903039	 positive	regulation	of	leukocyte	cell-cell	adhesion	 -29.37495	 58/219	GO:0002275	 myeloid	cell	activation	involved	in	immune	response	 -29.072195	 89/541		 	 	102	Term	 Description	 Log	q.value	 Overlap	GO:0050870	 positive	regulation	of	T	cell	activation	 -28.713519	 56/208	GO:0001816	 cytokine	production	 -28.622762	 100/685	GO:0001816	 cytokine	production	 -28.622762	 100/685	GO:0002521	 leukocyte	differentiation	 -28.54028	 83/480	GO:0022407	 regulation	of	cell-cell	adhesion	 -28.242825	 74/385	GO:0002444	 myeloid	leukocyte	mediated	immunity	 -28.027686	 88/547	GO:0002757	 immune	response-activating	signal	transduction	 -27.872847	 90/574	R-HSA-1280218	 Adaptive	Immune	System	 -27.285684	 104/765	GO:0022409	 positive	regulation	of	cell-cell	adhesion	 -26.86702	 59/252	GO:0001817	 regulation	of	cytokine	production	 -26.707703	 92/620	GO:0036230	 granulocyte	activation	 -26.321385	 82/504	GO:0042119	 neutrophil	activation	 -25.905548	 81/499	R-HSA-1280215	 Cytokine	Signaling	in	Immune	system	 -25.834891	 96/689	GO:0002768	 immune	response-regulating	cell	surface	receptor	signaling	pathway	 -24.987035	 76/455	R-HSA-6798695	 Neutrophil	degranulation	 -24.967711	 78/479	GO:0031347	 regulation	of	defense	response	 -24.69925	 102/795	GO:0043312	 neutrophil	degranulation	 -24.557578	 78/486	GO:0002283	 neutrophil	activation	involved	in	immune	response	 -24.385453	 78/489	GO:0032101	 regulation	of	response	to	external	stimulus	 -24.065544	 100/782	GO:0070661	 leukocyte	proliferation	 -23.755937	 58/277		 	 	103	Term	 Description	 Log	q.value	 Overlap	GO:0002697	 regulation	of	immune	effector	process	 -23.729515	 70/406	GO:0002697	 regulation	of	immune	effector	process	 -23.729515	 70/406	GO:0002446	 neutrophil	mediated	immunity	 -23.710055	 78/501	GO:0002683	 negative	regulation	of	immune	system	process	 -23.679813	 70/407	GO:0002683	 negative	regulation	of	immune	system	process	 -23.679813	 70/407	GO:0046651	 lymphocyte	proliferation	 -23.638146	 56/259	GO:0032943	 mononuclear	cell	proliferation	 -23.466746	 56/261	GO:0050670	 regulation	of	lymphocyte	proliferation	 -23.245579	 49/198	GO:0098609	 cell-cell	adhesion	 -23.165372	 98/776	GO:0032944	 regulation	of	mononuclear	cell	proliferation	 -23.153745	 49/199	GO:0070663	 regulation	of	leukocyte	proliferation	 -23.053601	 50/209	GO:0002460	 adaptive	immune	response	based	on	somatic	recombination	of	immune	receptors	built	from	immunoglobuli	 -22.65567	60/312	hsa04145	 Phagosome	 -22.482562	 43/154	GO:0006909	 phagocytosis	 -22.212446	 60/318	hsa04640	 Hematopoietic	cell	lineage	 -22.174739	 35/97	GO:0030098	 lymphocyte	differentiation	 -21.561159	 60/327	GO:0002429	 immune	response-activating	cell	surface	receptor	signaling	pathway	 -21.26632	 68/423	GO:0002449	 lymphocyte	mediated	immunity	 -21.231516	 58/310	GO:0006935	 chemotaxis	 -21.152422	 80/576	GO:0042330	 taxis	 -21.109299	 80/577	GO:0060326	 cell	chemotaxis	 -20.49816	 53/267		 	 	104	Term	 Description	 Log	q.value	 Overlap	GO:0019882	 antigen	processing	and	presentation	 -19.195733	 47/222	GO:0006897	 endocytosis	 -19.053492	 88/734	GO:0043062	 extracellular	structure	organization	 -18.797842	 62/393	GO:0043062	 extracellular	structure	organization	 -18.797842	 62/393	GO:0001819	 positive	regulation	of	cytokine	production	 -18.637158	 63/408	GO:0042098	 T	cell	proliferation	 -18.368584	 41/174	GO:0030217	 T	cell	differentiation	 -18.367903	 46/222	GO:0050727	 regulation	of	inflammatory	response	 -18.055812	 61/394	GO:0009617	 response	to	bacterium	 -17.941872	 75/578	GO:0009617	 response	to	bacterium	 -17.941872	 75/578	GO:0030595	 leukocyte	chemotaxis	 -17.718491	 43/200	hsa05323	 Rheumatoid	arthritis	 -17.678819	 30/90	GO:1902105	 regulation	of	leukocyte	differentiation	 -17.414886	 48/255	hsa04060	 Cytokine-cytokine	receptor	interaction	 -17.114832	 49/270	hsa04060	 Cytokine-cytokine	receptor	interaction	 -17.114832	 49/270	hsa05140	 Leishmaniasis	 -17.114268	 27/73	R-HSA-109582	 Hemostasis	 -16.90542	 78/645	R-HSA-109582	 Hemostasis	 -16.90542	 78/645	hsa04514	 Cell	adhesion	molecules	(CAMs)	 -16.768284	 36/145	GO:0002695	 negative	regulation	of	leukocyte	activation	 -16.041074	 36/152	M5884	 NABA	CORE	MATRISOME	 -16.02258	 48/275	M5884	 NABA	CORE	MATRISOME	 -16.02258	 48/275	GO:0050866	 negative	regulation	of	cell	activation	 -16.015096	 38/171	hsa05330	 Allograft	rejection	 -15.996896	 20/38	GO:0030198	 extracellular	matrix	organization	 -15.742232	 53/338		 	 	105	Term	 Description	 Log	q.value	 Overlap	GO:0050671	 positive	regulation	of	lymphocyte	proliferation	 -15.684074	 33/129	GO:0042129	 regulation	of	T	cell	proliferation	 -15.655258	 35/147	GO:0032946	 positive	regulation	of	mononuclear	cell	proliferation	 -15.582066	 33/130	GO:1903706	 regulation	of	hemopoiesis	 -15.254092	 61/449	GO:0006959	 humoral	immune	response	 -15.173868	 51/324	GO:0070665	 positive	regulation	of	leukocyte	proliferation	 -14.845002	 33/137	GO:0051250	 negative	regulation	of	lymphocyte	activation	 -14.647527	 32/130	M5885	 NABA	MATRISOME	ASSOCIATED	 -14.609539	 81/753	M5885	 NABA	MATRISOME	ASSOCIATED	 -14.609539	 81/753	hsa04672	 Intestinal	immune	network	for	IgA	production	 -14.568959	 21/49	GO:0002703	 regulation	of	leukocyte	mediated	immunity	 -14.500837	 36/169	GO:0034341	 response	to	interferon-gamma	 -14.343169	 38/191	GO:0019884	 antigen	processing	and	presentation	of	exogenous	antigen	 -14.333012	 37/181	GO:0032103	 positive	regulation	of	response	to	external	stimulus	 -14.283768	 46/280	GO:0042102	 positive	regulation	of	T	cell	proliferation	 -13.975269	 27/94	hsa05332	 Graft-versus-host	disease	 -13.833864	 19/41	hsa05416	 Viral	myocarditis	 -13.813967	 22/59	hsa05152	 Tuberculosis	 -13.682101	 36/179	GO:0002673	 regulation	of	acute	inflammatory	response	 -13.628037	 33/150	GO:0002237	 response	to	molecule	of	bacterial	origin	 -13.565624	 49/329	GO:0071346	 cellular	response	to	interferon-gamma	 -13.518196	 35/171		 	 	106	Term	 Description	 Log	q.value	 Overlap	GO:0045621	 positive	regulation	of	lymphocyte	differentiation	 -13.390334	 25/83	hsa04940	 Type	I	diabetes	mellitus	 -13.37909	 19/43	GO:1902107	 positive	regulation	of	leukocyte	differentiation	 -13.37909	 31/134	hsa04612	 Antigen	processing	and	presentation	 -13.182625	 24/77	GO:0031349	 positive	regulation	of	defense	response	 -13.168031	 58/456	GO:0031349	 positive	regulation	of	defense	response	 -13.168031	 58/456	R-HSA-1474244	 Extracellular	matrix	organization	 -13.147839	 46/300	GO:0097529	 myeloid	leukocyte	migration	 -13.135321	 35/176	GO:0050851	 antigen	receptor-mediated	signaling	pathway	 -12.994729	 42/255	GO:0030335	 positive	regulation	of	cell	migration	 -12.680797	 58/468	GO:0016064	 immunoglobulin	mediated	immune	response	 -12.656106	 36/193	GO:2000147	 positive	regulation	of	cell	motility	 -12.608563	 59/484	hsa05320	 Autoimmune	thyroid	disease	 -12.598609	 20/53	GO:0019724	 B	cell	mediated	immunity	 -12.59311	 36/194	GO:0040017	 positive	regulation	of	locomotion	 -12.561263	 61/514	GO:0032496	 response	to	lipopolysaccharide	 -12.529484	 46/312	R-HSA-202427	 Phosphorylation	of	CD3	and	TCR	zeta	chains	 -12.423559	 14/22	R-HSA-877300	 Interferon	gamma	signaling	 -12.291607	 25/92	GO:0048002	 antigen	processing	and	presentation	of	peptide	antigen	 -12.261229	 35/188	GO:0001818	 negative	regulation	of	cytokine	production	 -12.148453	 40/246	GO:0002526	 acute	inflammatory	response	 -12.123164	 37/212		 	 	107	Term	 Description	 Log	q.value	 Overlap	GO:0051272	 positive	regulation	of	cellular	component	movement	 -12.120405	 59/497	R-HSA-73728	 RNA	Polymerase	I	Promoter	Opening	 -12.072606	 21/63	R-HSA-202430	 Translocation	of	ZAP-70	to	Immunological	synapse	 -12.055356	 13/19	GO:1903708	 positive	regulation	of	hemopoiesis	 -11.88616	 33/172	R-HSA-2559586	 DNA	Damage/Telomere	Stress	Induced	Senescence	 -11.798098	 23/80	R-HSA-5334118	 DNA	methylation	 -11.777331	 21/65	GO:0060333	 interferon-gamma-mediated	signaling	pathway	 -11.691758	 24/89	R-HSA-171306	 Packaging	Of	Telomere	Ends	 -11.642248	 19/52	GO:0002699	 positive	regulation	of	immune	effector	process	 -11.602928	 33/176	R-HSA-5625886	 Activated	PKN1	stimulates	transcription	of	AR	(androgen	receptor)	regulated	genes	KLK2	and	KLK3	 -11.497033	21/67	GO:0071219	 cellular	response	to	molecule	of	bacterial	origin	 -11.417929	 32/168	R-HSA-427359	 SIRT1	negatively	regulates	rRNA	expression	 -11.358045	 21/68	R-HSA-3214815	 HDACs	deacetylate	histones	 -11.136813	 24/94	R-HSA-913531	 Interferon	Signaling	 -10.986472	 34/196	GO:0002478	 antigen	processing	and	presentation	of	exogenous	peptide	antigen	 -10.984619	 32/174		 	 	108	Term	 Description	 Log	q.value	 Overlap	GO:0070664	 negative	regulation	of	leukocyte	proliferation	 -10.951279	 21/71	hsa04610	 Complement	and	coagulation	cascades	 -10.944331	 22/79	GO:0045619	 regulation	of	lymphocyte	differentiation	 -10.825546	 30/155	GO:0045582	 positive	regulation	of	T	cell	differentiation	 -10.825546	 21/72	R-HSA-212300	 PRC2	methylates	histones	and	DNA	 -10.699296	 21/73	R-HSA-449147	 Signaling	by	Interleukins	 -10.666787	 54/464	GO:0071216	 cellular	response	to	biotic	stimulus	 -10.660699	 33/190	R-HSA-202433	 Generation	of	second	messenger	molecules	 -10.593039	 15/33	R-HSA-2299718	 Condensation	of	Prophase	Chromosomes	 -10.580834	 21/74	R-HSA-389948	 PD-1	signaling	 -10.568035	 13/23	R-HSA-977225	 Amyloid	fiber	formation	 -10.532531	 24/100	GO:0045088	 regulation	of	innate	immune	response	 -10.518583	 51/425	GO:0032945	 negative	regulation	of	mononuclear	cell	proliferation	 -10.482309	 20/67	GO:0050672	 negative	regulation	of	lymphocyte	proliferation	 -10.482309	 20/67	R-HSA-427389	 ERCC6	(CSB)	and	EHMT2	(G9a)	positively	regulate	rRNA	expression	 -10.344657	 21/76	GO:0002819	 regulation	of	adaptive	immune	response	 -10.319426	 27/131	hsa05145	 Toxoplasmosis	 -10.184785	 25/113	GO:0006956	 complement	activation	 -9.9910524	 29/156	hsa05310	 Asthma	 -9.7684182	 14/31	R-HSA-202733	 Cell	surface	interactions	at	the	vascular	wall	 -9.7621957	 27/138	hsa05321	 Inflammatory	bowel	disease	(IBD)	 -9.7238999	 19/65	GO:0071222	 cellular	response	to	lipopolysaccharide	 -9.7172399	 29/160		 	 	109	Term	 Description	 Log	q.value	 Overlap	GO:0002685	 regulation	of	leukocyte	migration	 -9.5874384	 30/173	R-HSA-5625740	 RHO	GTPases	activate	PKNs	 -9.2501933	 22/95	GO:0072376	 protein	activation	cascade	 -9.2143821	 30/179	R-HSA-912446	 Meiotic	recombination	 -9.1454339	 21/87	GO:0002440	 production	of	molecular	mediator	of	immune	response	 -9.1342165	 33/216	GO:0002920	 regulation	of	humoral	immune	response	 -9.1121517	 25/126	R-HSA-8936459	 RUNX1	regulates	genes	involved	in	megakaryocyte	differentiation	and	platelet	function	 -9.0725148	22/97	GO:0002224	 toll-like	receptor	signaling	pathway	 -9.0725148	 26/137	GO:0045580	 regulation	of	T	cell	differentiation	 -9.0393676	 25/127	hsa04659	 Th17	cell	differentiation	 -9.0361176	 23/107	GO:0030449	 regulation	of	complement	activation	 -8.9498398	 23/108	M3008	 NABA	ECM	GLYCOPROTEINS	 -8.9131422	 31/196	GO:2000257	 regulation	of	protein	activation	cascade	 -8.8691269	 23/109	R-HSA-73777	 RNA	Polymerase	I	Chain	Elongation	 -8.866219	 21/90	GO:0097530	 granulocyte	migration	 -8.7993796	 24/120								 	 	110	Top	200	gene	sets	enriched	in	down	regulated	genes	following	irbesartan	treatment.	Shaded	boxes	have	a	q	value	more	than	0.05.	