UBC Faculty Research and Publications

Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors Jones, Steven J; Laskin, Janessa; Li, Yvonne Y; Griffith, Obi L; An, Jianghong; Bilenky, Mikhail; Butterfield, Yaron S; Cezard, Timothee; Chuah, Eric; Corbett, Richard; Fejes, Anthony P; Griffith, Malachi; Yee, John; Martin, Montgomery; Mayo, Michael; Melnyk, Nataliya; Morin, Ryan D; Pugh, Trevor J; Severson, Tesa; Shah, Sohrab P; Sutcliffe, Margaret; Tam, Angela; Terry, Jefferson; Thiessen, Nina; Thomson, Thomas; Varhol, Richard; Zeng, Thomas; Zhao, Yongjun; Moore, Richard A; Huntsman, David G; Birol, Inanc; Hirst, Martin; Holt, Robert A; Marra, Marco A Aug 9, 2010

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RESEARCH Open AccessEvolution of an adenocarcinoma in response toselection by targeted kinase inhibitorsSteven JM Jones1*, Janessa Laskin2, Yvonne Y Li1, Obi L Griffith1, Jianghong An1, Mikhail Bilenky1,Yaron S Butterfield1, Timothee Cezard1, Eric Chuah1, Richard Corbett1, Anthony P Fejes1, Malachi Griffith1,John Yee3, Montgomery Martin2, Michael Mayo1, Nataliya Melnyk4, Ryan D Morin1, Trevor J Pugh1, Tesa Severson1,Sohrab P Shah4,5, Margaret Sutcliffe2, Angela Tam1, Jefferson Terry4, Nina Thiessen1, Thomas Thomson2,Richard Varhol1, Thomas Zeng1, Yongjun Zhao1, Richard A Moore1, David G Huntsman3, Inanc Birol1, Martin Hirst1,Robert A Holt1, Marco A Marra1AbstractBackground: Adenocarcinomas of the tongue are rare and represent the minority (20 to 25%) of salivary glandtumors affecting the tongue. We investigated the utility of massively parallel sequencing to characterize anadenocarcinoma of the tongue, before and after treatment.Results: In the pre-treatment tumor we identified 7,629 genes within regions of copy number gain. There were1,078 genes that exhibited increased expression relative to the blood and unrelated tumors and four genescontained somatic protein-coding mutations. Our analysis suggested the tumor cells were driven by the REToncogene. Genes whose protein products are targeted by the RET inhibitors sunitinib and sorafenib correlated withbeing amplified and or highly expressed. Consistent with our observations, administration of sunitinib wasassociated with stable disease lasting 4 months, after which the lung lesions began to grow. Administration ofsorafenib and sulindac provided disease stabilization for an additional 3 months after which the cancer progressedand new lesions appeared. A recurring metastasis possessed 7,288 genes within copy number amplicons,385 genes exhibiting increased expression relative to other tumors and 9 new somatic protein coding mutations.The observed mutations and amplifications were consistent with therapeutic resistance arising through activationof the MAPK and AKT pathways.Conclusions: We conclude that complete genomic characterization of a rare tumor has the potential to aid inclinical decision making and identifying therapeutic approaches where no established treatment protocols exist.These results also provide direct in vivo genomic evidence for mutational evolution within a tumor under drugselection and potential mechanisms of drug resistance accrual.BackgroundLarge-scale sequence analysis of cancer transcriptomes,predominantly using expressed sequence tags (ESTs) [1]or serial analysis of gene expression (SAGE) [2,3], hasbeen used to identify genetic lesions that accrue duringoncogenesis. Other studies have involved large-scalePCR amplification of exons and subsequent DNAsequence analysis of the amplicons to survey themutational status of protein kinases in many cancersamples [4], 623 ‘cancer genes’ in lung adenocarcinomas[5], 601 genes in glioblastomas, and all annotated codingsequences in breast, colorectal [6,7] and pancreatictumors [8], searching for somatic mutations that driveoncogenesis.The development of massively parallel sequencingtechnologies has provided an unprecedented opportunityto rapidly and efficiently sequence human genomes [9].Such technology has been applied to the identificationof genome rearrangements in lung cancer cell lines [10],and the sequencing of a complete acute myeloid* Correspondence: sjones@bcgsc.ca1Genome Sciences Centre, British Columbia Cancer Agency, 570 West 7thAvenue, Vancouver, BC, V5Z 4S6, CanadaFull list of author information is available at the end of the