Term	 Description	 Log10	q.value	 Overlap	M167	 PID	AP1	PATHWAY	 -4.91789	 9/70	M167	 PID	AP1	PATHWAY	 -4.91789	 9/70	GO:0010035	 response	to	inorganic	substance	 -4.3485406	 18/512	GO:0010035	 response	to	inorganic	substance	 -4.3485406	 18/512	GO:0042542	 response	to	hydrogen	peroxide	 -3.9811642	 10/137	GO:0000302	 response	to	reactive	oxygen	species	 -3.9811642	 12/224	GO:0000122	 negative	regulation	of	transcription	by	RNA	polymerase	II	 -3.9508367	 21/789	GO:0000122	 negative	regulation	of	transcription	by	RNA	polymerase	II	 -3.9508367	 21/789	GO:0008285	 negative	regulation	of	cell	proliferation	 -3.2378388	 19/725	GO:0008285	 negative	regulation	of	cell	proliferation	 -3.2378388	 19/725	GO:0050679	 positive	regulation	of	epithelial	cell	proliferation	 -3.0874941	 10/186	GO:0050679	 positive	regulation	of	epithelial	cell	proliferation	 -3.0874941	 10/186	GO:0050673	 epithelial	cell	proliferation	 -3.0874941	 14/406	GO:0050678	 regulation	of	epithelial	cell	proliferation	 -3.0444966	 13/352	GO:0006979	 response	to	oxidative	stress	 -2.849805	 14/435	GO:0034614	 cellular	response	to	reactive	oxygen	species	 -2.849805	 9/158	GO:0043620	 regulation	of	DNA-templated	transcription	in	response	to	stress	 -2.8361137	 8/118	GO:0043620	 regulation	of	DNA-templated	transcription	in	response	to	stress	 -2.8361137	 8/118		 	 	111	Term	 Description	 Log10	q.value	 Overlap	M166	 PID	ATF2	PATHWAY	 -2.6468115	 6/59	M166	 PID	ATF2	PATHWAY	 -2.6468115	 6/59	GO:0015671	 oxygen	transport	 -2.6468115	 4/15	GO:0015671	 oxygen	transport	 -2.6468115	 4/15	GO:0070848	 response	to	growth	factor	 -2.6468115	 17/694	GO:0070848	 response	to	growth	factor	 -2.6468115	 17/694	GO:0031214	 biomineral	tissue	development	 -2.6468115	 8/132	GO:0031214	 biomineral	tissue	development	 -2.6468115	 8/132	GO:0034599	 cellular	response	to	oxidative	stress	 -2.6468115	 11/284	GO:0097237	 cellular	response	to	toxic	substance	 -2.6468115	 10/229	hsa04657	 IL-17	signaling	pathway	 -2.6468115	 7/93	GO:0071363	 cellular	response	to	growth	factor	stimulus	 -2.2912198	 16/667	GO:0015669	 gas	transport	 -2.2912198	 4/19	GO:0010942	 positive	regulation	of	cell	death	 -2.2912198	 16/669	GO:0010942	 positive	regulation	of	cell	death	 -2.2912198	 16/669	GO:0009952	 anterior/posterior	pattern	specification	 -2.2898558	 9/202	GO:0009952	 anterior/posterior	pattern	specification	 -2.2898558	 9/202	GO:0001936	 regulation	of	endothelial	cell	proliferation	 -2.2494515	 8/155	GO:0043618	 regulation	of	transcription	from	RNA	polymerase	II	promoter	in	response	to	stress	 -2.2269609	 7/112	M285	 PID	HNF3A	PATHWAY	 -2.1837554	 5/44	GO:1901216	 positive	regulation	of	neuron	death	 -2.126191	 6/78	GO:0001667	 ameboidal-type	cell	migration	 -2.126191	 12/403	GO:0001935	 endothelial	cell	proliferation	 -2.0381706	 8/170	R-HSA-1247673	 Erythrocytes	take	up	oxygen	and	release	carbon	dioxide	 -1.866549	 3/9		 	 	112	Term	 Description	 Log10	q.value	 Overlap	GO:0070301	 cellular	response	to	hydrogen	peroxide	 -1.7638642	 6/92	GO:0060070	 canonical	Wnt	signaling	pathway	 -1.6943023	 10/313	GO:0060070	 canonical	Wnt	signaling	pathway	 -1.6943023	 10/313	GO:0001938	 positive	regulation	of	endothelial	cell	proliferation	 -1.6605848	 6/97	GO:0060828	 regulation	of	canonical	Wnt	signaling	pathway	 -1.612411	 9/259	GO:0071276	 cellular	response	to	cadmium	ion	 -1.612411	 4/31	GO:0003002	 regionalization	 -1.5631022	 10/329	GO:0071248	 cellular	response	to	metal	ion	 -1.5200164	 7/153	M255	 PID	HIF1	TFPATHWAY	 -1.5085657	 5/66	GO:0009991	 response	to	extracellular	stimulus	 -1.5085657	 12/484	GO:0009991	 response	to	extracellular	stimulus	 -1.5085657	 12/484	GO:0030111	 regulation	of	Wnt	signaling	pathway	 -1.5085657	 10/339	R-HSA-1237044	 Erythrocytes	take	up	carbon	dioxide	and	release	oxygen	 -1.5085657	 3/13	R-HSA-1480926	 O2/CO2	exchange	in	erythrocytes	 -1.5085657	 3/13	R-HSA-2168880	 Scavenging	of	heme	from	plasma	 -1.5085657	 3/13	GO:0071396	 cellular	response	to	lipid	 -1.5085657	 13/567	GO:0071396	 cellular	response	to	lipid	 -1.5085657	 13/567	GO:0009636	 response	to	toxic	substance	 -1.4958916	 12/491	GO:0007389	 pattern	specification	process	 -1.4903774	 11/419	GO:0032103	 positive	regulation	of	response	to	external	stimulus	 -1.4903774	 9/280	GO:0032103	 positive	regulation	of	response	to	external	stimulus	 -1.4903774	 9/280		 	 	113	Term	 Description	 Log10	q.value	 Overlap	GO:0035914	 skeletal	muscle	cell	differentiation	 -1.4903774	 5/69	GO:0035914	 skeletal	muscle	cell	differentiation	 -1.4903774	 5/69	GO:0006809	 nitric	oxide	biosynthetic	process	 -1.4693534	 5/70	GO:0006809	 nitric	oxide	biosynthetic	process	 -1.4693534	 5/70	GO:0051101	 regulation	of	DNA	binding	 -1.4481453	 6/114	GO:0051101	 regulation	of	DNA	binding	 -1.4481453	 6/114	hsa03320	 PPAR	signaling	pathway	 -1.4289894	 5/72	hsa03320	 PPAR	signaling	pathway	 -1.4289894	 5/72	M229	 PID	P38	ALPHA	BETA	DOWNSTREAM	PATHWAY	 -1.4289894	 4/38	GO:0061614	 pri-miRNA	transcription	by	RNA	polymerase	II	 -1.3984079	 4/39	GO:0046209	 nitric	oxide	metabolic	process	 -1.3984079	 5/74	GO:0017144	 drug	metabolic	process	 -1.3984079	 15/766	GO:0070167	 regulation	of	biomineral	tissue	development	 -1.357832	 5/76	GO:0001503	 ossification	 -1.357832	 10/370	GO:2001057	 reactive	nitrogen	species	metabolic	process	 -1.357832	 5/77	GO:0071241	 cellular	response	to	inorganic	substance	 -1.3406058	 7/177	R-HSA-2173782	 Binding	and	Uptake	of	Ligands	by	Scavenger	Receptors	 -1.333116	 4/42	GO:0031667	 response	to	nutrient	levels	 -1.3133679	 11/454	GO:0045429	 positive	regulation	of	nitric	oxide	biosynthetic	process	 -1.3133679	 4/43	GO:1904407	 positive	regulation	of	nitric	oxide	metabolic	process	 -1.3133679	 4/43	GO:0010631	 epithelial	cell	migration	 -1.296403	 9/311		 	 	114	Term	 Description	 Log10	q.value	 Overlap	GO:0015701	 bicarbonate	transport	 -1.296403	 4/44	M115	 PID	REG	GR	PATHWAY	 -1.296403	 5/82	GO:0015893	 drug	transport	 -1.296403	 7/184	GO:0030278	 regulation	of	ossification	 -1.296403	 7/184	GO:0090132	 epithelium	migration	 -1.2866044	 9/314	GO:0001568	 blood	vessel	development	 -1.2692688	 14/718	GO:0090130	 tissue	migration	 -1.2435306	 9/320	M60	 PID	NFAT	TFPATHWAY	 -1.2411163	 4/47	GO:0072593	 reactive	oxygen	species	metabolic	process	 -1.2411163	 8/255	GO:2000677	 regulation	of	transcription	regulatory	region	DNA	binding	 -1.2277691	 4/48	GO:0045639	 positive	regulation	of	myeloid	cell	differentiation	 -1.2277691	 5/88	GO:0060349	 bone	morphogenesis	 -1.2277691	 5/88	hsa05144	 Malaria	 -1.2130179	 4/49	GO:0001944	 vasculature	development	 -1.1990581	 14/743	GO:0071407	 cellular	response	to	organic	cyclic	compound	 -1.1980121	 12/569	GO:2000678	 negative	regulation	of	transcription	regulatory	region	DNA	binding	 -1.176123	 3/21	GO:0002763	 positive	regulation	of	myeloid	leukocyte	differentiation	 -1.176123	 4/51	GO:0043392	 negative	regulation	of	DNA	binding	 -1.176123	 4/51	GO:0072358	 cardiovascular	system	development	 -1.176123	 14/751	GO:2000379	 positive	regulation	of	reactive	oxygen	species	metabolic	process	 -1.1700731	 5/93	GO:0016055	 Wnt	signaling	pathway	 -1.1621783	 11/496		 	 	115	Term	 Description	 Log10	q.value	 Overlap	GO:0198738	 cell-cell	signaling	by	wnt	 -1.1570843	 11/498	GO:0035690	 cellular	response	to	drug	 -1.1570843	 9/343	GO:0042744	 hydrogen	peroxide	catabolic	process	 -1.1570843	 3/22	GO:0035924	 cellular	response	to	vascular	endothelial	growth	factor	stimulus	 -1.1570843	 4/53	GO:0006641	 triglyceride	metabolic	process	 -1.1531064	 5/96	GO:0051098	 regulation	of	binding	 -1.1522727	 9/347	GO:1903428	 positive	regulation	of	reactive	oxygen	species	biosynthetic	process	 -1.1522727	 4/54	GO:0009612	 response	to	mechanical	stimulus	 -1.1522727	 7/209	GO:0048705	 skeletal	system	morphogenesis	 -1.1522727	 7/209	GO:0051591	 response	to	cAMP	 -1.1344882	 5/98	GO:0030282	 bone	mineralization	 -1.1245316	 5/99	GO:0042136	 neurotransmitter	biosynthetic	process	 -1.1245316	 5/99	GO:0002040	 sprouting	angiogenesis	 -1.1186233	 6/154	GO:0036003	 positive	regulation	of	transcription	from	RNA	polymerase	II	promoter	in	response	to	stress	 -1.1186233	3/24	GO:0032101	 regulation	of	response	to	external	stimulus	 -1.1186233	 14/782	GO:0046686	 response	to	cadmium	ion	 -1.0891094	 4/58	GO:1903409	 reactive	oxygen	species	biosynthetic	process	 -1.0736855	 5/104	GO:0045428	 regulation	of	nitric	oxide	biosynthetic	process	 -1.0736855	 4/59	GO:0019216	 regulation	of	lipid	metabolic	process	 -1.0736855	 9/364	GO:0071277	 cellular	response	to	calcium	ion	 -1.0505194	 4/60	GO:0007519	 skeletal	muscle	tissue	development	 -1.0304173	 6/163		 	 	116	Term	 Description	 Log10	q.value	 Overlap	GO:0007586	 digestion	 -0.9954069	 6/166	M8	 PID	ENDOTHELIN	PATHWAY	 -0.9897361	 4/63	GO:0071496	 cellular	response	to	external	stimulus	 -0.9645124	 8/305	GO:0006639	 acylglycerol	metabolic	process	 -0.9645124	 5/113	GO:0043542	 endothelial	cell	migration	 -0.9645124	 7/236	GO:0060538	 skeletal	muscle	organ	development	 -0.9645124	 6/171	GO:0048514	 blood	vessel	morphogenesis	 -0.9645124	 12/640	GO:0006638	 neutral	lipid	metabolic	process	 -0.9645124	 5/114	GO:0035767	 endothelial	cell	chemotaxis	 -0.9645124	 3/29	GO:1903708	 positive	regulation	of	hemopoiesis	 -0.9639071	 6/172	GO:0042594	 response	to	starvation	 -0.9434817	 6/174	GO:0030500	 regulation	of	bone	mineralization	 -0.9434817	 4/67	GO:0051090	 regulation	of	DNA	binding	transcription	factor	activity	 -0.9434817	 9/390	hsa05031	 Amphetamine	addiction	 -0.9355969	 4/68	GO:0032637	 interleukin-8	production	 -0.906567	 4/70	GO:0006954	 inflammatory	response	 -0.9035794	 13/755	GO:0060348	 bone	development	 -0.8598508	 6/185	GO:0010632	 regulation	of	epithelial	cell	migration	 -0.8520319	 7/255	hsa04010	 MAPK	signaling	pathway	 -0.8520319	 7/255	GO:0010038	 response	to	metal	ion	 -0.8456642	 8/331	GO:1902893	 regulation	of	pri-miRNA	transcription	by	RNA	polymerase	II	 -0.840017	 3/34	GO:0007178	 transmembrane	receptor	protein	serine/threonine	kinase	signaling	pathway	 -0.8344418	 8/333	GO:0042632	 cholesterol	homeostasis	 -0.8220979	 4/76		 	 	117	Term	 Description	 Log10	q.value	 Overlap	GO:0055092	 sterol	homeostasis	 -0.8220979	 4/76	hsa05143	 African	trypanosomiasis	 -0.8220979	 3/35	GO:0071392	 cellular	response	to	estradiol	stimulus	 -0.8220979	 3/35	GO:0001816	 cytokine	production	 -0.8206349	 12/685	GO:0032870	 cellular	response	to	hormone	stimulus	 -0.8174496	 12/688	R-HSA-8953897	 Cellular	responses	to	external	stimuli	 -0.8174496	 10/506	GO:0046683	 response	to	organophosphorus	 -0.8146992	 5/131	GO:1905114	 cell	surface	receptor	signaling	pathway	involved	in	cell-cell	signaling	 -0.7940709	 11/601	GO:0019221	 cytokine-mediated	signaling	pathway	 -0.7865763	 13/793	M65	 PID	FRA	PATHWAY	 -0.7865763	 3/37	GO:1902107	 positive	regulation	of	leukocyte	differentiation	 -0.7865763	 5/134	GO:0002042	 cell	migration	involved	in	sprouting	angiogenesis	 -0.7831516	 4/80	GO:0007612	 learning	 -0.7723528	 5/136	GO:0090263	 positive	regulation	of	canonical	Wnt	signaling	pathway	 -0.7723528	 5/136	GO:1903426	 regulation	of	reactive	oxygen	species	biosynthetic	process	 -0.7239312	 4/84	GO:0001817	 regulation	of	cytokine	production	 -0.7219393	 11/620	GO:0042133	 neurotransmitter	metabolic	process	 -0.717912	 5/141	GO:1901214	 regulation	of	neuron	death	 -0.7053367	 7/281	GO:0043433	 negative	regulation	of	DNA	binding	transcription	factor	activity	 -0.6906574	 5/144	GO:0045444	 fat	cell	differentiation	 -0.6894019	 6/211		 	 	118	Term	 Description	 Log10	q.value	 Overlap	GO:0030336	 negative	regulation	of	cell	migration	 -0.6689904	 7/288	GO:0014074	 response	to	purine-containing	compound	 -0.6689904	 5/147	GO:0009798	 axis	specification	 -0.6680524	 4/90	GO:0042743	 hydrogen	peroxide	metabolic	process	 -0.6531491	 3/44	GO:0001525	 angiogenesis	 -0.6517265	 10/551	GO:0022600	 digestive	system	process	 -0.6471077	 4/92	GO:0007568	 aging	 -0.6424346	 7/296	M2579	 ST	DIFFERENTIATION	PATHWAY	IN	PC12	CELLS	 -0.6374939	 3/45	GO:0043534	 blood	vessel	endothelial	cell	migration	 -0.6226362	 5/155	GO:0051100	 negative	regulation	of	binding	 -0.6226362	 5/155	GO:2000146	 negative	regulation	of	cell	motility	 -0.6132972	 7/303	GO:0098869	 cellular	oxidant	detoxification	 -0.6132972	 4/96	GO:0043525	 positive	regulation	of	neuron	apoptotic	process	 -0.595211	 3/48	GO:0071560	 cellular	response	to	transforming	growth	factor	beta	stimulus	 -0.5928375	 6/230	GO:0007610	 behavior	 -0.592662	 10/571	GO:0002521	 leukocyte	differentiation	 -0.5883119	 9/480	GO:0030099	 myeloid	cell	differentiation	 -0.583264	 8/393	GO:1990748	 cellular	detoxification	 -0.5734233	 4/100	GO:0032496	 response	to	lipopolysaccharide	 -0.5734122	 7/312	GO:0048511	 rhythmic	process	 -0.5734122	 7/312	GO:0071559	 response	to	transforming	growth	factor	beta	 -0.5732311	 6/234	GO:0031668	 cellular	response	to	extracellular	stimulus	 -0.5681736	 6/235	GO:0007611	 learning	or	memory	 -0.5656933	 6/236		 	 	119	Term	 Description	 Log10	q.value	 Overlap	GO:0070997	 neuron	death	 -0.5580856	 7/316	GO:0009948	 anterior/posterior	axis	specification	 -0.5553384	 3/51	GO:0001501	 skeletal	system	development	 -0.5551981	 9/491	GO:0051271	 negative	regulation	of	cellular	component	movement	 -0.5387339	 7/320	GO:0050994	 regulation	of	lipid	catabolic	process	 -0.5387339	 3/52	GO:0098754	 detoxification	 -0.531029	 4/105	GO:0030177	 positive	regulation	of	Wnt	signaling	pathway	 -0.5253146	 5/170	GO:0051187	 cofactor	catabolic	process	 -0.5112518	 3/54																	 	 	120	Appendix B: Immunohistochemistry staining from patient diagnosis Results	from	immunohistochemistry	staining,	as	described	in	the	methods	chapter,	for	the	earlier	diagnostic	sample	from	the	patient	from	2010	(Figure	1–4).	Panels	and	cell	markers	are	as	described	in	the	methods,	and	in	the	other	biopsy	samples	(Figure	3-8).					Cytotoxic T CellsPD1  CD8  PDL1 CD33  CD11B  HLA-DRCD3  FOSCD20  CD79A	 	 	121	Appendix C: Sequencing coverage for biopsy samples Sequencing	coverage,	tumour	content	and	ploidy	information	from	POG	sequencing	pipeline	for	biopsy	1,	2,	the	diagnostic	sample	and	the	normal	DNA	comparator.		 