articleJones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82© 2010 Jones et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.leukemia genome [11] and a breast cancer genome [12].The technology has also been adapted for sequencing ofcancer cell line transcriptomes [13-16]. However, meth-odological approaches for integrated analysis of cancergenome and transcriptome sequences have not beenreported; nor has there been evidence presented in theliterature that such analysis has the potential to informthe choice of cancer treatment options. We present forthe first time such evidence here. This approach is ofparticular relevance for rarer tumor types, where thescarcity of patients, their geographic distribution and thediversity of patient presentation mean that the ability toaccrue sufficient patient numbers for statistically pow-ered clinical trials is unlikely. The ability to comprehen-sively genetically characterize rare tumor types at anindividual patient level therefore represents a logicalroute for informed clinical decision making andincreased understanding of these diseases.In this case the patient is a 78 year old, fit and activeCaucasian man. He presented in August 2007 withthroat discomfort and was found to have a 2 cm massat the left base of the tongue. He had minimal comor-bidities and no obvious risk factors for an oropharyngealmalignancy. A positron emission tomography-computedtomography (PET-CT) scan identified suspicious uptakein the primary mass and two local lymph nodes. Asmall biopsy of the tongue lesion revealed a papillaryadenocarcinoma, although the presence in the tonguemay indicate an origin in a minor salivary gland. Adeno-carcinomas of the tongue are rare and represent theminority (20 to 25%) of the salivary gland tumors affect-ing the tongue [17-19]. In November 2007 the patienthad a laser resection of the tumor and lymph node dis-section. The pathology described a 1.5 cm poorly differ-entiated adenocarcinoma with micropapillary andmucinous features. The final surgical margins werenegative. Three of 21 neck nodes (from levels 1 to 5)indicated the presence of metastatic adenocarcinoma.Subsequently, the patient received 60 Gy of adjuvantradiation therapy completed in February 2008. Fourmonths later, although the patient remained asympto-matic, a routine follow up PET-CT scan identifiednumerous small (largest 1.2 cm) bilateral pulmonarymetastases, none of which had been present on the pre-operative PET-CT 9 months previously. There was noevidence of local recurrence. Lacking standard che-motherapy treatment options for this rare tumor type,subsequent pathology review indicated +2 EGFR expres-sion (Zymed assay) and a 6-week trial of the epidermalgrowth factor receptor (EGFR) inhibitor erlotinib wasinitiated. All the pulmonary nodules grew while on thisdrug, the largest lesion increasing in size from 1.5 cm to2.1 cm from June 19th to August 18th. Chemotherapywas stopped on August 20th and a repeat CT onOctober 1st showed growth in all of the lung metas-tases. The patient provided explicit consent to pursue agenomic and transcriptome analysis and elected toundergo a fresh tumor tissue needle biopsy of a 1.7 cmleft upper lobe lung lesion. This was done under CTguidance and multiple aspirates were obtained foranalysis.Results and discussionDNA sequencing and mutation detectionThere were 2,584,553,684 and 498,229,009 42-bpsequence reads that aligned to the reference human gen-ome (HG18) from the tumor DNA and tumor transcrip-tome, respectively. We aligned 342,019,291 sequencereads from normal gDNA purified from peripheralblood cells and 62,517,972 sequence reads from the leu-kocyte transcriptome to the human reference to serve ascontrols. Our analysis concentrated on those geneticchanges that we could predict elicited an effect on thecellular function, that is, changes in effective copy num-ber of a gene or the sequence of a protein product. Dueto our inability to usefully interpret alterations in non-coding regions, such changes were not considered.Comparison of the relative frequency of sequence align-ment derived from the tumor and normal DNA identi-fied 7,629 genes in chromosomally amplified regions,and of these, 17 genes were classified as being highlyamplified. Our analysis also revealed large regions ofchromosomal loss, including 