Pre-POG diagnostic FFPE sample Biopsy 1 (Pre-treatment) Biopsy 2 (Post-treatment) Normal DNA comparator  Sample collection date December	2012	 September	2014	 March	2016	 September	2014	Tumour DNA input (ug) 1	 1	 1	 1	Tumour RNA input (ug) --	 5	 2	 --	Total coverage (DNA) 50X	 86X	 92X	 43X	Duplicates (DNA) 2.4%	 2.2%	 2.0%	 1.6%	Total mapped reads (RNA) --	 159M	 191M	 --	Sample origin Abdominal	cavity	–	Retroperitoneal	(FFPE)	Bone-vertebra	 Bone-vertebra	 Blood	Pathology estimated tumour content 60	 43	 45	 --	Sequencing estimated tumour content (APOLLOH) 60	 63	 55	 --	Ploidy Diploid	 Diploid	 Diploid	 --				 	 	122	Appendix D: Reagents and resource information Antibody	information	for	markers	used	in	the	IHC	panels,	and	peptide	sourcing	for	the	ELISPOT	experiments.		Antibody	 Source	 Identifier	Mouse	Anti-CD20	Monoclonal	Antibody,	Clone	L26	 Biocare	Medical	 CM004	Rabbit	Anti-CD79A	Monoclonal	Antibody,	Clone	SP18	 Spring	Bioscience	 M3182	Rabbit	Anti-PDL1	Monoclonal	Antibody,	Clone	SP142	 Spring	Bioscience	 M4422	Mouse	Anti-PD1	Monoclonal	Antibody,	Clone	NAT105	 Cell	Marque	 315M-94	Mouse	Anti-CD8	Monoclonal	Antibody,	Clone	C8/144B	 Cell	Marque	 108M-94	Mouse	Anti-Granzyme	B	Monoclonal	Antibody,	Clone	GrB-7	 Thermo	Fisher	 MA1-35461	Rabbit	Anti-CD3	Monoclonal	Antibody,	Clone	SP7	 Spring	Bioscience	 M3074	Rabbit	Anti-HLA-DR	Monoclonal	Antibody,	Clone	SEPR3692	 Abcam	 Ab92511	Mouse	Anti-CD33	Monoclonal	Antibody,	Clone	6C5/2	 Abcam	 Ab11032	Rabbit	Anti-CD11b	Monoclonal	Antibody,	Clone	EPR1344	 Abcam	 Ab133357	Mouse	Anti-c-FOS	Monoclonal	Antibody,	Clone	2H2	 Abcam	 Ab208942	anti-CD3	 Mabtech	 3605-1-50	IFNγ	1-D1K	antibody	 Mabtech	 3420-3-250	Biotinylated	IFNγ	7-B7-1	antibody	 Mabtech	 3420-6-250		 	 	Peptides		 Source	 Identifier	8-11mer	peptide	sequences	 Peptide	2.0	 https://www.peptide2.com/		CEF	peptide	pool		 Mabtech	 3615-1			 	 	123	Appendix E: Mutations increasing in variant allele frequency following irbesartan treatment (cluster 6)  Mutations	increasing	in	variant	allele	frequency	(VAF)	in	the	second	biopsy	compared	to	the	first.	The	coding	mutation	is	the	amino	acid	change	detected	in	the	second	biopsy,	and	previous	mutations	refers	to	the	number	of	other	mutations	found	in	that	gene	(detected	in	the	first	biopsy).			Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	AC104809.3	 	 	 A522T	 4	ACAP1	 Centaurin-Beta-1	 Q15027	 Q10*		 1	ALOXE3	 Arachidonate	Lipoxygenase	3	 Q9BYJ1	 L314M	 1	ANKRD29	 Ankyrin	Repeat	Domain	29	 Q8N6D5	 R200C	 0	APBA1	 Amyloid	Beta	Precursor	Protein	Binding	Family	A	 P05067	 Q34P	 0	APOD	 Apolipoprotein	D	 P05090	 L32M	 0	ARSF	 Arylsulfatase		F	 P54793	 P419H	 0	ASCL3	 Achaete-Scute	Family	BHLH	Transcription	Factor	3	 Q9NQ33	 P54S	 1	ASPHD2	 Aspartate	Beta-Hydroxylase	Domain	Containing	2		 Q6ICH7	 C45R		 0	ATP10D	 ATPase	Phospholipid	Transporting	10D	(Putative),	ATPase,	Class	V,	Type	10D		 Q9P241	 G992V	 5	BTN1A1	 Butyrophilin	Subfamily	1	Member	A1	 Q13410	 M438T	 2		 	 	124	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	BTRC	 Beta-Transducin	Repeat	Containing	E3	Ubiquitin	Protein	Ligase,	F-Box	And	WD-Repeat	Protein	1B,	BetaTrCP	Q9Y297	 A502V		 0	C11orf16	 Uncharacterised	protein	C11orf16	 Q9NQ32	 R8M	 4	C14orf118	 G-Patch	Domain	Containing	2	Like,	GPATCH2L	 Q9NWQ4	 Y20C	 1	C19orf10	 Myeloid	derived	growth	factor,	MYDGF,	stromal-cell	derived	growth	factor	sf20,	interleukin	25	Q969H8	 I94T	 0	C1QL4	 Complement	Component	1,	Q	Subcomponent-Like	4,	C1q	And	Tumor	Necrosis	Factor-Related	Protein	11	Q86Z23	 R63H	 0	C3orf30	 Chromosome	3	Open	Reading	Frame	30,	TSCPA	 Q96M34	 D401G	 1	C9orf91	 Chromosome	9	Open	Reading	Frame	91	 Q5VZI3	 P14T	 0	CARD11	 Caspase	Recruitment	Domain	Family	Member	11,	CARMA1,	Bcl10-Interacting	Maguk	Protein	3	Q9BXL7	 P1114S	 0	CCDC17	 Coiled-Coil	Domain	Containing	17	 Q96LX7	 Splice	site	acceptor	 2	CCDC42	 Coiled-Coil	Domain	Containing	42	 Q96M95	 L182F	 0	CCDC96	 Coiled-Coil	Domain	Containing	96	 Q2M329	 G541C	 1	CEP128	 Centrosomal	protein	of	128	kDa,	C14orf61,	C14orf145	 Q6ZU80	 L536S	 2	CFP	 Complement	Factor	Properdin,	PFC,	 P27918	 W385L	 0		 	 	125	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	PROPERDIN,	Complement	factor	P	CHRAC1	 Chromatin	accessibility	complex	protein	1,	DNA	Polymerase	Epsilon	Subunit	P15,	Histone-fold	protein	CHRAC15	Q9NRG0	 Splice	site	acceptor	 0	CHRM3-AS2	 CHRM3	Antisense	RNA	2	 NA	 Splice	site	donor	 0	CLCNKA	 Chloride	voltage-gated	channel	Ka,	CIC-K1,	Chloride	channel	kidney	A	 P51800	 Splice	site	donor	 2	COL17A1	 Collagen	Type	XVII	Alpha	1,	BPAG2,	BP180	 Q9UMD9	 G1263*	 4	CPNE7	 Copine	7	 Q9UBL6	 G495S	 1	CRMP1	 Dihydropyrimidinase-related	protein	1,	Collapsin	Response	Mediator	Protein	1,	CRMP-1,	DRP-1	Q14194	 Q26*	 1	CTSC	 Cathepsin	C,	Dipeptdyl	peptidase	1	 P53634	 I136T	 0	CTSC	 Cathepsin	C,	Dipeptdyl	peptidase	1	 P53634	 T391I	 0	CYBB	 Cytochrome	B-245	Beta	polypeptide,	NOX2,	NADPH	oxidase	2,	gp91	(phox)	 P04839	 H111R	 0	CYP3A7	 Cytochrome	P450	3A7,	CYPIIIA7	 P24462	 S501N	 1	DDC	 Dopa	decarboxylase	 P20711	 N196D	 2	DDOST	 Dolichyl-Diphosphooligosaccharide--Protein	Glycosyltransferase,		OST48,	Advanced	Glycation	End-Product	Receptor	1,	CDG1R,	WBP1	P39656	 I111T	 1		 	 	126	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	DNAH12	 Dynein	axonemal	heavy	chain	12,	HL19,	ciliary	dynein	heavy	chain	12	 Q6ZR08	 A841D	 4	DNAJC14	 DNAJ	Heat	shock	protein	family	(Hsp40)	member	C14,	HDJ3,	DNAJ	protein	homolog	3,	DNAJ,	dopamine	receptor	interacting	protein	Q6Y2X3	 H16N	 0	DOCK1	 Dedicator	of	cytokinesis	1,	DOCK180,	Ced5	 Q14185	 W103*	 2	DZANK1	 Double	zinc	ribbon	and	ankyrin	repeat	containing	protein	1,		 Q9NVP4	 A28T	 2	E2F8	 E2F	transcription	factor	8	 A0AVK6	 D242Y	 0	EIF4ENIF1	 Eukaryotic	translation	initiation	factor	4E	transporter	 Q9NRA8	 G7C	 1	ELF5	 ETS	related	transcription	factor	Elf5,	E74	Like	ETS	Transcription	Factor	5	 Q9UKW6	 Splice	site	donor	 0	EXOSC1	 Exosome	component	1,	CSL4	 Q9Y3B2	 A38V	 0	FAT2	 FAT	Atypical	cadherin	2,	HFAT2,	CDHF8,	Multiple	epidermal	growth	factor-like	domains	protein	1	Q9NYQ8	 Q120*	 9	FURIN	 Paired	amino	acid	cleaving	enzyme,	PACE,	FUR,	PCSK3	 P09958	 A786T	 0	GALNTL5	 Polypeptide	N-Acetylgalactosaminyltransferase-Like	5,	Inactive	polypeptide	N-acetylgalactosaminyltransferase-like	Q7Z4T8	 S137N	 2		 	 	127	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	protein	5	GARNL3	 GTPase	activating	Rap/RanGAP	domain-like	3		 Q5VVW2	 N52D	 0	GARNL3	 GTPase	activating	Rap/RanGAP	domain-like	3		 Q5VVW2	 Splice	site	acceptor	 0	GAS7	 Growth	arrest	specific	7,	MLL/GAS7	fusion	protein	 O60861	 M129T	 1	GBP3	 Guanylate-binding	protein	3	 Q9H0R5	 V145A	 3	GNL1	 G	Protein	Nucleolar	1	(Putative),	Guanine	Nucleotide	Binding	Protein-Like	1,	HSR1	P36915	 R313Q	 0	GRIPAP1	 GRIP	associated	protein	1,	GRASP-1	 Q4V328	 L72P	 0	HCN4	 Hyperpolarization	Activated	Cyclic	Nucleotide	Gated	Potassium	Channel	4,	SSS2	Q9Y3Q4	 L391I	 1	HECW1	 E3	ubiquitin-protein	ligase	HECW1,	NEDL1	 Q76N89	 S808P	 0	HIST1H1B	 Histone	cluster	1	H1b,	Histone	1	H1b,	Histone	H1a,	H1F5	 P16401	 A52V	 0	HLF	 Hepatic	leukaemia	factor	 Q16534	 P111H	 0	IQGAP2	 IQ	motif	containing	GTPase	activating	protein	2	 Q13576	 G350C	 6	IRGC	 Immunity	related	GTPase	cinema,	ligp5,	Interferon	inducible	GTPase	5	 Q6NXR0	 P281S	 0		 	 	128	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	ITFG3	 FAM234A,	Integrin	Alpha	FG-GAP	Repeat	Containing	3	 Q9H0X4	 R527C	 0	KCMF1	 Potassium	channel	modulatory	factor	1,	FIGC,	ZZ-type	zinc	finger-containing	protein	1	Q9P0J7	 Q242R	 0	KCNK13	 Potassium	channel	subfamily	K	member	13,	THIK-1		 Q9HB14	 S296N	 0	KDM6B	 Lysine	demethylase	6B,	jumonji	domain	containing	3,	JMJD3	 O15054	 P469H	 0	KIAA0528	 C2CD5,	C2	calcium-dependent	domain	containing	5	 Q86YS7	 V768I	 0	KIAA1107	 NA	 Q9UPP5	 T21A	 2	KIAA1199	 Cell	migration	inducing	hyaluronan	binding	protein,	CEMIP,	colon	cancer	secreted	protein	1	Q8WUJ3	 G840D	 1	KIAA1614	 NA	 Q5VZ46	 Q511H	 3	KIR3DX1	 Killer	Cell	Immunoglobulin	Like	Receptor,	Three	Ig	Domains	X1,	KIR3DL0,	LENG12,	Leukocyte	Receptor	Cluster	Member	12		Q9H7L2	 R54W	 0	L3MBTL3	 Lethal(3)malignant	brain	tumor-like	protein	3,	Lethal(3)malignant	brain	tumor-like	protein	3,	MBT1	Q96JM7	 R36W	 1	LAGE3	 L	antigen	family	member	3,	protein	ESO-3,	ITBA2	 Q14657	 Splice	site	donor	 0		 	 	129	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	LILRA6	 Leukocyte	Immunoglobulin	Like	Receptor	A6,	ILT8	 Q6PI73	 H405D		 12	MAP3K1	 Mitogen-Activated	Protein	Kinase	Kinase	Kinase	1,	MEK	Kinase	1,	MEKK1,	MEKK	Q13233	 A82T	 4	MBTD1	 Mbt	Domain	Containing	1,	SA49P01	 Q05BQ5	 Q282*	 0	MLL3	 Lysine	methyltransferase	2C,	KMT2C	 Q8NEZ4	 I3488M	 6	MLLT6	 Myeloid/Lymphoid	Or	Mixed-Lineage	Leukemia;	Translocated	To	6,	protein	AF-17,	ALL1-fused	gene	from	chromosome	17	protein	P55198	 Q350*	 1	MYO5A	 Myosin	VA,	dilute	heavy	chain	non-muscle,	myosin-12,	unconventional	myosin	VA	Q9Y4I1	 R1409W	 2	MYT1L	 Myelin	Transcription	Factor	1	Like,	MyT1-L,	MRD39	 Q9UL68	 V1016I	 0	NACC1	 Nucleus	Accumbens	Associated	1,	NAC1	 Q96RE7	 N61D	 0	NCKAP5	 NCK	Associated	Protein	5,	NAP5,	peripheral	clock	protein	 O14513	 P1493S	 4	NCOA4	 Nuclear	Receptor	Coactivator	4,	Androgen	Receptor-Associated	Protein	Of	70	Kda,	NCoA4	Q13772	 R24Q	 2	NEFM	 Neurofilament	Medium	Polypeptide,	NEF3	 P07197	 T481M	 1		 	 	130	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	NLN	 Neurolysin	 Q9BYT8	 T323A	 0	NOTCH2	 HN2	 Q04721	 H1133Y	 10	NPAS4	 Neuronal	PAS	Domain	Protein	4,	PASD10,	Class	E	basic	helix-loop-helix	protein	79	Q8IUM7	 R145H	 0	NUP133	 Nucleoporin	133kDa	 Q8WUM0	 T371A	 1	OMP	 Olfactory	marker	protein	 P47874	 V16I	 0	OR2M5	 Olfactory	Receptor	Family	2	Subfamily	M	Member	5	 A3KFT3	 M69V	 0	OR5M11	 Olfactory	receptor	5M11	 Q96RB7	 M49T	 3	OSBPL3	 Oxysterol	Binding	Protein	Like	3,	OSBP3,	ORP3	 Q9H4L5	 E217D	 2	PCDH20	 Protocadherin	20,	protocadherin	13	 Q8N6Y1	 S636G	 1	PCDHA12	 Protocadherin	Alpha	12	 Q9UN75	 V555M	 1	PCDHGA12	 Protocadherin	Gamma	Subfamily	A	12,	fibroblast	cadherin	FIB3	 O60330	 T569M	 0	PCDHGA8	 Protocadherin	Gamma	Subfamily	A	8	 Q9Y5G5	 E12K	 0	PCDHGB3	 Protocadherin	Gamma	Subfamily	B	3		 Q9Y5G1	 T623M	 2	PCED1B	 PC-Esterase	Domain	Containing	1B,	FAM113B	 Q96HM7	 R415W	 0	PGBD5	 PiggyBac	transposable	element-derived	protein	5	 Q8N414	 G187R	 0	PI4KAP1	 Phosphatidylinositol	4-Kinase	Alpha	Pseudogene	1	 Q8N8J0	 Splice	site	acceptor	 0		 	 	131	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	PIWIL4	 Piwi	Like	RNA-Mediated	Gene	Silencing	,	HIWI2,	PIWI	 Q7Z3Z4	 T154R	 1	POP1	 POP1	Homolog,	Ribonuclease	P/MRP	Subunit,	Processing	Of	Precursors	1	 Q99575	 T277M	 0	PPFIA3	 Liprin-Alpha	3,	Protein	Tyrosine	Phosphatase,	Receptor	Type,	F	Polypeptide	(PTPRF)	Interacting	Protein	(Liprin)	Alpha	3,	LIPRIN	O75145	 R322W	 2	PRDM15	 PR	Domain	15,	zinc	finger	protein	298	 P57071	 V159M	 6	PRKAG2	 Protein	Kinase	AMP-Activated	Non-Catalytic	Subunit	Gamma	2,	AMPK	subunit	gamma	2,	5'-AMP-activated	protein	kinase	subunit	gamma-2	Q9UGJ0	 P168L	 0	PSME4	 Proteasome	activator	complex	subunit	4,	proteasome	activator	PA200		 Q14997	 M319I	 1	PTPN11	 Protein	Tyrosine	Phosphatase,	Non-Receptor	Type	11,	SH-PTP2,	SHP2	 Q06124	 V14M	 0	PTPN13	 Protein	Tyrosine	Phosphatase,	Non-Receptor	Type	13,	PTP1E,	FAP1	 Q12923	 Q385E	 0	PTPRA	 Protein	Tyrosine	Phosphatase,	Receptor	Type	A,	PTPA,	HEPTP	 P18433	 S299G	 0	RAD21L1	 RAD21	Cohesin	Complex	Component	Like	1		 Q9H4I0	 L299R	 3	RP11-108K14.4	NA	 	 E200K	 5		 	 	132	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	RP11-124D2.6	 NA	 NA	 M1I	 0	RP11-297A16.2	ENSG00000230534	 	 Splice	site	acceptor	 0	RP11-45I20.1	 Uncharacterised	LOC101928929	 	 Splice	site	donor	 0	SAP18	 Sin3A	Associated	Protein	18kDa,	Cell	Growth-Inhibiting	Gene	38	Protein,	SAP18P	O00422	 R168C		 1	SLC10A3	 Solute	Carrier	Family	10	Member	3,	P3	 P09131	 A2T	 0	SLC5A11	 Solute	Carrier	Family	5	Member	11,	KST1	 Q8WWX8	 Q303*	 0	SNRK	 SNF	Related	Kinase		 Q9NRH2	 G449D	 0	SORCS1	 Sortilin	Related	VPS10	Domain	Containing	Receptor	1,	HSorCS	 Q8WY21	 A103V	 0	SPOPL	 Speckle	Type	BTB/POZ	Protein	Like,	roadkill	homolog	2,	speckle	type	POZ	protein-like	Q6IQ16	 V206M	 0	STIM1	 Stromal	Interaction	Molecule	1,	GOK,	IMD10	 Q13586	 S346T	 0	STRN4	 Striatin	4,	zinedin,	SIN	 Q9NRL3	 V159M	 1	TAX1BP1	 Tax1	Binding	Protein	1,	TRAF6-binding	protein,	T6BP	 Q86VP1	 A375T	 1	TBRG1	 Transforming	Growth	Factor	Beta	Regulator	1,	nuclear	interactor	of	ARF	and	MDM2	Q3YBR2	 R180W	 0		 	 	133	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	TBX6	 T	box	6,	SCDO5	 O95947	 W101R	 0	TG	 Thryoglobulin	 P01266	 L1592P	 6	TIMM13	 Translocase	Of	Inner	Mitochondrial	Membrane	13,	Ppv1,	TIMM13B/A	 Q9Y5L4	 G41S	 0	TMEM104	 Transmembrane	Protein	104	 Q8NE00	 P67S	 1	TMEM206	 Transmembrane	Protein	206	 Q9H813	 R148W	 0	TMEM62	 Transmembrane	protein	62	 Q0P6H9	 I61T	 0	TMEM63C	 Transmembrane	Protein	63C,	C14orf171,	CSC1	 Q9P1W3	 V610A	 0	TNKS1BP1	 Tankyrase	1	Binding	Protein	1,	TAB182	 Q9C0C2	 R127Q	 3	TRABD	 TraB	Domain	Containing,	protein	TTG2	 Q9H4I3	 C108Y	 0	TTN	 Titin,	connectin,	EOMFC,	CMPD4,	cardiomyopathy	dilated	1G	 Q8WZ42	 Y15532C	 22	TUBGCP6	 Tubulin	gamma	complex	associated	protein	6,	GCP-6	 Q96RT7	 S213L	 4	USP5	 Ubiquitin	Specific	Peptidase	5,	isopeptidase	T,	ISOT	 P45974	 T559S	 0	WBSCR17	 Williams-Beuren	Syndrome	Chromosome	Region	17,	Williams-Beuren	Syndrome	Chromosomal	Region	17	Protein,	protein-UDP	Acetylgalactosaminyltransferase-Like	Protein	3,	GALNTL3	Q6IS24	 E19Q	 0	WDR41	 WD	Repeat	Domain	41	 Q9HAD4	 Splice	site	donor	 0		 	 	134	Gene	 Synonym		 Uniprot	 Coding	Mutation	Previous	mutations	YIF1A	 Yip1	Interacting	Factor	Homolog	A	Membrane	Trafficking	Protein,	YIF1,	54TM	O95070	 A188V	 1	ZBTB7C	 Zinc	Finger	And	BTB	Domain	Containing	7C,	APM1,	Zinc	Finger	And	BTB	Domain-Containing	Protein	36	A1YPR0	 G598D	 0	ZMYM3	 Zinc	Finger	MYM-Type	Containing	3,	DXS6673E,	ZNF261	 Q14202	 Q608R	 1	ZNF841	 Zinc	Finger	Protein	841	 Q6ZN19	 T633A	 0	ZW10	 Zw10	Kinetochore	Protein,	ZW10	 O43264	 Q469R	 0															 	 	135	Appendix F: Top differentially expressed genes following irbesartan treatment (A) Top	100	up	regulated	genes	following	irbesartan	treatment	(Biopsy	2	vs	1).	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	IGHV3-23	 1.46	 16.17	 23.61	F13A1	 3.84	 12.66	 48.6144	CD163	 3.69	 9.63	 35.5347	FCN1	 1.01	 9.51	 9.6051	HIST1H2AM	 1.13	 9.46	 10.6898	HLA-DOA	 2.65	 9.11	 24.1415	HLA-DPB1	 37.57	 8.94	 335.8758	FOLR2	 4.57	 8.91	 40.7187	PLA1A	 1.34	 8.4	 11.256	CD14	 25.34	 8.37	 212.0958	ORC1	 12.08	 8.23	 99.4184	FGL2	 3.41	 8.05	 27.4505	MCU	 4.17	 7.92	 33.0264	HLA-DPA1	 36.26	 7.88	 285.7288	LYZ	 89.87	 7.86	 706.3782	MUC5AC	 26.39	 7.78	 205.3142	SLC52A2	 6.26	 7.7	 48.202	MPEG1	 1.96	 7.56	 14.8176	CYBB	 4.52	 7.49	 33.8548	C1QB	 65.31	 7.44	 485.9064	C1QA	 54.72	 7.38	 403.8336	HLA-DQA1	 10.75	 7.09	 76.2175	CPVL	 2.14	 7.06	 15.1084		 	 	136	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	HIST1H3B	 1.04	 7.05	 7.332	FPR3	 5.71	 6.92	 39.5132	CD86	 2.7	 6.84	 18.468	HLA-DQB2	 2.77	 6.79	 18.8083	HIST1H1E	 2.15	 6.74	 14.491	GPR183	 10.52	 6.69	 70.3788	GNB4	 0.76	 6.65	 5.054	AIF1	 5.33	 6.65	 35.4445	CD74	 336.87	 6.59	 2219.9733	HLA-DRA	 306.9	 6.52	 2000.988	HLA-DRB1	 206.44	 6.41	 1323.2804	CD37	 4.02	 6.39	 25.6878	C5AR1	 8.76	 6.37	 55.8012	HLA-DMA	 14.6	 6.32	 92.272	TMEM176B	 8.11	 6.31	 51.1741	SEPP1	 11.54	 6.25	 72.125	HLA-DMB	 7.39	 6.22	 45.9658	CD4	 10.04	 6.17	 61.9468	HLA-DRB5	 103.7	 6.16	 638.792	VSIG4	 3.6	 5.96	 21.456	HLA-DQB1	 24.31	 5.93	 144.1583	HIST1H3J	 0.71	 5.91	 4.1961	MFSD12	 8.08	 5.9	 47.672	CCDC152	 16.19	 5.9	 95.521	CLEC10A	 0.93	 5.82	 5.4126	SRGN	 47.28	 5.76	 272.3328		 	 	137	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	C3AR1	 3.75	 5.72	 21.45	HLA-DQA2	 1.62	 5.72	 9.2664	CECR1	 6.34	 5.71	 36.2014	MS4A6A	 3.16	 5.69	 17.9804	MS4A4A	 3.21	 5.68	 18.2328	VCAM1	 3.02	 5.67	 17.1234	PTPRC	 2.32	 5.65	 13.108	OLR1	 1.11	 5.65	 6.2715	HIST1H4E	 0.15	 5.64	 0.846	C1QC	 86.92	 5.56	 483.2752	PSMC1P1	 6.43	 5.55	 35.6865	APOC1	 22.6	 5.49	 124.074	SLC50A1	 4.26	 5.49	 23.3874	CSF2RA	 0.79	 5.47	 4.3213	THEM6	 4.97	 5.45	 27.0865	AOAH	 1.66	 5.45	 9.047	HIST1H2BG	 0.44	 5.39	 2.3716	GPNMB	 21.66	 5.38	 116.5308	SDS	 6.45	 5.32	 34.314	CSF1R	 11.48	 5.31	 60.9588	LILRB4	 2.42	 5.3	 12.826	GPSM3	 4.76	 5.29	 25.1804	FCER1A	 0.7	 5.27	 3.689	SIGLEC1	 1.03	 5.22	 5.3766	PLTP	 20.23	 5.22	 105.6006	EVI2B	 7.99	 5.2	 41.548		 	 	138	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	HIST1H2BN	 0.68	 5.2	 3.536	TNFSF8	 0.38	 5.19	 1.9722	FPR1	 2.37	 5.18	 12.2766	EMILIN2	 2.18	 5.16	 11.2488	IL10RA	 2.13	 5.11	 10.8843	PLA2G2D	 0.22	 5.06	 1.1132	C3	 1.7	 5.05	 8.585	LY86	 1.81	 5.04	 9.1224	ESM1	 1.48	 4.99	 7.3852	NCF2	 4.16	 4.98	 20.7168	LAPTM5	 63.23	 4.97	 314.2531	TYROBP	 48.5	 4.95	 240.075	GADD45B	 12.07	 4.92	 59.3844	ITGB2	 9.21	 4.91	 45.2211	LGALS2	 2.1	 4.9	 10.29	ITGBL1	 6.37	 4.89	 31.1493	CD48	 2.47	 4.88	 12.0536	RNASE6	 14.82	 4.8	 71.136	NCKAP1L	 2.38	 4.79	 11.4002	HIST1H1D	 0.28	 4.75	 1.33	FCGRT	 16.38	 4.74	 77.6412	CST1	 9.93	 4.72	 46.87	CD53	 10.84	 4.69	 50.84	MS4A7	 2.38	 4.67	 11.11	ADAMDEC1	 2.40	 4.67	 11.20			 	 	139	(B)	 Top	100	down	regulated	genes	following	irbesartan	treatment	(Biopsy	2	vs	1).	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	SIX3	 2.23	 -2.11	 -4.7053	MSX2	 8.16	 -2.12	 -17.2992	IER5	 39.42	 -2.12	 -83.5704	IRX2	 8.89	 -2.12	 -18.8468	TMEM97	 39.52	 -2.13	 -84.1776	LBH	 41.4	 -2.13	 -88.182	C7orf55	 16.64	 -2.14	 -35.6096	TMEM201	 8.19	 -2.15	 -17.6085	NRARP	 82.45	 -2.15	 -177.2675	BTG2	 133.38	 -2.16	 -288.1008	FABP1	 9.36	 -2.16	 -20.2176	RRAD	 82.37	 -2.17	 -178.7429	HSD11B2	 47.69	 -2.17	 -103.4873	DPT	 6.26	 -2.18	 -13.6468	FOXA2	 53.98	 -2.2	 -118.756	FOXP4	 28.02	 -2.21	 -61.9242	HBD	 1.27	 -2.21	 -2.8067	PKP1	 9.73	 -2.22	 -21.6006	NPTX2	 2.19	 -2.22	 -4.8618	AHSP	 1.25	 -2.22	 -2.775	GIF	 8.84	 -2.23	 -19.7132	NXF3	 2.74	 -2.23	 -6.1102	PHOSPHO1	 1.82	 -2.23	 -4.0586	H2AFX	 86.09	 -2.23	 -191.9807		 	 	140	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	HSPH1	 28.55	 -2.24	 -63.952	IGFL1	 2.82	 -2.24	 -6.3168	PHGR1	 508.2	 -2.24	 -1138.368	FABP4	 5.74	 -2.25	 -12.915	NOS2	 3.13	 -2.26	 -7.0738	ARSE	 14.46	 -2.27	 -32.8242	PF4	 1.49	 -2.28	 -3.3972	JUND	 375.25	 -2.32	 -870.58	GPATCH4	 28.18	 -2.32	 -65.3776	MMP1	 5.54	 -2.32	 -12.8528	HCFC1	 23.91	 -2.34	 -55.9494	SSTR1	 11.39	 -2.38	 -27.1082	OTUD1	 16.32	 -2.38	 -38.8416	ALX1	 1.45	 -2.38	 -3.451	FGFBP1	 13.46	 -2.4	 -32.304	EIF5B	 87.32	 -2.4	 -209.568	PIGR	 7.24	 -2.4	 -17.376	RPL29	 744.44	 -2.41	 -1794.1004	NEDD9	 34.76	 -2.43	 -84.4668	LDLR	 27.13	 -2.43	 -65.9259	IBSP	 203.62	 -2.44	 -496.8328	TNFSF9	 34.23	 -2.45	 -83.8635	EGR3	 9.61	 -2.45	 -23.5445	PCK1	 7.17	 -2.48	 -17.7816	ZNF703	 86.37	 -2.49	 -215.0613	IER2	 122.76	 -2.5	 -306.9		 	 	141	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	ALDOC	 13.45	 -2.51	 -33.7595	BEST2	 15.33	 -2.53	 -38.7849	PHF14	 13.97	 -2.53	 -35.3441	EEF1A2	 2.69	 -2.56	 -6.8864	SOX9	 72.68	 -2.56	 -186.0608	WBP5	 44.37	 -2.58	 -114.4746	ZNF165	 9.7	 -2.58	 -25.026	WFDC2	 10.42	 -2.62	 -27.3004	C8orf4	 32.14	 -2.62	 -84.2068	ATF3	 120.4	 -2.64	 -317.856	PHLDA2	 146.74	 -2.65	 -388.861	CCL26	 30.28	 -2.75	 -83.27	TRIB1	 53.98	 -2.75	 -148.445	SYT13	 2.97	 -2.77	 -8.2269	ODAM	 8.81	 -2.78	 -24.4918	MUC5B	 88.68	 -2.79	 -247.4172	CA9	 93.62	 -2.82	 -264.0084	RHOB	 402.54	 -2.82	 -1135.1628	ACKR1	 3.15	 -2.82	 -8.883	RPS21	 914.68	 -2.83	 -2588.5444	ATP5EP2	 14.46	 -2.83	 -40.9218	FOSB	 125.79	 -2.85	 -358.5015	SOX4	 23.43	 -2.92	 -68.4156	MMP7	 3.04	 -2.92	 -8.8768	MANEAL	 17.77	 -2.92	 -51.8884	PRB2	 7.47	 -2.93	 -21.8871		 	 	142	Gene	 Biopsy	1	RPKM	 Fold	change	 Biopsy	2	RPKM	CLU	 24.9	 -3.01	 -74.949	OSR2	 7.46	 -3.03	 -22.6038	Neutral	 -1.05	 -3.15	 3.3075	KLF4	 162.57	 -3.21	 -521.8497	FOS	 516.74	 -3.25	 -1679.405	JUN	 383.25	 -3.4	 -1303.05	MT1G	 43.63	 -3.47	 -151.3961	RASD1	 155.6	 -3.48	 -541.488	CLDN2	 51.04	 -3.53	 -180.1712	KLF2	 140.01	 -3.56	 -498.4356	EDN2	 12.02	 -3.64	 -43.7528	KLK7	 39.96	 -3.8	 -151.848	SPRR1A	 9.42	 -3.95	 -37.209	EGR1	 327.85	 -3.96	 -1298.286	DNAJB1	 320.82	 -3.99	 -1280.0718	OLFM4	 13.23	 -4.06	 -53.7138	HSPA1B	 161.17	 -5.43	 -875.1531	ARC	 11.84	 -5.7	 -67.488	CHAD	 12.73	 -6.05	 -77.0165	IFITM5	 11.37	 -10.52	 -119.6124	CCL21	 10.5	 -10.93	 -114.765	HBA2	 334.09	 -51.58	 -17232.3622	HBA1	 204.85	 -69.85	 -14308.7725	HBB	 546.55	 -83.49	 -45631.4595				 	 	143	Appendix G: 100 peptides tested using an ELISPOT The	100	peptides	that	were	tested	using	an	ELISPOT	assay.	From	left	to	right	the	columns	are:	Gene	name,	the	amino	acid	mutation,	the	HLA	the	peptide	is	predicted	to	bind	to,	the	peptide	sequence,	mass	spectrometry	data	expression	for	the	first	biopsy,	fold	change	in	the	mass	spectrometry	(second	biopsy	vs	first),	RNA-Seq	expression	for	the	first	biopsy,	fold	change	in	RNA-Seq	(second	biopsy	vs	first).	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	BAI1	 G1297R	 HLA-B*07:02	 SPRYPGRPL	 	 	 0.05	 -1.03	SBF2	 V10A	 HLA-A*02:01	 RLADYFIAV	 	 	 2.96	 1.46	MLC1	 A113V	 HLA-A*02:01	 ILDEVPFPV	 	 	 1.45	 1.6	UBR5	 R284H	 HLA-B*07:02	 YPSFHRSSL	 5.36	 -0.02	 5.72	 1.37	TCTEX1D1	 A169T	 HLA-A*02:01	 FTLANVYAV	 	 	 0.07	 1.03	MYH7	 P132L	 HLA-A*02:01	 WLLVYTPEV	 4.3	 -1.27	 0.01	 1.01	UGGT2	 A1325V	 HLA-A*02:01	 FLDVLFPLV	 3.46	 -0.44	 2.25	 1.21	GPR158	 R601W	 HLA-A*02:01	 YLCYAVWTV	 	 	 0.04	 -1.04	ZNRF3	 L26F	 HLA-B*07:02	 RPRGFRCSRL	 	 	 4.33	 -1.32	PLP2	 I58V	 HLA- MVLAAIFFV	 5.39	 0.04	 232.46	 -1.63		 	 	144	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	A*02:01	AATK	 G688R	 HLA-B*07:02	 RPRLPLPSV	 	 	 1.44	 1.1	CACNA1G	 R1982H	 HLA-B*07:02	 RPLRHQAAI	 	 	 0.04	 1.02	BAI1	 G1297R	 HLA-B*07:02	 GSPRYPGRPL	 	 	 0.05	 -1.03	INPP5E	 T440I	 HLA-A*02:01	 RLLDYIRTV	 	 	 3.29	 -1.2	SH3D19	 P562T	 HLA-A*02:01	 FTLNFVEPV	 4.08	 -0.3	 17.24	 1.04	P2RX2	 I52T	 HLA-A*02:01	 QLLTLLYFV	 	 	 0.01	 -1.01	GBA2	 A488V	 HLA-A*02:01	 ALFNELYFLV	 	 	 18.49	 1.25	OR51B2	 M274I	 HLA-A*02:01	 IIISYIYFL	 	 	 0	 0	GIT2	 E455K	 HLA-B*07:02	 RPKESRMRL	 4.4	 -0.33	 3.94	 1.67	JARID2	 R434Q	 HLA-A*02:01	 KLNDEMQFV	 4.62	 0.43	 5.48	 1.16	ZSCAN31	 R232Q	 HLA-A*03:01	 KLFSKQTLLK	 	 	 1.05	 -1.16	ARL11	 V115I	 HLA-A*02:01	 NMAGIPFLV	 	 	 1.91	 2.05	ACTL7A	 T98M	 HLA-B*07:02	 RPTHKISTM	 	 	 0	 0		 	 	145	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	HS3ST6	 R280H	 HLA-B*07:02	 RPHPHVPQAL	 	 	 0.06	 -1.01	LAMA5	 R2582M	 HLA-A*02:01	 QMLGLVWAA	 4.76	 -0.46	 6.62	 -1.04	TACR2	 A42V	 HLA-A*02:01	 ALWATAYLV	 	 	 0.54	 1.08	P2RX2	 I52T	 HLA-A*02:01	 TLLYFVWYV	 	 	 0.01	 -1.01	HMGXB3	 V13M	 HLA-A*02:01	 VMMEEIEEA	 	 	 11.77	 -1.01	SBF2	 V10A	 HLA-A*02:01	 ARLADYFIAV	 	 	 2.96	 1.46	ATXN2	 P1026L	 HLA-B*07:02	 HPASAAGPL	 5.31	 -0.03	 3.32	 -1.28	PCDHB6	 R392H	 HLA-A*02:01	 ALQSFEFHV	 	 	 0.18	 1.08	CBL	 L643M	 HLA-B*07:02	 VPRLGSTFSM	 	 	 4.69	 1.94	TNK2	 A116V	 HLA-A*02:01	 GLPRGLWLV	 	 	 4.39	 1.04	HSD11B1L	 V62I	 HLA-B*07:02	 RPRAHVILHSL	 	 	 0.9	 -1.03	CAMKV	 A357T	 HLA-B*07:02	 SPQPLTGTL	 	 	 0.05	 -1.03	SLC17A2	 I361T	 HLA-A*02:01	 LLLPSTCAV	 	 	 0	 0	CD6	 T8A	 HLA- WLFFGIAGL	 	 	 2.79	 1.9		 	 	146	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	A*02:01	PTGES2	 P139L	 HLA-B*07:02	 RPFMGGQKL	 	 	 33.45	 1.01	GPR98	 D3862G	 HLA-A*02:01	 ILPDGLPEL	 	 	 0.01	 1	BMP5	 T132M	 HLA-B*07:02	 YPRRIQLSRM	 	 	 0.11	 1.09	TET2	 A1779V	 HLA-A*02:01	 GMFNSSLHV	 5.59	 0.42	 4.39	 2.52	SEC16A	 P1152L	 HLA-B*07:02	 VPALAPGPL	 4.83	 -0.95	 11.04	 -1.54	TBXA2R	 A223V	 HLA-B*07:02	 APPVPFRGAL	 	 	 0.67	 -1	ERVW-1	 P265H	 HLA-A*02:01	 CLHSGIFFV	 	 	 0.02	 -1	RTN4RL1	 G147S	 HLA-B*07:02	 LPAGVFGSL	 	 	 0.72	 -1.23	PCDHA10	 A455V	 HLA-A*02:01	 VQSEYTVFV	 	 	 0.2	 -1.31	DECR2	 A241T	 HLA-A*02:01	 YLTSPLASYV	 	 	 6.56	 -1.25	SEPT1	 R121Q	 HLA-B*07:02	 SPFGRGLQPL	 	 	 2.39	 1.47	CBL	 L643M	 HLA-C*15:04	 FSMDTSMSM	 	 	 4.69	 1.94	PCDHA10	 A455V	 HLA-A*02:01	 FVQSEYTVFV	 	 	 0.2	 -1.31		 	 	147	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	TELO2	 V695M	 HLA-A*02:01	 SMAGHFFFPL	 	 	 8.34	 -1.31	NLGN4X	 Q359R	 HLA-A*02:01	 ILMERGEFL	 	 	 0.28	 1.2	BAI1	 G1297R	 HLA-B*07:02	 HGSPRYPGRPL	 	 	 0.05	 -1.03	EHD1	 A502V	 HLA-A*02:01	 VLANHLIKV	 5.1	 0.31	 14.63	 -1.16	OBSCN	 A2376P	 HLA-B*07:02	 SPTLTIRAL	 5.8	 0.31	 0.25	 -1.07	ZNF837	 V331I	 HLA-B*07:02	 RGPILARRAF	 	 	 1.61	 -1.32	KIAA1432	 Y799C	 HLA-A*02:01	 ALWNCLFAA	 	 	 4.24	 1.8	ZNF837	 V331I	 HLA-B*07:02	 GPILARRAF	 	 	 1.61	 -1.32	SRRT	 A190V	 HLA-A*02:01	 QQMQDFFLV	 5.32	 0.18	 26.31	 -1.34	LIF	 G28R	 HLA-B*07:02	 WPQRDRLPAL	 	 	 10.27	 1.15	URGCP	 R762M	 HLA-A*03:01	 ILHAFLMLEK	 	 	 5.13	 -1.01	UBR5	 R284H	 HLA-B*07:02	 GYPSFHRSSL	 5.36	 -0.02	 5.72	 1.37	P2RY1	 R287L	 HLA-A*02:01	 ALLDFQTPA	 	 	 0.93	 1.21	HERC5	 P439S	 HLA- KLLDQMSSL	 	 	 2.53	 2.22		 	 	148	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	A*02:01	TRAP1	 R176C	 HLA-A*02:01	 ILPKWLCFI	 4.74	 0.007	 42.84	 -1.26	BAI1	 G1297R	 HLA-B*07:02	 SPRYPGRPLP	 	 	 0.05	 -1.03	MLC1	 A113V	 HLA-A*02:01	 NILDEVPFPV	 	 	 1.45	 1.6	GRIN2D	 R1241H	 HLA-B*07:02	 RPRASHHTPA	 	 	 7.42	 -1.74	GBA2	 A488V	 HLA-A*02:01	 FLVDGGTVWL	 	 	 18.49	 1.25	TDRD6	 S13L	 HLA-B*07:02	 MPAPGALLAL	 6.43	 0.19	 0.54	 1.39	PLP2	 I58V	 HLA-A*02:01	 VLAAIFFVV	 5.39	 0.04	 232.46	 -1.63	ZFHX3	 S1565L	 HLA-B*07:02	 LPQLVSLPL	 3.59	 -0.16	 2.5	 -1.09	CLSTN3	 V218M	 HLA-A*03:01	 RLYKFTMTAY	 	 	 3.81	 1.52	OR52N5	 C83Y	 HLA-A*02:01	 SLIDLLTYT	 	 	 0	 -1	KIAA1432	 Y799C	 HLA-A*02:01	 ALWNCLFAAV	 	 	 4.24	 1.8	PDE4DIP	 A1057V	 HLA-A*02:01	 MLSLCLENV	 	 	 5.22	 1.65	MDN1	 R3141W	 HLA-A*02:01	 VLFWHLAGL	 4.52	 0.4	 3.9	 1.18		 	 	149	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	EHD1	 A502V	 HLA-A*02:01	 GLLDDEEFV	 5.1	 0.31	 14.63	 -1.16	FZD8	 A445T	 HLA-A*02:01	 HLATWLVPSV	 	 	 3.2	 1.35	MLLT11	 I19V	 HLA-A*02:01	 FLFWRMPV	 4.1	 0.61	 1.82	 1.47	MTOR	 R1640W	 HLA-A*02:01	 ILMVWSLVV	 4.25	 -0.2	 8.31	 1.11	USP14	 T26M	 HLA-A*03:01	 KVMVKGGMLK	 4.94	 -0.14	 15.92	 -1.01	WWP1	 F398V	 HLA-A*02:01	 GLAREWVFLL	 	 	 7.9	 1.61	ZBTB6	 V35I	 HLA-A*02:01	 NLFCDISIYI	 	 	 3.67	 1.06	ERCC6	 V709I	 HLA-A*02:01	 FMEQFSIPI	 	 	 1.71	 1.43	PRAM1	 R310W	 HLA-B*07:02	 RPWPAEFKAL	 	 	 2.01	 1.93	TTC7A	 Q127H	 HLA-B*07:02	 KPLYHMRLL	 	 	 9.35	 1.03	FAM83E	 S351N	 HLA-A*02:01	 ALNDILRSV	 4.8	 -0.16	 6.45	 -1.17	SLC25A38	 C136R	 HLA-A*03:01	 RMSPITVIK	 	 	 16.52	 -1.29	MDFIC	 V89M	 HLA-B*07:02	 HPAPHSPSSM	 	 	 6.62	 2	EHD1	 A502V	 HLA- FVLANHLIKV	 5.1	 0.31	 14.63	 -1.16		 	 	150	Gene	 Mutation	 HLA	 Peptide	 Mass	spectrometry	(MS)	MS	log	fold	change	RNA-Seq	expression	RNA-Seq	fold	change	A*02:01	DLEC1	 F326L	 HLA-A*03:01	 RMESRNHLLK	 	 	 2.85	 1.11	NYNRIN	 R757H	 HLA-B*07:02	 APRQPPHHL	 	 	 6	 1.3	TBX15	 P254H	 HLA-B*07:02	 FPTSHRLAA	 	 	 1.2	 1.27	TDRD6	 S13L	 HLA-B*07:02	 TPGMPAPGAL	 6.42	 0.19	 0.54	 1.39	SETD1B	 S907L	 HLA-A*02:01	 RMAKALLTPV	 	 	 4.45	 -1.06	C22orf34	 H163Y	 HLA-A*02:01	 YSLEAIALV	 	 	 0.32	 1.06	TMEM132A	 A860V	 HLA-A*02:01	 ALLGVFCVV	 	 	 2.54	 -1.3	KNCN	 V107M	 HLA-A*03:01	 STMSRTLEK	 	 	 0.01	 1	GAS2L3	 S277F	 HLA-B*07:02	 SPAASFHPKL	 	 	 1.84	 1.18									 	 	151	Appendix H: Candidate neoantigens Predicted	candidate	neoantigens	(n=502)	as	described	in	the	methods.	