12p, 17p, 18q and 22q(Figure 1). Intriguingly, we observed loss of approxi-mately 57 megabases from 18q, although within thisregion we observed three highly amplified segments(Figure S3a in Additional file 1). Frequent loss of 18qhas been observed in colorectal metastases. In suchcases it is believed that the inactivation of the tumorsuppressor protein Smad4 and the allelic loss of 18q aredriving events in the formation of metastasis to the liver[20]. The expression level of Smad4 in the tumor wasfound to be very low (43-fold lower than in sampleswithin our compendium of tumor expression data).Hence, down-regulation of Smad4 along with loss of18q also appear to be properties of the tumor. Otherlarge chromosomal losses observed in the tumor, 17p,22q and 12p, did not correlate with losses commonlydetermined in previous studies of salivary gland tumors[21-23].Our initial analysis of sequence alignments identified84 DNA putative sequence changes corresponding tonon-synonymous changes in protein coding regions pre-sent only within the tumor, of which 4 were subse-quently validated to be somatic tumor mutations bySanger sequencing (Table 1). The vast majority of falsepositives were due to undetected heterozygous alleles inthe germline. Somatic mutations were observed in twoJones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 2 of 12well characterized tumor suppressor genes, TP53(D259Y) and a truncating mutation in RB1 (L234*)removing 75% of its coding sequence [24], with TP53also within a region of heterozygous loss (LOH).Transcriptome analysisWhole transcriptome shotgun sequencing (WTSS)[15,25] was conducted to profile the expression oftumor transcripts. In the absence of an equivalent nor-mal tissue for comparison, we compared expressionchanges to the patient’s leukocytes and a compendiumof 50 tumor-derived WTSS datasets, which would avoidspurious observations due to technical or methodologi-cal differences between gene expression profiling plat-forms. This compendium approach allowed us toidentify a specific and unique molecular transcript signa-ture for this tumor, as compared to unrelated tumors,enriched in cancer causing events specific to thepatient’s tumor and therefore should represent relevantdrug targets for therapeutic intervention. There wereFigure 1 Identified regions of chromosomal copy number variation (CNV) and loss of heterozygosity (LOH) in both the pre-treatment(T1) and post-treatment (T2) tumor samples and matched normal patient DNA (R) plotted in Circos format [52]. CNV values are thehidden Markov model (HMM) state. Δ indicates the degree in change of HMM state between the two cancers.Jones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 3 of 12Table 1 Predicted protein coding somatic changes within the initial and the drug resistant recurrent tumorTumor Chr. Ensembl gene ID EnsembldisplayHUGOIDChr.positionRef. Obs. Het. ProteinpositionRef.aminoacidAlt.aminoacidDescriptionInitial 6 ENSG00000197062 ZNF187-20112978 28352058 G T K 62 G C Zinc finger protein 187 (Zincfinger and SCAN domain-containing protein 26)(Protein SRE-ZBP)Initial 8 ENSG00000169946 ZFPM2-20216700 106884238 A G R 785 K E Zinc finger protein ZFPM2(Zinc finger protein multitype2) (Friend of GATA protein 2)(FOG-2) (hFOG-2)Initial 13 ENSG00000139687 RB1-002 9884 47832247 T A W 234 L * Retinoblastoma-associatedprotein (pRb) (Rb) (pp110)(p105-Rb)Initial 17 ENSG00000141510 TP53-202 11998 7518231 C A M 259 D Y Cellular tumor antigen p53(Tumor suppressor p53)(Phosphoprotein p53)(Antigen NY-CO-13)Recurrence 1 ENSG00000146463 ZMYM4-00113055 35608585 G C S 317 Q H Zinc finger MYM-type protein4 (Zinc finger protein 262)Recurrence 2 ENSG00000118997 DNAH7-20118661 196431742 C G S 2590 V L Dynein heavy chain 7,axonemal (Axonemal betadynein heavy chain 7) (Ciliarydynein heavy chain 7)(Dynein heavy chain-likeprotein 2) (HDHC2)Recurrence 4 ENSG00000156234 CXCL13-00110639 78747983 G A R 56 R H C-X-C motif chemokine 13Precursor (Small-induciblecytokine B13) (B lymphocytechemoattractant) (CXCchemokine BLC) (B cell-attracting chemokine 1) (BCA-1) (ANGIE)Recurrence 6 ENSG00000204228 HSD17B8-0013554 33281235 G A R 141 A T Estradiol 17-beta-dehydrogenase 8 (EC 17-beta-dehydrogenase 8) (EC1.1.1.63) (17-beta-hydroxysteroiddehydrogenase 8) (17-beta-HSD 8) (Protein Ke6) (Ke-6)Recurrence 