HLA	–	the	patient	specific	allele	that	the	peptide	binding	affinity	is	for.	The	last	two	columns	refer	to	the	predicted	binding	affinity	of	the	mutant	and	wild	type	(WT)	peptides	respectively.		ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000313046	 G1297R	 HLA-B*07:02	 SPRYPGRPL	 7	 2.2	 2.2	ENSP00000427819	 R284H	 HLA-B*07:02	 YPSFHRSSL	 5	 2.7	 2.8	ENSP00000327540	 M274I	 HLA-A*02:01	 IIISYIYFL	 3	 4.9	 2.8	ENSP00000256190	 V10A	 HLA-A*02:01	 RLADYFIAV	 8	 2.3	 3.5	ENSP00000365500	 I58V	 HLA-A*02:01	 MVLAAIFFV	 2	 4.1	 3.5	ENSP00000255612	 R310W	 HLA-B*07:02	 RPWPAEFKAL	 3	 11.6	 3.7	ENSP00000443824	 L26F	 HLA-B*07:02	 RPRGFRCSRL	 5	 3.8	 3.9	ENSP00000398823	 Y799C	 HLA-A*02:01	 ALWNCLFAA	 5	 8.7	 4	ENSP00000350718	 R158Q	 HLA-B*07:02	 QPSIKRGASL	 1	 33.2	 4	ENSP00000422407	 R1982H	 HLA-B*07:02	 RPLRHQAAI	 5	 4.3	 4.1	ENSP00000402937	 R232Q	 HLA-A*03:01	 KLFSKQTLLK	 6	 5	 4.1	ENSP00000417614	 A357T	 HLA-B*07:02	 SPQPLTGTL	 8	 7	 4.4	ENSP00000313046	 G1297R	 HLA-B*07:02	 GSPRYPGRPL	 8	 4.4	 4.5	ENSP00000468145	 A223V	 HLA-B*07:02	 APPVPFRGAL	 4	 7.7	 4.8	ENSP00000265425	 I361T	 HLA-A*02:01	 LLLPSTCAV	 6	 7.2	 4.9	ENSP00000424129	 P439S	 HLA-A*02:01	 KLLDQMSSL	 7	 9.5	 5.1	ENSP00000405531	 I52T	 HLA-A*02:01	 TLLYFVWYV	 1	 5.7	 5.2	ENSP00000390354	 R280H	 HLA-B*07:02	 RPHPHVPQAL	 5	 5.4	 5.4	ENSP00000264033	 L643M	 HLA-B*07:02	 VPRLGSTFSM	 10	 6.6	 5.4		 	 	152	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000282670	 A169T	 HLA-A*02:01	 FTLANVYAV	 2	 3.1	 5.6	ENSP00000400374	 A241T	 HLA-A*02:01	 YLTSPLASYV	 3	 8	 5.6	ENSP00000419945	 P265H	 HLA-A*02:01	 CLHSGIFFV	 3	 7.8	 5.8	ENSP00000275857	 Q359R	 HLA-A*02:01	 ILMERGEFL	 5	 8.3	 5.8	ENSP00000347507	 P132L	 HLA-A*02:01	 WLLVYTPEV	 3	 3.3	 5.9	ENSP00000341988	 R704H	 HLA-B*07:02	 LPHRIHESL	 3	 16.4	 5.9	ENSP00000440055	 T8A	 HLA-A*02:01	 WLFFGIAGL	 7	 7.2	 6.1	ENSP00000282026	 V115I	 HLA-A*02:01	 NMAGIPFLV	 5	 5.4	 6.2	ENSP00000398823	 Y799C	 HLA-A*02:01	 ALWNCLFAAV	 5	 10	 6.2	ENSP00000251287	 R744Q	 HLA-B*07:02	 QPLVGPLAL	 1	 42.8	 6.2	ENSP00000421030	 A455V	 HLA-A*02:01	 VQSEYTVFV	 1	 7.9	 6.5	ENSP00000456512	 R121Q	 HLA-B*07:02	 SPFGRGLQPL	 8	 8.1	 6.6	ENSP00000463187	 V62I	 HLA-B*07:02	 RPRAHVILHSL	 7	 6.7	 6.7	ENSP00000436000	 R153H	 HLA-B*07:02	 APHMTGSLV	 3	 23.4	 6.8	ENSP00000360777	 T440I	 HLA-A*02:01	 RLLDYIRTV	 6	 4.4	 7	ENSP00000320516	 A502V	 HLA-A*02:01	 VLANHLIKV	 1	 8.4	 7	ENSP00000382745	 A37T	 HLA-B*07:02	 RPARRCYTV	 8	 15.1	 7.1	ENSP00000457715	 A181T	 HLA-B*07:02	 APTGAPGPL	 3	 14	 7.2	ENSP00000365500	 I58V	 HLA-A*02:01	 VLAAIFFVV	 1	 9.9	 7.3	ENSP00000295453	 A478V	 HLA-A*02:01	 FIVHVMAFA	 3	 23.3	 7.4	ENSP00000389242	 R161W	 HLA-B*07:02	 RPKKCFWNL	 7	 28.4	 7.5	ENSP00000263269	 R1241H	 HLA-B*07:02	 RPRASHHTPA	 7	 9.8	 7.6	ENSP00000438910	 A113V	 HLA-A*02:01	 ILDEVPFPV	 9	 2.3	 7.7	ENSP00000398823	 Y799C	 HLA-A*02:01	 YALWNCLFAA	 6	 16.1	 7.7		 	 	153	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000367334	 A488V	 HLA-A*02:01	 FLVDGGTVWL	 3	 9.8	 7.8	ENSP00000374273	 S192G	 HLA-A*02:01	 YLGDQLVLDV	 3	 13.8	 7.9	ENSP00000278426	 R409H	 HLA-B*07:02	 RPHYCKIQKL	 3	 21.2	 8	ENSP00000443299	 S13L	 HLA-B*07:02	 MPAPGALLAL	 7	 9.8	 8.1	ENSP00000299192	 A94V	 HLA-A*02:01	 LLLDPSLVV	 8	 16.4	 8.4	ENSP00000369344	 R263W	 HLA-B*07:02	 WPQAPAAAA	 1	 41	 8.4	ENSP00000313046	 G1297R	 HLA-B*07:02	 HGSPRYPGRPL	 9	 8.3	 8.7	ENSP00000322706	 A176V	 HLA-B*07:02	 RPTGGVGVV	 8	 15.5	 8.8	ENSP00000362403	 A42V	 HLA-A*02:01	 YLVLVLVAV	 3	 33.3	 8.8	ENSP00000263266	 S351N	 HLA-A*02:01	 ALNDILRSV	 3	 12	 8.9	ENSP00000448915	 S48L	 HLA-B*07:02	 LPRLECNGAI	 1	 14.8	 8.9	ENSP00000365938	 A1325V	 HLA-A*02:01	 FLDVLFPLVV	 9	 13.1	 9	ENSP00000361202	 A441T	 HLA-B*07:02	 SPTRPRHPA	 3	 19.7	 9.2	ENSP00000363826	 A445T	 HLA-A*02:01	 HLATWLVPSV	 4	 10.4	 9.3	ENSP00000384582	 D3862G	 HLA-A*02:01	 ILPDGLPEL	 5	 7.5	 9.4	ENSP00000313046	 G1297R	 HLA-B*07:02	 SPRYPGRPLP	 7	 9.7	 9.4	ENSP00000405699	 V331I	 HLA-B*07:02	 RGPILARRAF	 4	 8.4	 9.6	ENSP00000417614	 A357T	 HLA-B*07:02	 SPQPLTGTLL	 8	 18.8	 9.7	ENSP00000409537	 A122V	 HLA-B*07:02	 RPNGRVVL	 7	 23.3	 9.8	ENSP00000405699	 V331I	 HLA-B*07:02	 GPILARRAF	 3	 8.8	 10	ENSP00000463187	 V62I	 HLA-B*07:02	 RPRAHVIL	 7	 13.4	 10	ENSP00000373933	 R228Q	 HLA-A*02:01	 QLQPLLPPI	 1	 20.6	 10	ENSP00000436000	 R153H	 HLA-B*07:02	 APHMTGSL	 3	 34.7	 10	ENSP00000365175	 R1755Q	 HLA-B*07:02	 RPSHQPGPPV	 5	 14.5	 10.2		 	 	154	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000421030	 A455V	 HLA-A*02:01	 FVQSEYTVFV	 2	 8.2	 10.6	ENSP00000313158	 R475H	 HLA-A*03:01	 HAYQCHLCYK	 1	 23.6	 10.6	ENSP00000450973	 S381N	 HLA-B*07:02	 NPAHLAREL	 1	 47.4	 10.6	ENSP00000300231	 R2442Q	 HLA-B*07:02	 TEPQPHRGEL	 4	 21.5	 10.8	ENSP00000319135	 R90Q	 HLA-A*02:01	 QLWQADVPV	 1	 17	 10.9	ENSP00000342665	 F398V	 HLA-A*02:01	 GLAREWVFLL	 7	 11	 11	ENSP00000261381	 A329T	 HLA-B*07:02	 MPANPVRITF	 9	 20.1	 11.1	ENSP00000434050	 A172V	 HLA-A*02:01	 RLNVGQWFL	 4	 26.8	 11.4	ENSP00000365938	 A1325V	 HLA-A*02:01	 FLDVLFPLV	 9	 3.4	 11.5	ENSP00000443203	 A203T	 HLA-A*02:01	 TLIGCPPLV	 1	 16.2	 11.5	ENSP00000427819	 R284H	 HLA-B*07:02	 GYPSFHRSSL	 6	 9.4	 11.6	ENSP00000192314	 R89H	 HLA-B*07:02	 LPAGSHVHL	 6	 31.4	 11.8	ENSP00000400378	 S85R	 HLA-A*02:01	 KTFWMRYWV	 6	 40	 11.8	ENSP00000313731	 Y222H	 HLA-A*03:01	 NDMHAYLLFK	 4	 28.3	 11.9	ENSP00000340938	 E455K	 HLA-B*07:02	 RPKESRMRL	 3	 4.9	 12.1	ENSP00000345060	 T462I	 HLA-A*03:01	 IVYGIIILK	 5	 16.5	 12.1	ENSP00000256190	 V10A	 HLA-A*02:01	 ARLADYFIAV	 9	 6.1	 12.4	ENSP00000438466	 R392H	 HLA-A*02:01	 ALQSFEFHV	 8	 6.5	 12.4	ENSP00000313731	 Y222H	 HLA-A*03:01	 MNDMHAYLLFK	 5	 19.4	 12.4	ENSP00000219271	 Y281C	 HLA-A*03:01	 IMAPFCQWK	 6	 26.9	 12.5	ENSP00000348089	 V709I	 HLA-A*02:01	 FMEQFSIPI	 7	 11.5	 12.6	ENSP00000320516	 A502V	 HLA-A*02:01	 FVLANHLIKV	 2	 12.1	 12.6	ENSP00000365500	 I58V	 HLA-A*02:01	 MVLAAIFFVV	 2	 15.5	 12.7		 	 	155	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000320516	 A502V	 HLA-A*02:01	 LLDDEEFVL	 8	 24.2	 12.7	ENSP00000362763	 V35I	 HLA-A*02:01	 NLFCDISIYI	 6	 11	 12.9	ENSP00000407980	 S184G	 HLA-A*02:01	 MLPRFGTFI	 6	 46.7	 13	ENSP00000371994	 R757H	 HLA-B*07:02	 APRQPPHHL	 7	 12.3	 13.1	ENSP00000367334	 A488V	 HLA-A*02:01	 ALFNELYFLV	 10	 4.8	 13.2	ENSP00000450973	 S381N	 HLA-B*07:02	 RNPAHLAREL	 2	 13.2	 13.2	ENSP00000431620	 R744H	 HLA-B*07:02	 LPAELRAHL	 8	 16.4	 13.2	ENSP00000364555	 R164C	 HLA-B*07:02	 QPFHQGCPL	 7	 28.7	 13.5	ENSP00000326575	 A101T	 HLA-A*03:01	 KIKDTYHMLK	 5	 15.5	 13.6	ENSP00000078527	 A253V	 HLA-A*02:01	 FTLGLPFVL	 8	 26	 13.6	ENSP00000223341	 R762M	 HLA-A*03:01	 ILHAFLMLEK	 7	 9.3	 13.8	ENSP00000383896	 D14N	 HLA-A*02:01	 ILNWQPPEV	 3	 20	 14.2	ENSP00000468145	 A223V	 HLA-B*07:02	 SAPPVPFRGAL	 5	 27	 14.2	ENSP00000384450	 G28R	 HLA-B*07:02	 WPQRDRLPAL	 6	 9.1	 14.4	ENSP00000386300	 L315P	 HLA-B*07:02	 RPVSRPGDQM	 6	 25.7	 14.7	ENSP00000450741	 R568C	 HLA-A*03:01	 KTWCFSNMK	 4	 19.6	 14.8	ENSP00000380478	 R434Q	 HLA-A*02:01	 KLNDEMQFV	 7	 4.9	 14.9	ENSP00000398796	 G688R	 HLA-B*07:02	 RPRLPLPSV	 1	 4.3	 15.1	ENSP00000417614	 A357T	 HLA-B*07:02	 VSPQPLTGTL	 9	 36.8	 15.1	ENSP00000343026	 R652Q	 HLA-A*02:01	 RQWRQSFLV	 5	 15.4	 15.3	ENSP00000312066	 Y137C	 HLA-A*03:01	 RAWSCAMQLK	 5	 20.6	 15.3	ENSP00000464436	 V272I	 HLA-A*03:01	 RIYSHLKSVLK	 2	 13.1	 15.4	ENSP00000375413	 L23F	 HLA-A*03:01	 MMYHTFSFTR	 8	 19.5	 15.6	ENSP00000383333	 T364A	 HLA-C*15:04	 YANEYRLTI	 2	 24.8	 15.6		 	 	156	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000434640	 A146T	 HLA-B*07:02	 VPSWPTTPV	 7	 16.6	 15.7	ENSP00000267584	 S153L	 HLA-A*02:01	 KLFILLLTV	 7	 23.5	 15.8	ENSP00000398625	 P254H	 HLA-B*07:02	 FPTSHRLAA	 5	 12.4	 15.9	ENSP00000219271	 Y281C	 HLA-A*03:01	 AIMAPFCQWK	 7	 13.8	 16.2	ENSP00000442406	 S277F	 HLA-B*07:02	 SPAASFHPKL	 6	 13	 16.4	ENSP00000327440	 T261P	 HLA-B*07:02	 SPIRPPRAV	 5	 13.8	 16.4	ENSP00000439036	 R156H	 HLA-B*07:02	 LPRHAALHKL	 8	 16.9	 16.4	ENSP00000402937	 R232Q	 HLA-A*03:01	 SKLFSKQTLLK	 7	 23.6	 16.5	ENSP00000364555	 R164C	 HLA-B*07:02	 RVQPFHQGCPL	 9	 28.7	 16.5	ENSP00000243077	 G600S	 HLA-A*02:01	 ILKDSIHNV	 5	 20.5	 16.6	ENSP00000344223	 S441N	 HLA-A*03:01	 RELVNPANMK	 8	 32.3	 16.7	ENSP00000403793	 S182G	 HLA-A*02:01	 VLIGVLQAI	 4	 16.1	 17	ENSP00000444521	 R132C	 HLA-A*03:01	 KLIKCQRLAK	 5	 19.7	 17.1	ENSP00000409537	 A122V	 HLA-B*07:02	 RPNGRVVLRTL	 7	 23.9	 17.1	ENSP00000217188	 P448L	 HLA-B*07:02	 RLPRLAACPA	 5	 17.5	 17.2	ENSP00000402343	 T644M	 HLA-B*07:02	 HPQEMGSPF	 5	 16.7	 17.7	ENSP00000462315	 T26M	 HLA-A*03:01	 KVMVKGGMLK	 8	 10.6	 18.1	ENSP00000308597	 F326L	 HLA-A*03:01	 RMESRNHLLK	 8	 12.3	 18.1	ENSP00000310094	 L1004P	 HLA-A*02:01	 YPLLCTALHL	 2	 24.1	 18.1	ENSP00000439955	 R311H	 HLA-A*02:01	 HLDADGFLYI	 1	 26.3	 18.4	ENSP00000217188	 P448L	 HLA-B*07:02	 LPRLAACPA	 4	 17.9	 18.5	ENSP00000363509	 R641W	 HLA-A*03:01	 KLNQEIWMMK	 7	 22.7	 18.5	ENSP00000267197	 S907L	 HLA-A*02:01	 RMAKALLTPV	 6	 12.7	 18.6	ENSP00000400582	 G51S	 HLA-A*03:01	 VMFYSRKIMRK	 5	 17.5	 18.7		 	 	157	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000262238	 P138L	 HLA-A*02:01	 ILIPVPALA	 8	 31.9	 18.8	ENSP00000264033	 L643M	 HLA-C*15:04	 FSMDTSMSM	 3	 8.2	 19.1	ENSP00000345060	 T462I	 HLA-A*03:01	 AIVYGIIILK	 6	 25	 19.1	ENSP00000362403	 A42V	 HLA-A*02:01	 ALWATAYLV	 9	 5.6	 19.2	ENSP00000365500	 I58V	 HLA-A*02:01	 EMVLAAIFFV	 3	 24.7	 19.2	ENSP00000266546	 V218M	 HLA-A*03:01	 RLYKFTMTAY	 7	 10	 19.3	ENSP00000306496	 T194N	 HLA-A*03:01	 AIFMNLAHK	 5	 24.2	 19.3	ENSP00000297268	 R282H	 HLA-B*07:02	 GPAGPHGEV	 6	 47.1	 19.3	ENSP00000386997	 R502C	 HLA-A*02:01	 ALLETVNCL	 8	 22.2	 19.5	ENSP00000405290	 P490L	 HLA-B*07:02	 HPATLRPEF	 5	 24.6	 19.5	ENSP00000282670	 A169T	 HLA-C*07:02	 FRNSSLFTL	 8	 17.7	 19.7	ENSP00000419788	 Y425H	 HLA-A*02:01	 LQIFILHTV	 7	 25.2	 19.7	ENSP00000361196	 R177C	 HLA-A*03:01	 RSFECCMCGK	 6	 29	 19.7	ENSP00000241125	 A213T	 HLA-A*02:01	 FMLAVACTSL	 8	 19.2	 20.2	ENSP00000371786	 G196W	 HLA-B*07:02	 LPAMWSPL	 5	 26.5	 20.2	ENSP00000307156	 A1030T	 HLA-B*07:02	 KPGFHGQTA	 8	 46.5	 20.3	ENSP00000428333	 A579V	 HLA-B*07:02	 VPRSAEPGYL	 1	 31.8	 20.4	ENSP00000295113	 S129L	 HLA-C*07:02	 YRHLWPENL	 4	 23	 20.7	ENSP00000390790	 S54N	 HLA-A*02:01	 NLFNNLNYDV	 5	 31.5	 20.7	ENSP00000402937	 R232Q	 HLA-A*03:01	 KLFSKQTLLKK	 6	 20.6	 20.9	ENSP00000440055	 T8A	 HLA-A*02:01	 MWLFFGIAGL	 8	 25.5	 20.9	ENSP00000358400	 R3141W	 HLA-A*02:01	 VLFWHLAGL	 4	 10.3	 21	ENSP00000280562	 P186S	 HLA-A*03:01	 QVFSIMCAYK	 4	 21.8	 21	ENSP00000369351	 A1779V	 HLA-A*03:01	 SLHVLHLQNK	 4	 40.6	 21		 	 	158	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000265425	 I361T	 HLA-B*07:02	 LPSTCAVAL	 4	 17.2	 21.3	ENSP00000364555	 R164C	 HLA-B*07:02	 VQPFHQGCPL	 8	 45	 21.5	ENSP00000250113	 R203C	 HLA-C*07:02	 FRSLCTKLL	 5	 26.9	 21.6	ENSP00000282670	 A169T	 HLA-A*02:01	 TLANVYAVYL	 1	 36.5	 21.6	ENSP00000362576	 G447R	 HLA-A*02:01	 MLLCASIERI	 9	 26.9	 22.2	ENSP00000413953	 V325I	 HLA-A*02:01	 VLADILQDI	 5	 18.9	 22.3	ENSP00000429491	 A379T	 HLA-A*02:01	 TFLFDSTLTA	 1	 22.4	 22.4	ENSP00000434050	 A172V	 HLA-A*03:01	 RLNVGQWFLK	 4	 28	 22.4	ENSP00000357917	 I19V	 HLA-A*02:01	 FLFWRMPVPEL	 8	 28	 22.4	ENSP00000399145	 A309V	 HLA-B*07:02	 SPVSVMLVL	 5	 41.6	 22.4	ENSP00000448480	 A390T	 HLA-B*07:02	 MPSTQLCAAL	 4	 29.6	 22.5	ENSP00000363435	 A1343V	 HLA-A*03:01	 SLVHLLDMMK	 3	 48.5	 22.5	ENSP00000450458	 V4I	 HLA-A*02:01	 YIIGGITVSV	 2	 13.7	 22.7	ENSP00000467177	 A50T	 HLA-A*03:01	 RAMTRKIRMK	 4	 41.9	 22.7	ENSP00000343634	 R496H	 HLA-B*07:02	 LPQESRHVHL	 7	 31.5	 22.9	ENSP00000409180	 P137S	 HLA-B*07:02	 RPLTPDSESL	 9	 29.9	 23.1	ENSP00000419945	 P265H	 HLA-A*02:01	 VCLHSGIFFV	 4	 19.3	 23.2	ENSP00000398614	 A116V	 HLA-A*02:01	 GLPRGLWLV	 9	 6.7	 23.5	ENSP00000264449	 L934I	 HLA-A*02:01	 CLNGIFHSV	 5	 17.7	 23.5	ENSP00000439409	 A69T	 HLA-C*15:04	 LTFDPATLL	 7	 20.1	 23.6	ENSP00000269844	 V101A	 HLA-B*07:02	 RPPPPGGVA	 9	 42.4	 24	ENSP00000419399	 G145S	 HLA-A*02:01	 SLLTFQAPFL	 1	 20.3	 24.3	ENSP00000362411	 A482V	 HLA-A*02:01	 VLLQISIPFL	 1	 35.8	 24.7		 	 	159	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000365500	 I58V	 HLA-A*02:01	 VLAAIFFV	 1	 43.2	 24.8	ENSP00000265641	 S768G	 HLA-A*03:01	 TLFGLGSNSK	 6	 30.3	 25.2	ENSP00000464436	 V272I	 HLA-A*03:01	 ALRIYSHLK	 4	 25.6	 25.6	ENSP00000371786	 G196W	 HLA-B*07:02	 LPAMWSPLSPL	 5	 28.6	 25.6	ENSP00000386307	 Q127H	 HLA-B*07:02	 KPLYHMRLL	 5	 11.8	 25.7	ENSP00000220244	 A1166T	 HLA-A*02:01	 TLIPKNAGV	 1	 39	 25.7	ENSP00000320516	 A502V	 HLA-A*03:01	 VLANHLIKVK	 1	 23.7	 26	ENSP00000427819	 R284H	 HLA-B*07:02	 FGYPSFHRSSL	 7	 20.7	 26.2	ENSP00000241125	 A213T	 HLA-A*02:01	 MLAVACTSL	 7	 37.5	 26.2	ENSP00000443203	 A203T	 HLA-A*02:01	 KLSTAITLI	 7	 33.2	 26.5	ENSP00000436324	 V143A	 HLA-A*02:01	 FLIAAGAAAL	 8	 32.2	 26.8	ENSP00000386953	 R142C	 HLA-A*03:01	 KLCHHRMYR	 3	 45.7	 27.1	ENSP00000383029	 V136A	 HLA-A*03:01	 KIHLAVLGK	 5	 26	 27.3	ENSP00000438926	 S1565L	 HLA-B*07:02	 LPQLVSLPLL	 9	 29	 27.3	ENSP00000266546	 V218M	 HLA-A*02:01	 RLYKFTMTA	 7	 16.2	 28.1	ENSP00000388314	 G1771C	 HLA-A*02:01	 FLSHCCPGM	 5	 23.7	 28.1	ENSP00000427819	 R284H	 HLA-B*07:02	 YPSFHRSSLS	 5	 22.9	 28.4	ENSP00000405531	 I52T	 HLA-A*02:01	 QLLTLLYFV	 4	 4.5	 28.5	ENSP00000220244	 A1166T	 HLA-A*03:01	 RIKIKTLIPK	 6	 24.6	 28.7	ENSP00000454771	 R33Q	 HLA-A*03:01	 KTSPPQTAPK	 6	 30.2	 28.7	ENSP00000394244	 C136R	 HLA-A*03:01	 VRMSPITVIK	 2	 26.6	 28.8	ENSP00000369030	 S240N	 HLA-A*03:01	 LLRNNNCLSK	 6	 49.4	 28.9	ENSP00000263373	 A2205V	 HLA-B*07:02	 IPGRVEPVAL	 8	 48.3	 29.4	ENSP00000398823	 Y799C	 HLA-A*03:01	 CLFAAVGNPK	 1	 24.7	 29.5		 	 	160	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000344223	 S441N	 HLA-B*07:02	 NPANMKQAL	 4	 33.7	 29.6	ENSP00000344223	 S441N	 HLA-B*07:02	 VNPANMKQAL	 5	 34.1	 29.6	ENSP00000443824	 L26F	 HLA-B*07:02	 RRPRGFRCSRL	 6	 31.3	 29.8	ENSP00000078527	 A253V	 HLA-A*02:01	 VLFQYYAYT	 1	 39.2	 30.2	ENSP00000386627	 V3445I	 HLA-C*07:02	 FYIHHPIHL	 7	 31.6	 30.3	ENSP00000296220	 I370V	 HLA-A*03:01	 VLHLLSQLK	 1	 34.5	 30.4	ENSP00000440055	 T8A	 HLA-A*02:01	 WLFFGIAGLL	 7	 37.6	 30.4	ENSP00000304437	 G1253S	 HLA-B*07:02	 RILQASTPL	 6	 47.7	 30.5	ENSP00000330631	 G147S	 HLA-B*07:02	 LPAGVFGSL	 8	 7.9	 30.7	ENSP00000263269	 R1241H	 HLA-B*07:02	 RPRASHHTPAA	 7	 28	 30.7	ENSP00000387634	 P155L	 HLA-C*15:04	 LAYPPLPSY	 6	 48.7	 30.8	ENSP00000428148	 L105F	 HLA-B*07:02	 LPWFKPSAHL	 4	 30.6	 31	ENSP00000313046	 G1297R	 HLA-B*07:02	 PRYPGRPL	 6	 39.2	 31.6	ENSP00000256447	 W591R	 HLA-A*03:01	 SNIHFLTRYK	 8	 42.6	 31.8	ENSP00000392057	 P484H	 HLA-A*02:01	 MVLVFNHSV	 7	 29.4	 32.2	ENSP00000362403	 A42V	 HLA-A*02:01	 ALWATAYLVL	 9	 36.4	 32.2	ENSP00000366843	 P1026L	 HLA-B*07:02	 HPASAAGPLI	 9	 46.1	 32.2	ENSP00000394244	 C136R	 HLA-A*03:01	 RMSPITVIK	 1	 12	 32.4	ENSP00000257724	 V89M	 HLA-B*07:02	 HPAPHSPSSM	 10	 12.1	 32.4	ENSP00000288709	 L26V	 HLA-A*02:01	 LLLVVTAAL	 4	 29.3	 32.4	ENSP00000373700	 R51K	 HLA-A*03:01	 LSYSRLQKK	 8	 40.1	 32.5	ENSP00000434586	 L16359I	 HLA-A*03:01	 RSSVFISWSK	 6	 30.9	 32.7	ENSP00000436504	 R139H	 HLA-A*03:01	 AAYYHYIEK	 5	 26.5	 32.8	ENSP00000348996	 V233M	 HLA-B*07:02	 RPISKMIREPL	 6	 27.2	 32.9		 	 	161	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000311835	 P235S	 HLA-A*02:01	 CLSLYFSGV	 3	 36.9	 32.9	ENSP00000318900	 T347M	 HLA-B*07:02	 MPVPKSIPI	 1	 13.8	 33.1	ENSP00000255008	 V292I	 HLA-A*02:01	 SLDATINHV	 6	 22.8	 33.1	ENSP00000404379	 R2449C	 HLA-A*02:01	 RLYCCMALL	 5	 15.5	 33.7	ENSP00000365938	 A1325V	 HLA-A*03:01	 VLFPLVVDK	 6	 36.7	 33.7	ENSP00000438910	 A113V	 HLA-A*02:01	 ILDEVPFPVRV	 9	 33.7	 34	ENSP00000405823	 A860V	 HLA-A*02:01	 ALLGVFCVV	 9	 12.8	 34.3	ENSP00000361219	 D541G	 HLA-A*02:01	 KQVDLIGLV	 7	 44.5	 34.4	ENSP00000342434	 L130R	 HLA-A*03:01	 KMRKVKIVK	 3	 41	 34.5	ENSP00000457448	 T35S	 HLA-B*07:02	 RVPPNYGSAL	 8	 24.8	 34.6	ENSP00000446972	 V90A	 HLA-A*02:01	 AMLETYGHLL	 1	 27.2	 35.2	ENSP00000330432	 V460I	 HLA-B*07:02	 APNGMASSI	 9	 24.5	 35.4	ENSP00000415183	 L2241M	 HLA-C*15:04	 MSFSVRLPY	 1	 16.8	 35.5	ENSP00000380490	 A219S	 HLA-A*03:01	 RVTADISLSK	 9	 31.5	 36.2	ENSP00000441226	 P228T	 HLA-A*02:01	 ALCSTFFSI	 5	 24	 36.3	ENSP00000388062	 R121S	 HLA-A*02:01	 ILFSNSTSL	 8	 27.3	 36.3	ENSP00000416643	 D271G	 HLA-B*07:02	 RPGRSSQGGSL	 3	 37	 36.3	ENSP00000322706	 A176V	 HLA-B*07:02	 RPTGGVGVVAL	 8	 38.7	 36.8	ENSP00000399341	 L110I	 HLA-A*03:01	 ILSIFKMDAK	 1	 27.8	 37.6	ENSP00000439409	 A69T	 HLA-A*03:01	 TLLPGASPK	 1	 50	 37.6	ENSP00000385362	 D455N	 HLA-A*03:01	 KMTDNPMNNK	 5	 19.2	 37.7	ENSP00000387316	 R108H	 HLA-A*02:01	 SLFHEYLNET	 4	 37.7	 37.7	ENSP00000354558	 R1640W	 HLA-A*02:01	 ILMVWSLVV	 5	 10.6	 37.9		 	 	162	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000394449	 A101T	 HLA-C*15:04	 FTSDLLHLY	 2	 31.8	 38.2	ENSP00000296220	 I370V	 HLA-A*03:01	 VVLHLLSQLK	 2	 44.8	 38.3	ENSP00000319135	 R90Q	 HLA-A*02:01	 KQLWQADVPV	 2	 37.2	 38.8	ENSP00000355670	 R185W	 HLA-A*03:01	 VLTLWQLALK	 5	 38.1	 39.2	ENSP00000404416	 Y174C	 HLA-A*02:01	 WLCFETGTWV	 3	 49.7	 39.2	ENSP00000302578	 A270T	 HLA-A*02:01	 VLQELTTHL	 6	 18.9	 39.3	ENSP00000374135	 S577R	 HLA-C*07:02	 YFADTTRFL	 7	 31	 39.3	ENSP00000384392	 R131C	 HLA-A*02:01	 MLREYLECL	 8	 40.7	 39.3	ENSP00000282670	 A169T	 HLA-A*02:01	 SLFTLANVYAV	 4	 42.9	 39.3	ENSP00000357917	 I19V	 HLA-A*02:01	 FLFWRMPV	 8	 10.5	 39.5	ENSP00000422407	 R1982H	 HLA-B*07:02	 QRPLRHQAAI	 6	 40.1	 39.7	ENSP00000405531	 I52T	 HLA-A*02:01	 LTLLYFVWYV	 2	 40.6	 39.9	ENSP00000371036	 L194S	 HLA-A*03:01	 LLGLTGSLSK	 7	 38.2	 40	ENSP00000390354	 R280H	 HLA-B*07:02	 RPHPHVPQA	 5	 42.5	 40.1	ENSP00000462315	 T26M	 HLA-A*03:01	 VMVKGGMLK	 7	 18.9	 40.6	ENSP00000419892	 T104M	 HLA-A*03:01	 YVYKMQSEK	 5	 33.3	 40.9	ENSP00000347454	 A495V	 HLA-A*03:01	 VMAGVKLTDK	 1	 42.2	 41	ENSP00000292599	 P88T	 HLA-B*07:02	 APAATAPRL	 5	 49.7	 41	ENSP00000443299	 S13L	 HLA-A*02:01	 LLALRVSFV	 1	 27.3	 41.2	ENSP00000294117	 G14V	 HLA-A*03:01	 STMSIVQARK	 6	 30.5	 41.3	ENSP00000303316	 M2680I	 HLA-A*02:01	 FLYRSPETI	 9	 18.4	 41.4	ENSP00000419788	 Y425H	 HLA-A*02:01	 GLQIFILHTV	 8	 41.4	 41.4	ENSP00000261686	 M148T	 HLA-A*03:01	 AMRKTGVKK	 5	 47.7	 41.4	ENSP00000205890	 P776S	 HLA-B*07:02	 SPLASSQPSL	 6	 28.7	 41.7		 	 	163	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000400374	 A241T	 HLA-C*15:04	 LTSPLASYV	 2	 27.1	 42.1	ENSP00000267584	 S153L	 HLA-A*02:01	 LLLTVMTWA	 3	 30.1	 42.3	ENSP00000389524	 V113A	 HLA-A*03:01	 VLANVPQSPK	 3	 26.6	 42.7	ENSP00000405699	 V331I	 HLA-B*07:02	 RRGPILARRAF	 5	 34.7	 42.9	ENSP00000368796	 V17I	 HLA-A*03:01	 VIYLTDLVLK	 2	 36.3	 42.9	ENSP00000281928	 S907L	 HLA-A*03:01	 MVLTQLTEFK	 3	 39.5	 42.9	ENSP00000261686	 M148T	 HLA-A*03:01	 ALFAAAMRKT	 10	 43.4	 43.4	ENSP00000256190	 V10A	 HLA-A*02:01	 MARLADYFIAV	 10	 15.6	 43.7	ENSP00000450940	 V257A	 HLA-B*07:02	 LPATSRTRI	 3	 20.8	 43.9	ENSP00000343706	 A106P	 HLA-A*03:01	 LIFIMGNSPK	 9	 36.3	 44	ENSP00000449101	 P41L	 HLA-A*02:01	 RLWAALGVV	 6	 17	 44.3	ENSP00000256190	 V10A	 HLA-A*02:01	 RLADYFIAVG	 8	 18.5	 45.2	ENSP00000365938	 A1325V	 HLA-A*02:01	 ILFLDVLFPLV	 11	 39.5	 45.2	ENSP00000345182	 G64S	 HLA-C*15:04	 FSRDFSLLV	 6	 33.9	 45.5	ENSP00000358407	 G590D	 HLA-A*02:01	 LMCWGTDYHL	 7	 33	 45.8	ENSP00000262319	 V695M	 HLA-A*02:01	 SMAGHFFFPL	 2	 8.3	 46.3	ENSP00000078527	 A253V	 HLA-A*02:01	 FVLFQYYAYT	 2	 46.3	 46.3	ENSP00000405531	 I52T	 HLA-C*07:02	 YRAVQLLTL	 8	 26.1	 47.5	ENSP00000390505	 N379S	 HLA-B*07:02	 IPSFSESSSL	 5	 30.8	 47.5	ENSP00000264405	 T133A	 HLA-B*07:02	 LDPFGAGQPL	 6	 39.1	 47.7	ENSP00000406125	 R15H	 HLA-A*03:01	 KIHIFDLGRK	 3	 42.8	 49.1	ENSP00000439036	 R156H	 HLA-A*03:01	 GLPRHAALHK	 9	 45.6	 49.5	ENSP00000261637	 I1679M	 HLA-A*02:01	 KLGVSLLVMV	 9	 29.3	 49.8	ENSP00000298910	 N1506H	 HLA-A*03:01	 IIHESLNFK	 3	 43.8	 50.5		 	 	164	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000359285	 V571I	 HLA-C*15:04	 RAVEGIQYI	 6	 49	 50.6	ENSP00000400582	 G51S	 HLA-A*03:01	 MFYSRKIMRK	 4	 49.2	 51.1	ENSP00000347507	 P132L	 HLA-A*02:01	 KWLLVYTPEV	 4	 16.1	 51.3	ENSP00000419705	 V107M	 HLA-A*03:01	 STMSRTLEK	 3	 12.8	 51.5	ENSP00000388062	 R121S	 HLA-C*15:04	 FSNSTSLSF	 6	 31.9	 51.5	ENSP00000266070	 S1237L	 HLA-A*03:01	 KVATVPQLEK	 8	 34.9	 51.8	ENSP00000267499	 T251A	 HLA-A*02:01	 KQLGAYIILI	 5	 46.5	 52.3	ENSP00000267499	 T251A	 HLA-A*02:01	 QLGAYIILI	 4	 42.7	 52.6	ENSP00000252595	 A155P	 HLA-A*02:01	 GMEPALLNV	 4	 42.1	 52.7	ENSP00000355670	 R185W	 HLA-A*03:01	 LTLWQLALK	 4	 42.4	 53	ENSP00000263967	 V344M	 HLA-A*02:01	 ILCATYMNV	 7	 34.9	 53.3	ENSP00000389524	 V113A	 HLA-A*02:01	 CLFQVLANV	 7	 28.4	 53.4	ENSP00000265983	 G424S	 HLA-C*15:04	 CSANGPSLY	 7	 40.9	 54	ENSP00000387775	 R146Q	 HLA-A*02:01	 KLWDLYLQT	 8	 42.6	 54.1	ENSP00000263967	 V344M	 HLA-A*02:01	 KILCATYMNV	 8	 44.2	 54.6	ENSP00000365529	 R601W	 HLA-A*02:01	 YLCYAVWTV	 7	 3.6	 55.9	ENSP00000396081	 V510A	 HLA-B*07:02	 SPARKTTKI	 3	 30.5	 56	ENSP00000457448	 T35S	 HLA-B*07:02	 VPPNYGSAL	 7	 39.9	 56.7	ENSP00000434586	 R560M	 HLA-A*03:01	 KETMKTVVPK	 4	 21.6	 57.9	ENSP00000358696	 N1394S	 HLA-A*02:01	 LLADLIEKSL	 9	 40.5	 58.8	ENSP00000322802	 R80W	 HLA-A*02:01	 WLPLDCGLAL	 1	 50	 59	ENSP00000290607	 L1431F	 HLA-A*02:01	 SLWGIQRFI	 8	 30	 59.6	ENSP00000347507	 P132L	 HLA-A*02:01	 WLLVYTPEVV	 3	 15.3	 59.8	ENSP00000241125	 A213T	 HLA-A*02:01	 MLAVACTSLL	 7	 48.9	 60.2		 	 	165	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000439409	 A69T	 HLA-A*03:01	 ATLLPGASPK	 2	 34.1	 60.4	ENSP00000431586	 G378R	 HLA-C*07:02	 FRSRALPVL	 4	 49.8	 60.7	ENSP00000355512	 A130V	 HLA-A*02:01	 SLFGIVSIV	 9	 22.4	 61.8	ENSP00000405823	 A860V	 HLA-A*02:01	 YALLGVFCVV	 10	 26.9	 61.8	ENSP00000388634	 A232T	 HLA-C*15:04	 RTFSVSSSL	 2	 36	 62.5	ENSP00000397170	 A111T	 HLA-C*07:02	 SRPYTFLEF	 5	 48.7	 62.6	ENSP00000462150	 L127P	 HLA-A*03:01	 RIKPEMAMK	 4	 40.9	 63.2	ENSP00000323929	 R804H	 HLA-A*03:01	 VIHGEAFTLK	 3	 48.9	 64	ENSP00000294117	 G14V	 HLA-A*03:01	 TMSIVQARK	 5	 48.8	 64.9	ENSP00000413729	 V160I	 HLA-C*15:04	 ISTPRSSFY	 1	 46.6	 66.5	ENSP00000449515	 R60H	 HLA-C*15:04	 LSFVHSVTM	 5	 44.4	 67.1	ENSP00000322866	 C83Y	 HLA-A*02:01	 SLIDLLTYT	 8	 10	 68.1	ENSP00000244364	 V4577I	 HLA-A*02:01	 TMGDTILAI	 6	 46.5	 68.3	ENSP00000436324	 V143A	 HLA-A*02:01	 FLIAAGAAA	 8	 28.2	 68.8	ENSP00000384450	 G28R	 HLA-B*07:02	 WPQRDRLPA	 6	 30.4	 69	ENSP00000358360	 A1057V	 HLA-A*02:01	 MLSLCLENV	 9	 10.1	 69.2	ENSP00000255008	 V292I	 HLA-C*15:04	 ATINHVSLI	 3	 44	 70.1	ENSP00000282493	 T1142R	 HLA-B*07:02	 KPSGSQRVNL	 7	 34.9	 70.3	ENSP00000267502	 V176M	 HLA-A*03:01	 SSMAHHIGK	 3	 17	 71.8	ENSP00000450458	 V4I	 HLA-A*02:01	 IIGGITVSV	 1	 40.4	 73.9	ENSP00000345060	 T462I	 HLA-A*02:01	 IIILKLYRV	 1	 38.9	 75.3	ENSP00000350332	 G1013R	 HLA-A*03:01	 RLSDSPGVSK	 1	 19.6	 75.5	ENSP00000261772	 G495C	 HLA-A*02:01	 YHLDSSCSYV	 7	 24.6	 76.2	ENSP00000405788	 A171V	 HLA-A*03:01	 IVFTPQSTSK	 2	 17.5	 76.7		 	 	166	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000369351	 A1779V	 HLA-A*02:01	 GMFNSSLHV	 9	 7.6	 76.8	ENSP00000334300	 T98M	 HLA-B*07:02	 RPTHKISTM	 9	 5.4	 78.4	ENSP00000371786	 G196W	 HLA-A*02:01	 AMWSPLSPL	 3	 13.6	 78.7	ENSP00000322866	 C83Y	 HLA-A*02:01	 SLIDLLTYTT	 8	 34.8	 78.7	ENSP00000362403	 A42V	 HLA-A*02:01	 LALWATAYLV	 10	 21	 79.3	ENSP00000382900	 H163Y	 HLA-C*15:04	 YSLEAIALV	 1	 45.6	 79.8	ENSP00000363045	 R270H	 HLA-C*07:02	 RRHHRISSF	 3	 40.6	 80.9	ENSP00000445551	 I298M	 HLA-A*02:01	 FTVMGSLFLM	 4	 28.9	 83.1	ENSP00000439975	 T569M	 HLA-A*03:01	 RTHMGERPFK	 4	 39.9	 84	ENSP00000373378	 V671M	 HLA-A*03:01	 RLCAVNDMGK	 8	 34.9	 87.5	ENSP00000352613	 T2139M	 HLA-A*03:01	 AMLRCELSK	 2	 23.9	 88.5	ENSP00000322866	 C83Y	 HLA-A*02:01	 IDLLTYTTTL	 6	 50	 88.7	ENSP00000391852	 A190V	 HLA-A*02:01	 QQMQDFFLV	 9	 9	 89.9	ENSP00000327290	 T530M	 HLA-A*03:01	 NLFVYNGMLK	 8	 38.4	 94.1	ENSP00000343952	 T277M	 HLA-A*03:01	 LLVMAANLGK	 4	 24.7	 96.5	ENSP00000398796	 G688R	 HLA-B*07:02	 TRPRLPLPSV	 2	 45.3	 100.7	ENSP00000385123	 Y23C	 HLA-B*07:02	 RPRGPIAAHC	 10	 22.2	 104.8	ENSP00000376350	 D253N	 HLA-A*02:01	 LLLAVVFNT	 8	 48.3	 104.8	ENSP00000347883	 R1111W	 HLA-A*02:01	 YLSWPLPGDL	 4	 32.7	 106.4	ENSP00000347507	 P132L	 HLA-A*02:01	 YKWLLVYTPEV	 5	 18.5	 109.9	ENSP00000358360	 A1057V	 HLA-A*02:01	 TMLSLCLENV	 10	 19	 110.4	ENSP00000304767	 R287L	 HLA-C*15:04	 KTMNLRALL	 8	 47.7	 110.8	ENSP00000222969	 V121M	 HLA-A*03:01	 GTNCICRMPK	 8	 47.2	 110.9	ENSP00000297438	 R298C	 HLA-A*02:01	 KLCGKVDPV	 3	 25	 111.1		 	 	167	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000438910	 A113V	 HLA-A*02:01	 NILDEVPFPV	 10	 9.7	 111.4	ENSP00000220058	 V101M	 HLA-C*15:04	 YAMQSQLPV	 3	 33.1	 113.5	ENSP00000317997	 R1248C	 HLA-A*02:01	 QLPCFHIFFL	 4	 43.8	 115.4	ENSP00000459354	 R176C	 HLA-A*02:01	 ILPKWLCFI	 7	 9.6	 119	ENSP00000330631	 G147S	 HLA-B*07:02	 ALPAGVFGSL	 9	 14.8	 120	ENSP00000282670	 A169T	 HLA-A*02:01	 LFTLANVYAV	 3	 42.5	 120.2	ENSP00000441728	 T348M	 HLA-A*02:01	 MMTCMKGTYLV	 1	 19.6	 123.4	ENSP00000419705	 V107M	 HLA-A*03:01	 LSTMSRTLEK	 4	 23.4	 125.7	ENSP00000422231	 V13M	 HLA-A*02:01	 VMMEEIEEA	 2	 5.7	 133.3	ENSP00000402937	 R232Q	 HLA-A*02:01	 KLFSKQTLL	 6	 35.4	 135.6	ENSP00000419705	 V107M	 HLA-A*03:01	 SLSTMSRTLEK	 5	 40.6	 138	ENSP00000365938	 A1325V	 HLA-A*02:01	 LVVDKIIFV	 2	 16.7	 138.4	ENSP00000467784	 T62M	 HLA-A*02:01	 SLSLVRMGV	 7	 47.2	 140.9	ENSP00000407801	 T265I	 HLA-A*02:01	 IMDYPSLGL	 1	 38.3	 144.7	ENSP00000252999	 R2582M	 HLA-A*02:01	 MLGLVWAA	 1	 42.4	 147.3	ENSP00000387775	 R146Q	 HLA-A*02:01	 YLQTRNEFV	 3	 19.1	 153.4	ENSP00000251127	 R708C	 HLA-A*02:01	 KLCKSVFSI	 3	 35.4	 154.1	ENSP00000366362	 G391R	 HLA-A*03:01	 LELRRQELFK	 4	 47.9	 156.1	ENSP00000426252	 A83V	 HLA-B*07:02	 APAPAAPTPV	 10	 46.8	 157.5	ENSP00000340938	 E455K	 HLA-B*07:02	 SRPKESRMRL	 4	 46.5	 158.3	ENSP00000365938	 A1325V	 HLA-A*02:01	 LFLDVLFPLV	 10	 21.5	 158.5	ENSP00000363826	 A445T	 HLA-A*03:01	 ATWLVPSVK	 2	 44.8	 160.6	ENSP00000391818	 T132M	 HLA-B*07:02	 YPRRIQLSRM	 10	 7.5	 162.4		 	 	168	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000405531	 I52T	 HLA-A*02:01	 VQLLTLLYFV	 5	 17.4	 164.8	ENSP00000356014	 T144M	 HLA-A*02:01	 MIYCGDNSV	 1	 23.6	 167.1	ENSP00000345853	 R30C	 HLA-A*02:01	 LLLDCRSCEL	 8	 31.9	 169.5	ENSP00000261772	 G495C	 HLA-A*02:01	 HLDSSCSYV	 6	 43.8	 169.5	ENSP00000345060	 T462I	 HLA-A*02:01	 LLGFAIVYGI	 10	 42.3	 172.2	ENSP00000267502	 V176M	 HLA-A*03:01	 VSSMAHHIGK	 4	 46.2	 175.5	ENSP00000349432	 L9F	 HLA-A*03:01	 RLFRASVAR	 3	 48.6	 178.8	ENSP00000371711	 P216L	 HLA-C*15:04	 YSSFYQLSL	 7	 48.5	 179.2	ENSP00000392105	 L187F	 HLA-A*02:01	 FLLEEEESQV	 1	 21.5	 188.6	ENSP00000356081	 M200I	 HLA-A*02:01	 KLSKFLQQI	 9	 32.6	 193.7	ENSP00000367334	 A488V	 HLA-A*02:01	 SALFNELYFLV	 11	 34.4	 196.9	ENSP00000392811	 P487L	 HLA-A*02:01	 HLFRGLHFT	 6	 30.5	 197.4	ENSP00000319062	 H570Y	 HLA-A*02:01	 WLYNTSSWLA	 3	 23.6	 200.1	ENSP00000320516	 A502V	 HLA-A*02:01	 GLLDDEEFV	 9	 10.4	 202.9	ENSP00000309343	 G172R	 HLA-B*07:02	 GPRPALGYF	 3	 13.4	 207.4	ENSP00000078527	 A253V	 HLA-A*02:01	 SVFTLGLPFV	 10	 17.9	 209.6	ENSP00000326330	 D687G	 HLA-A*02:01	 LQVEQLSGV	 8	 28.4	 213.7	ENSP00000319062	 H570Y	 HLA-A*02:01	 WLYNTSSWL	 3	 30.8	 220.6	ENSP00000343952	 T277M	 HLA-A*03:01	 LVMAANLGK	 3	 27.9	 222.4	ENSP00000382900	 H163Y	 HLA-A*02:01	 YSLEAIALV	 1	 12.7	 228.2	ENSP00000444695	 V503M	 HLA-A*02:01	 NLMDWSEAFA	 3	 29.5	 236	ENSP00000318900	 T347M	 HLA-B*07:02	 IPMPVPKSI	 3	 30.4	 251	ENSP00000351101	 A1165V	 HLA-A*02:01	 ILDFVVVVGV	 10	 33.2	 251.4	ENSP00000442051	 A182V	 HLA-A*02:01	 TLINTIWVVSV	 11	 39.7	 265.1		 	 	169	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000354558	 R1640W	 HLA-A*02:01	 KILMVWSLVV	 6	 34.7	 271.2	ENSP00000269844	 G99V	 HLA-B*07:02	 APRPPPPGVV	 9	 43.3	 278.2	ENSP00000419705	 V107M	 HLA-A*03:01	 STMSRTLEKLK	 3	 45.2	 306.3	ENSP00000280562	 P186S	 HLA-A*03:01	 SIMCAYKLCK	 1	 27.7	 321.5	ENSP00000252999	 R2582M	 HLA-A*02:01	 QQMLGLVWAA	 3	 30.7	 363.3	ENSP00000342665	 F398V	 HLA-A*02:01	 WVFLLSHEV	 2	 26.7	 364.8	ENSP00000220058	 V101M	 HLA-A*02:01	 YAMQSQLPV	 3	 30.5	 407.3	ENSP00000229634	 A825V	 HLA-A*02:01	 FIVQDLEVWV	 10	 43.1	 423.1	ENSP00000352613	 A2376P	 HLA-B*07:02	 SPTLTIRAL	 2	 8.4	 433.9	ENSP00000438910	 A113V	 HLA-A*02:01	 ILDEVPFPVR	 9	 22.7	 437.8	ENSP00000253122	 R207W	 HLA-A*02:01	 QLADRWSPV	 6	 31.9	 454.9	ENSP00000318884	 P936H	 HLA-B*07:02	 LPHLPGAGI	 3	 31.6	 458.4	ENSP00000443299	 S13L	 HLA-A*02:01	 ALLALRVSFV	 2	 23.4	 478.2	ENSP00000438926	 S1565L	 HLA-B*07:02	 LPQLVSLPL	 9	 9.9	 480.3	ENSP00000391852	 A190V	 HLA-A*02:01	 RQQMQDFFLV	 10	 47.3	 497.1	ENSP00000281928	 S624L	 HLA-A*02:01	 ALYCGIRPL	 9	 18.5	 521.4	ENSP00000380969	 C507Y	 HLA-A*02:01	 YQYLFPAKV	 1	 31.5	 555.2	ENSP00000264676	 T155M	 HLA-A*02:01	 SLMSEDGTFL	 3	 44.8	 581.5	ENSP00000348385	 G1042V	 HLA-B*07:02	 MPGGMGTPV	 9	 23	 614.2	ENSP00000245817	 R151H	 HLA-A*02:01	 FQLELRHVV	 7	 38	 690.4	ENSP00000365529	 R601W	 HLA-A*02:01	 VYLCYAVWTV	 8	 24.7	 864.3	ENSP00000354828	 G20V	 HLA-B*07:02	 RPGVTLPPV	 9	 37.3	 885.8	ENSP00000443299	 S13L	 HLA-B*07:02	 TPGMPAPGAL	 10	 12.4	 899	ENSP00000266070	 S1237L	 HLA-B*07:02	 YPKVATVPQL	 10	 16.2	 993.8		 	 	170	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000404542	 P562T	 HLA-A*02:01	 FTLNFVEPV	 2	 4.4	 1047.7	ENSP00000411885	 R195W	 HLA-A*02:01	 ALWEVIASKA	 3	 47.6	 1058.6	ENSP00000416610	 T233R	 HLA-B*07:02	 FPRSQEPL	 3	 47.8	 1090.5	ENSP00000416610	 T233R	 HLA-B*07:02	 FPRSQEPLM	 3	 50	 1144.2	ENSP00000438926	 S1565L	 HLA-B*07:02	 KLPQLVSLPL	 10	 15.6	 1206.6	ENSP00000366843	 P1026L	 HLA-B*07:02	 HPASAAGPL	 9	 6.1	 1208.4	ENSP00000442406	 S277F	 HLA-B*07:02	 LPNSPAASF	 9	 42.3	 1321.4	ENSP00000327440	 T261P	 HLA-B*07:02	 RPPRAVRPL	 2	 13.2	 1396.1	ENSP00000303350	 T468M	 HLA-C*15:04	 KSHTIVMLM	 9	 35.4	 1469.3	ENSP00000312844	 R165W	 HLA-A*02:01	 RLWPLRDPLL	 3	 43.7	 1471.4	ENSP00000369921	 P900L	 HLA-B*07:02	 LARRVRKPL	 1	 27.7	 1567.8	ENSP00000334300	 T98M	 HLA-B*07:02	 LPRPTHKISTM	 11	 26.6	 1599.1	ENSP00000365529	 R601W	 HLA-A*02:01	 YLCYAVWTVP	 7	 49.5	 1831.4	ENSP00000252999	 R2582M	 HLA-A*02:01	 QMLGLVWAAL	 2	 21.6	 1945.6	ENSP00000300061	 K106T	 HLA-C*15:04	 YTYSTVRHL	 2	 39.5	 1948.7	ENSP00000325827	 P1152L	 HLA-B*07:02	 VPALAPGPL	 9	 7.7	 2009	ENSP00000281928	 S907L	 HLA-B*07:02	 SPMVSMVL	 8	 42.7	 2018.5	ENSP00000277462	 P139L	 HLA-B*07:02	 RPFMGGQKL	 9	 7.5	 2038	ENSP00000361219	 D541G	 HLA-A*03:01	 GLVRWKILK	 1	 46.7	 2102.2	ENSP00000374274	 P994S	 HLA-B*07:02	 SPRPAATRTA	 1	 27	 2123.3	ENSP00000339637	 P668S	 HLA-B*07:02	 SPRTHCPYAV	 1	 24.2	 2176.6	ENSP00000411181	 P408S	 HLA-B*07:02	 SPIPVVPPI	 1	 48.7	 2403.8	ENSP00000434640	 T232M	 HLA-C*15:04	 RASFPPLEM	 9	 41.2	 2581.9	ENSP00000363509	 R641W	 HLA-A*02:01	 KLNQEIWMM	 7	 46.7	 2685.1		 	 	171	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000304767	 R287L	 HLA-A*02:01	 ALLDFQTPAM	 2	 41.5	 2889.3	ENSP00000397849	 L501P	 HLA-B*07:02	 SPNDVTLSL	 2	 19.4	 2959.9	ENSP00000253159	 T524M	 HLA-C*07:02	 FRAFAVKSM	 9	 42.1	 2986.4	ENSP00000264676	 T155M	 HLA-A*02:01	 LMSEDGTFL	 2	 21	 3438.2	ENSP00000437856	 R105W	 HLA-A*02:01	 GAFGEVWLV	 7	 25.3	 3472.3	ENSP00000352613	 A2376P	 HLA-B*07:02	 RTSPTLTIRAL	 4	 38.9	 3608.7	ENSP00000366843	 P1026L	 HLA-B*07:02	 MHPASAAGPL	 10	 13.2	 4008.8	ENSP00000350863	 F198V	 HLA-A*02:01	 IELLDTQVNV	 10	 29.8	 4308.8	ENSP00000299432	 F47L	 HLA-A*02:01	 GLFSLDDNV	 2	 44.1	 5028.2	ENSP00000451131	 E186K	 HLA-A*03:01	 RVFHLGRERK	 10	 37	 5109.6	ENSP00000453408	 R405W	 HLA-A*02:01	 GLWEYSEVKTV	 3	 43.6	 5303.9	ENSP00000429834	 R158W	 HLA-A*02:01	 FAWDADVGV	 3	 31.7	 5323.2	ENSP00000453408	 R405W	 HLA-A*02:01	 GLWEYSEV	 3	 28.6	 6641.5	ENSP00000390102	 C19R	 HLA-C*07:02	 YRNSVLQAL	 2	 20.1	 6870.5	ENSP00000297494	 R242Q	 HLA-A*02:01	 FQIWNSQLV	 2	 13.4	 7315.5	ENSP00000462150	 L127P	 HLA-B*07:02	 KPEMAMKEL	 2	 33.9	 8858.1	ENSP00000465722	 E588K	 HLA-A*03:01	 GLLRASLSK	 9	 26.4	 10053.3	ENSP00000422374	 P215L	 HLA-A*02:01	 FHLHHCNLFV	 3	 23.3	 11385.5	ENSP00000252999	 R2582M	 HLA-A*02:01	 QMLGLVWAA	 2	 5.5	 12260.4	ENSP00000239444	 E463K	 HLA-A*03:01	 TSYTLFVRK	 9	 46.4	 12400.8	ENSP00000366843	 P1026L	 HLA-B*07:02	 MMHPASAAGPL	 11	 40.7	 12474.5	ENSP00000349757	 D117Y	 HLA-A*02:01	 YMWHKCRGL	 1	 35.6	 13218.3	ENSP00000466489	 P160L	 HLA-C*07:02	 YRDPSCLSL	 9	 47	 13311.7		 	 	172	ENSP	ID	 Mutation	 HLA	 Mutant	peptide	AA	variant	position	Mutant	ic50	WT	ic50	ENSP00000465722	 E588K	 HLA-A*03:01	 TGLLRASLSK	 10	 37.1	 13921.6	ENSP00000397075	 P757L	 HLA-B*07:02	 GPLTTSSQL	 9	 46.1	 14249.2	ENSP00000364265	 P263L	 HLA-B*07:02	 APATTTGYQL	 10	 37.7	 14272.7	ENSP00000217188	 P448L	 HLA-A*02:01	 RLAACPAEV	 2	 16.4	 15540.1	ENSP00000304767	 R287L	 HLA-A*02:01	 ALLDFQTPA	 2	 9.5	 16288.4	ENSP00000422374	 P215L	 HLA-A*02:01	 HLHHCNLFV	 2	 29.1	 20284.5	ENSP00000379387	 R575M	 HLA-A*02:01	 YMAALTYSS	 2	 47	 28168.3	ENSP00000445841	 R474M	 HLA-A*02:01	 KMAGKEEPV	 2	 45.7	 33890.2			 											 	 	173	Appendix I: Copy number variations in both biopsies chromosome 1012345-1012345-1Tumour / normal linear ratioBiopsy 1Biopsy 25chromosome 2012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 3012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 4012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	174		chromosome 5012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 6012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 7012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 8012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	175		chromosome 9012345-1012345-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 10012345-1012345-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 11012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 12012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	176			chromosome 13012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 14012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 15012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 16012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	177			chromosome 17012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 18012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 19012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 20012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	178			chromosome 21012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome 22012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2chromosome X012345-101234-1Tumour / normal linear ratioBiopsy 1Biopsy 2	 	 	179	Appendix J: Enrichment analysis of mass spectrometry data Enrichment	for	up-regulated	proteins	Term	 Description	 LogP	 Overlap	GO:0071222	 cellular	response	to	lipopolysaccharide	 -4.882479135	 5/160	GO:0071222	 cellular	response	to	lipopolysaccharide	 -4.882479135	 5/160	GO:0071219	 cellular	response	to	molecule	of	bacterial	origin	 -4.780099122	 5/168	GO:0071216	 cellular	response	to	biotic	stimulus	 -4.523260669	 5/190	GO:0034121	 regulation	of	toll-like	receptor	signaling	pathway	 -3.687890132	 3/60	GO:0034121	 regulation	of	toll-like	receptor	signaling	pathway	 -3.687890132	 3/60	GO:0032496	 response	to	lipopolysaccharide	 -3.511633543	 5/312	GO:0002237	 response	to	molecule	of	bacterial	origin	 -3.406067486	 5/329	GO:0045089	 positive	regulation	of	innate	immune	response	 -3.272486515	 5/352	GO:0009617	 response	to	bacterium	 -3.122590156	 6/578	GO:1904950	 negative	regulation	of	establishment	of	protein	localization	 -3.099511075	 4/219	GO:0006820	 anion	transport	 -3.038338043	 6/600	GO:0006820	 anion	transport	 -3.038338043	 6/600	GO:0001817	 regulation	of	cytokine	production	 -2.964820132	 6/620	GO:0001817	 regulation	of	cytokine	production	 -2.964820132	 6/620	GO:0045088	 regulation	of	innate	immune	response	 -2.905608761	 5/425	GO:0032680	 regulation	of	tumor	necrosis	factor	production	 -2.883223239	 3/113	GO:0032640	 tumor	necrosis	factor	production	 -2.850506849	 3/116	GO:1903555	 regulation	of	tumor	necrosis	factor	superfamily	cytokine	production	 -2.83980072	 3/117	GO:0071706	 tumor	necrosis	factor	superfamily	cytokine	production	 -2.787689868	 3/122		 	 	180	Term	 Description	 LogP	 Overlap	GO:0031349	 positive	regulation	of	defense	response	 -2.770913163	 5/456	GO:0007249	 I-kappaB	kinase/NF-kappaB	signaling	 -2.758213275	 4/271	GO:0001816	 cytokine	production	 -2.743927279	 6/685	GO:0002758	 innate	immune	response-activating	signal	transduction	 -2.67860229	 4/285	GO:0002224	 toll-like	receptor	signaling	pathway	 -2.644039726	 3/137	GO:0002218	 activation	of	innate	immune	response	 -2.582417178	 4/303	GO:0006954	 inflammatory	response	 -2.532477559	 6/755	GO:1903828	 negative	regulation	of	cellular	protein	localization	 -2.500333522	 3/154	GO:0048015	 phosphatidylinositol-mediated	signaling	 -2.484559392	 3/156	GO:0006497	 protein	lipidation	 -2.484559392	 3/156	GO:0006497	 protein	lipidation	 -2.484559392	 3/156	GO:0048017	 inositol	lipid-mediated	signaling	 -2.461303009	 3/159	GO:0042158	 lipoprotein	biosynthetic	process	 -2.446060692	 3/161	GO:0071396	 cellular	response	to	lipid	 -2.36350867	 5/567	GO:0002221	 pattern	recognition	receptor	signaling	pathway	 -2.290808289	 3/183	GO:1901215	 negative	regulation	of	neuron	death	 -2.258359506	 3/188	GO:0042157	 lipoprotein	metabolic	process	 -2.214484368	 3/195	GO:0060249	 anatomical	structure	homeostasis	 -2.213415783	 4/385	GO:0060249	 anatomical	structure	homeostasis	 -2.213415783	 4/385	GO:0001819	 positive	regulation	of	cytokine	production	 -2.125932157	 4/408	GO:0051224	 negative	regulation	of	protein	transport	 -2.098082635	 3/215	GO:0007276	 gamete	generation	 -2.067770843	 5/668	GO:0007276	 gamete	generation	 -2.067770843	 5/668	GO:0015850	 organic	hydroxy	compound	transport	 -2.033852659	 3/227		 	 	181	Enrichment	for	down-regulated	proteins	Term	 Description	 LogP	 Overlap	GO:0007596	 blood	coagulation	 -10.57397537	 13/342	GO:0007596	 blood	coagulation	 -10.57397537	 13/342	GO:0007599	 hemostasis	 -10.49554558	 13/347	GO:0050817	 coagulation	 -10.48000658	 13/348	GO:0002576	 platelet	degranulation	 -9.765163617	 9/128	R-HSA-114608	 Platelet	degranulation		 -9.734781866	 9/129	R-HSA-76005	 Response	to	elevated	platelet	cytosolic	Ca2+	 -9.586552659	 9/134	GO:0042060	 wound	healing	 -9.153681716	 14/543	GO:0009611	 response	to	wounding	 -9.091775132	 15/655	R-HSA-140877	 Formation	of	Fibrin	Clot	(Clotting	Cascade)	 -8.741025068	 6/39	GO:0050878	 regulation	of	body	fluid	levels	 -8.555354044	 13/500	R-HSA-140875	 Common	Pathway	of	Fibrin	Clot	Formation	 -8.26395981	 5/22	R-HSA-76002	 Platelet	activation,	signaling	and	aggregation	 -7.939489828	 10/280	GO:0042730	 fibrinolysis	 -7.782503914	 5/27	CORUM:6417	 Fibrinogen	complex	 -7.580618472	 3/3	R-HSA-109582	 Hemostasis	 -7.247480912	 13/645	GO:0061045	 negative	regulation	of	wound	healing	 -7.094624746	 6/72	R-HSA-354194	 GRB2:SOS	provides	linkage	to	MAPK	signaling	for	Integrins		 -6.996312476	 4/15	R-HSA-372708	 p130Cas	linkage	to	MAPK	signaling	for	integrins	 -6.996312476	 4/15	R-HSA-76009	 Platelet	Aggregation	(Plug	Formation)	 -6.94144591	 5/39	GO:0030193	 regulation	of	blood	coagulation	 -6.884879675	 6/78	GO:1900046	 regulation	of	hemostasis	 -6.884879675	 6/78	hsa04610	 Complement	and	coagulation	cascades	 -6.851600066	 6/79		 	 	182	Term	 Description	 LogP	 Overlap	GO:0061041	 regulation	of	wound	healing	 -6.793887804	 7/134	GO:0050818	 regulation	of	coagulation	 -6.691668649	 6/84	GO:1903035	 negative	regulation	of	response	to	wounding	 -6.691668649	 6/84	GO:0031638	 zymogen	activation	 -6.571808915	 5/46	GO:0030195	 negative	regulation	of	blood	coagulation	 -6.342932904	 5/51	GO:1900047	 negative	regulation	of	hemostasis	 -6.342932904	 5/51	GO:0030168	 platelet	activation	 -6.288561288	 7/159	GO:1903034	 regulation	of	response	to	wounding	 -6.270162762	 7/160	GO:0031639	 plasminogen	activation	 -6.192152659	 4/23	GO:0050819	 negative	regulation	of	coagulation	 -6.136842371	 5/56	GO:0072378	 blood	coagulation,	fibrin	clot	formation	 -5.899010753	 4/27	R-HSA-354192	 Integrin	alphaIIb	beta3	signaling	 -5.899010753	 4/27	R-HSA-9006921	 Integrin	signaling	 -5.899010753	 4/27	M169	 PID	INTEGRIN2	PATHWAY	 -5.769578948	 4/29	M257	 PID	EPHRINB	REV	PATHWAY	 -5.708417439	 4/30	R-HSA-6802948	 Signaling	by	high-kinase	activity	BRAF	mutants	 -5.382012239	 4/36	GO:0034116	 positive	regulation	of	heterotypic	cell-cell	adhesion	 -5.24665646	 3/12	R-HSA-5674135	 MAP2K	and	MAPK	activation	 -5.195209309	 4/40	R-HSA-6802946	 Signaling	by	moderate	kinase	activity	BRAF	mutants	 -5.195209309	 4/40	R-HSA-6802955	 Paradoxical	activation	of	RAF	signaling	by	kinase	inactive	BRAF	 -5.195209309	 4/40		 	 	183	Term	 Description	 LogP	 Overlap	M174	 PID	UPA	UPAR	PATHWAY	 -5.109132919	 4/42	M53	 PID	INTEGRIN3	PATHWAY	 -5.0677147	 4/43	R-HSA-5673001	 RAF/MAP	kinase	cascade	 -5.065325117	 7/243	R-HSA-5684996	 MAPK1/MAPK3	signaling	 -4.996431812	 7/249	GO:0045055	 regulated	exocytosis	 -4.842858568	 11/752	GO:0007157	 heterophilic	cell-cell	adhesion	via	plasma	membrane	cell	adhesion	molecules	 -4.838895807	 4/49	GO:0007157	 heterophilic	cell-cell	adhesion	via	plasma	membrane	cell	adhesion	molecules	 -4.838895807	 4/49	GO:0034113	 heterotypic	cell-cell	adhesion	 -4.769181677	 4/51	GO:0098609	 cell-cell	adhesion	 -4.716393291	 11/776	R-HSA-6802949	 Signaling	by	RAS	mutants	 -4.702309902	 4/53	GO:0032101	 regulation	of	response	to	external	stimulus	 -4.685513597	 11/782	GO:0032101	 regulation	of	response	to	external	stimulus	 -4.685513597	 11/782	R-HSA-5686938	 Regulation	of	TLR	by	endogenous	ligand	 -4.609330099	 3/19	GO:0015669	 gas	transport	 -4.609330099	 3/19	GO:0015669	 gas	transport	 -4.609330099	 3/19	GO:0070527	 platelet	aggregation	 -4.606863678	 4/56	R-HSA-5683057	 MAPK	family	signaling	cascades	 -4.589196932	 7/288	R-HSA-6802952	 Signaling	by	BRAF	and	RAF	fusions	 -4.487716705	 4/60		 	 	184	Term	 Description	 LogP	 Overlap	hsa04611	 Platelet	activation	 -4.451984524	 5/123	GO:0034114	 regulation	of	heterotypic	cell-cell	adhesion	 -4.41094934	 3/22	GO:0030198	 extracellular	matrix	organization	 -4.149252032	 7/338	GO:0034109	 homotypic	cell-cell	adhesion	 -4.105836267	 4/75	R-HSA-6802957	 Oncogenic	MAPK	signaling	 -4.0833388	 4/76	R-HSA-445095	 Interaction	between	L1	and	Ankyrins	 -3.954183283	 3/31	R-HSA-445095	 Interaction	between	L1	and	Ankyrins	 -3.954183283	 3/31	GO:2000352	 negative	regulation	of	endothelial	cell	apoptotic	process	 -3.91236888	 3/32	GO:0051258	 protein	polymerization	 -3.910236213	 6/256	GO:0034446	 substrate	adhesion-dependent	cell	spreading	 -3.894027955	 4/85	R-HSA-216083	 Integrin	cell	surface	interactions	 -3.894027955	 4/85	GO:0045907	 positive	regulation	of	vasoconstriction	 -3.871913668	 3/33	GO:1900026	 positive	regulation	of	substrate	adhesion-dependent	cell	spreading	 -3.832733812	 3/34	GO:0043062	 extracellular	structure	organization	 -3.743704994	 7/393	M5884	 NABA	CORE	MATRISOME	 -3.741308411	 6/275	GO:1902042	 negative	regulation	of	extrinsic	apoptotic	signaling	pathway	via	death	domain	receptors	 -3.687337243	 3/38	GO:0072376	 protein	activation	cascade	 -3.682431296	 5/179	GO:0032102	 negative	regulation	of	response	to	external	stimulus	 -3.665826295	 6/284	GO:2001237	 negative	regulation	of	extrinsic	apoptotic	signaling	pathway	 -3.57254392	 4/103	GO:1904036	 negative	regulation	of	epithelial	cell	apoptotic	 -3.557278652	 3/42		 	 	185	Term	 Description	 LogP	 Overlap	process	R-HSA-1474244	 Extracellular	matrix	organization	 -3.538108056	 6/300	GO:0019730	 antimicrobial	humoral	response	 -3.524882405	 4/106	M3008	 NABA	ECM	GLYCOPROTEINS	 -3.500396153	 5/196	GO:0022604	 regulation	of	cell	morphogenesis	 -3.481991893	 7/434	GO:0022604	 regulation	of	cell	morphogenesis	 -3.481991893	 7/434	GO:0019731	 antibacterial	humoral	response	 -3.468047077	 3/45	GO:0016485	 protein	processing	 -3.425238094	 6/315	GO:1900024	 regulation	of	substrate	adhesion-dependent	cell	spreading	 -3.358389302	 3/49	R-HSA-373760	 L1CAM	interactions	 -3.320189862	 4/120	GO:2000351	 regulation	of	endothelial	cell	apoptotic	process	 -3.307059454	 3/51	GO:0031333	 negative	regulation	of	protein	complex	assembly	 -3.240262356	 4/126	GO:0031347	 regulation	of	defense	response	 -3.209177309	 9/795	GO:0035296	 regulation	of	tube	diameter	 -3.176756743	 4/131	GO:0097746	 regulation	of	blood	vessel	diameter	 -3.176756743	 4/131	GO:0072577	 endothelial	cell	apoptotic	process	 -3.16498129	 3/57	GO:1905952	 regulation	of	lipid	localization	 -3.127826847	 4/135	GO:1905952	 regulation	of	lipid	localization	 -3.127826847	 4/135	GO:1902041	 regulation	of	extrinsic	apoptotic	signaling	pathway	via	death	domain	receptors	 -3.121121527	 3/59	GO:0019229	 regulation	of	vasoconstriction	 -3.099779733	 3/60	GO:0098742	 cell-cell	adhesion	via	plasma-membrane	adhesion	molecules	 -3.09265702	 5/241	R-HSA-202733	 Cell	surface	interactions	at	the	vascular	wall	 -3.092157905	 4/138		 	 	186	Term	 Description	 LogP	 Overlap	GO:0050880	 regulation	of	blood	vessel	size	 -3.080456491	 4/139	GO:0035150	 regulation	of	tube	size	 -3.068846917	 4/140	GO:0032272	 negative	regulation	of	protein	polymerization	 -3.058207093	 3/62	GO:0008360	 regulation	of	cell	shape	 -3.057327858	 4/141	GO:0051604	 protein	maturation	 -3.040760541	 6/373	GO:0000904	 cell	morphogenesis	involved	in	differentiation	 -3.017589128	 8/677	GO:0022409	 positive	regulation	of	cell-cell	adhesion	 -3.00599504	 5/252	GO:0043242	 negative	regulation	of	protein	complex	disassembly	 -2.998448499	 3/65	M18	 PID	INTEGRIN1	PATHWAY	 -2.979179067	 3/66	GO:1903524	 positive	regulation	of	blood	circulation	 -2.905095026	 3/70	GO:2001236	 regulation	of	extrinsic	apoptotic	signaling	pathway	 -2.90488733	 4/155	R-HSA-199977	 ER	to	Golgi	Anterograde	Transport	 -2.90488733	 4/155	R-HSA-168898	 Toll-Like	Receptors	Cascades	 -2.884355017	 4/157	GO:0007156	 homophilic	cell	adhesion	via	plasma	membrane	adhesion	molecules	 -2.854092716	 4/160	GO:1905954	 positive	regulation	of	lipid	localization	 -2.835396992	 3/74	GO:0003018	 vascular	process	in	circulatory	system	 -2.834264813	 4/162	GO:0042310	 vasoconstriction	 -2.802043341	 3/76	GO:1904035	 regulation	of	epithelial	cell	apoptotic	process	 -2.769616407	 3/78	GO:0007009	 plasma	membrane	organization	 -2.769616407	 3/78	GO:0007009	 plasma	membrane	organization	 -2.769616407	 3/78	GO:0045921	 positive	regulation	of	exocytosis	 -2.738068621	 3/80	GO:0070371	 ERK1	and	ERK2	cascade	 -2.710924075	 5/294	GO:0097435	 supramolecular	fiber	organization	 -2.707360787	 7/589		 	 	187	Term	 Description	 LogP	 Overlap	GO:0008625	 extrinsic	apoptotic	signaling	pathway	via	death	domain	receptors	 -2.692299979	 3/83	GO:0044070	 regulation	of	anion	transport	 -2.692299979	 3/83	GO:0097756	 negative	regulation	of	blood	vessel	diameter	 -2.677437706	 3/84	GO:0002221	 pattern	recognition	receptor	signaling	pathway	 -2.641173527	 4/183	GO:0015893	 drug	transport	 -2.632604806	 4/184	R-HSA-948021	 Transport	to	the	Golgi	and	subsequent	modification	 -2.615623083	 4/186	GO:0015711	 organic	anion	transport	 -2.60802784	 6/454	GO:0017157	 regulation	of	exocytosis	 -2.607208957	 4/187	GO:0031349	 positive	regulation	of	defense	response	 -2.598545265	 6/456	GO:0090066	 regulation	of	anatomical	structure	size	 -2.593822973	 6/457	GO:0070555	 response	to	interleukin-1	 -2.590531429	 4/189	GO:0006888	 ER	to	Golgi	vesicle-mediated	transport	 -2.582266956	 4/190	GO:0090277	 positive	regulation	of	peptide	hormone	secretion	 -2.564989207	 3/92	GO:0006959	 humoral	immune	response	 -2.528617897	 5/324	GO:0070374	 positive	regulation	of	ERK1	and	ERK2	cascade	 -2.525754526	 4/197	GO:0043244	 regulation	of	protein	complex	disassembly	 -2.487376257	 3/98	GO:1904019	 epithelial	cell	apoptotic	process	 -2.462644597	 3/100	R-HSA-977225	 Amyloid	fiber	formation	 -2.462644597	 3/100	R-HSA-977225	 Amyloid	fiber	formation	 -2.462644597	 3/100	R-HSA-6807878	 COPI-mediated	anterograde	transport	 -2.438444445	 3/102	GO:0007160	 cell-matrix	adhesion	 -2.426591637	 4/210	GO:2001234	 negative	regulation	of	apoptotic	signaling	pathway	 -2.419254246	 4/211		 	 	188	Term	 Description	 LogP	 Overlap	GO:0045861	 negative	regulation	of	proteolysis	 -2.386021151	 5/350	GO:0045861	 negative	regulation	of	proteolysis	 -2.386021151	 5/350	GO:0010811	 positive	regulation	of	cell-substrate	adhesion	 -2.380135407	 3/107	GO:0032368	 regulation	of	lipid	transport	 -2.380135407	 3/107	GO:0045089	 positive	regulation	of	innate	immune	response	 -2.375577471	 5/352	GO:0097191	 extrinsic	apoptotic	signaling	pathway	 -2.368978237	 4/218	R-HSA-8957275	 Post-translational	protein	phosphorylation	 -2.368830287	 3/108	GO:0043410	 positive	regulation	of	MAPK	cascade	 -2.35207133	 6/512	GO:0008015	 blood	circulation	 -2.323624439	 6/519	GO:0042177	 negative	regulation	of	protein	catabolic	process	 -2.313969244	 3/113	GO:0003013	 circulatory	system	process	 -2.307584192	 6/523	GO:1902904	 negative	regulation	of	supramolecular	fiber	organization	 -2.251544155	 3/119	GO:2001233	 regulation	of	apoptotic	signaling	pathway	 -2.231706115	 5/381	GO:0051592	 response	to	calcium	ion	 -2.221616165	 3/122	GO:0022407	 regulation	of	cell-cell	adhesion	 -2.212903141	 5/385	GO:0051047	 positive	regulation	of	secretion	 -2.212903141	 5/385	GO:0045785	 positive	regulation	of	cell	adhesion	 -2.208239485	 5/386	R-HSA-381426	 Regulation	of	Insulin-like	Growth	Factor	(IGF)	transport	and	uptake	by	Insulin-like	Growth	Factor	Bi	-2.19248841	 3/125	R-HSA-425407	 SLC-mediated	transmembrane	transport	 -2.184938626	 4/246	R-HSA-425407	 SLC-mediated	transmembrane	transport	 -2.184938626	 4/246	GO:0046887	 positive	regulation	of	hormone	secretion	 -2.182950286	 3/126	GO:0051494	 negative	regulation	of	cytoskeleton	organization	 -2.182950286	 3/126		 	 	189	Term	 Description	 LogP	 Overlap	GO:0050865	 regulation	of	cell	activation	 -2.166192847	 6/560	hsa04380	 Osteoclast	differentiation	 -2.145617599	 3/130	GO:0006954	 inflammatory	response	 -2.11939582	 7/755	GO:0009617	 response	to	bacterium	 -2.101644679	 6/578	GO:0002224	 toll-like	receptor	signaling	pathway	 -2.083252179	 3/137	GO:0010770	 positive	regulation	of	cell	morphogenesis	involved	in	differentiation	 -2.083252179	 3/137	GO:0043254	 regulation	of	protein	complex	assembly	 -2.066359202	 5/418	GO:0008277	 regulation	of	G-protein	coupled	receptor	protein	signaling	pathway	 -2.066082738	 3/139	GO:0008277	 regulation	of	G-protein	coupled	receptor	protein	signaling	pathway	 -2.066082738	 3/139	GO:0009895	 negative	regulation	of	catabolic	process	 -2.04558492	 4/270	hsa05418	 Fluid	shear	stress	and	atherosclerosis	 -2.040838492	 3/142	GO:0045088	 regulation	of	innate	immune	response	 -2.037102238	 5/425	GO:0001906	 cell	killing	 -2.032555858	 3/143	GO:0001906	 cell	killing	 -2.032555858	 3/143	GO:0006820	 anion	transport	 -2.026186783	 6/600	GO:0070372	 regulation	of	ERK1	and	ERK2	cascade	 -2.007665945	 4/277	GO:0002764	 immune	response-regulating	signaling	pathway	 -2.006229114	 6/606	GO:0035821	 modification	of	morphology	or	physiology	of	other	organism	 -2.000061481	 3/147	GO:0035725	 sodium	ion	transmembrane	transport	 -2.000061481	 3/147		

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