7 ENSG00000186472 PCLO-201 13406 82419723 T C Y 2759 T A Protein piccolo (Aczonin)Recurrence 11 ENSG00000152578 GRIA4-2014574 105355581 C T Y 872 R C Glutamate receptor 4Precursor (GluR-4) (GluR4)(GluR-D) (Glutamate receptorionotropic, AMPA 4) (AMPA-selective glutamate receptor4)Recurrence 14 ENSG00000165762 OR4K2-20114728 19414855 C T Y 197 L F Olfactory receptor 4K2(Olfactory receptor OR14-15)Recurrence 14 ENSG00000054654 SYNE2-20617084 63500386 C G S 302 A G Nesprin-2 (Nuclear envelopespectrin repeat protein 2)(Synaptic nuclear envelopeprotein 2) (Syne-2) (Nucleusand actin connecting elementprotein) (Protein NUANCE)Recurrence 18 ENSG00000173482 PTPRM-2029675 8333477 G A R 929 A T Receptor-type tyrosine-proteinphosphatase mu Precursor(Protein-tyrosine phosphatasemu) (R-PTP-mu) (EC non-synonymous single nucleotide variations (SNVs) predicted by high-throughput sequencing are listed with the corresponding chromosome (CHr.),Ensembl gene ID, the HUGO ID, chromosomal position, the identity of the base at this location in the reference genome (Ref.), the observed base that does notmatch the reference (Obs.), and the IUPAC code at the heterogeneous position (Het.), the position in the protein where the amino acid changed as a result ofthe SNV, the reference amino acid, the altered amino acid, and the Ensembl description for this gene. Those marked as ‘Initial’ (first four SNVs) were identified inthe primary tumor and were validated using PCR and Sanger sequencing on germline and tumor genomic DNA. Those marked as ‘Recurrence’ (remaining nineSNVs) were identified in the post-treatment secondary tumor and were validated by Illumina sequencing. SNVs in the initial tumor were also identified andvalidated in the recurrent tumor.Jones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 4 of 123,064 differentially expressed genes (1,078 up-regulated,1,986 down-regulated) in the lung tumor versus theblood/compendium. This analysis provided insight intothose genes whose expression rate was likely to be adriving factor specific to this tumor, not identifyinggenes that correlate simply with proliferation and celldivision. It is conceivable that such an approach,coupled with a greater understanding from multipletumor datasets, could be replaced by the absolute quan-tification of oncogene expression as a means to deter-mine clinical relevance. Changes in expression in bothmetastases were significantly associated with copy num-ber changes (Figures S4 and S5 in Additional file 1). Alarge number of canonical pathways were identified asover-represented in the pathway analysis. Specifically,ten pathways were significant from the lung versusblood/compendium gene lists (predominantly from thedown-regulated list), two from skin versus blood/com-pendium, and 98 from skin versus lung (predominantlyover-expressed in skin relative to lung). These includedmany molecular mechanisms of cancer and cancer-related signaling pathways, such as mammalian target ofrapamycin (mTOR) signaling, p53 signaling, Myc-mediated apoptosis signaling, vascular endothelialgrowth factor (VEGF) signaling, phosphoinositide 3-kinase (PI3K)/AKT signaling, and phosphatase and ten-sin homolog (PTEN) signaling, amongst others (TableS5 in Additional file 1).We correlated the mutated, amplified or differentiallyexpressed genes with known cancer pathways from theKyoto Encyclopedia of Genes and Genomes (KEGG)database [26] and to drug targets present in the Drug-Bank database [27]. The 15 amplified, over-expressed ormutated genes in cancer pathways targetable byapproved drugs are listed in Table S2 in Additional file1. Some amplified genes, such as NKX3-1, RBBP8 andCABL1, were implicated in cancer but are not well char-acterized in this role. In addition, they did not haveknown drugs targeting them. The Ret proto-oncogene(RET) emerged as a gene of particular interest to us, asit was present in a region of genomic amplification andwas abundantly expressed. RET is a receptor tyrosinekinase that stimulates signals for cell growth and differ-entiation via the mitogen-activated protein kinase(MAPK)-extracellular signal-regulated kinase (ERK)pathway [28] and its constitutive activation is responsi-ble for oncogenic transformation in medullary andpapillary thyroid carcinoma [29]. In the lung tumor,RET was both highly amplified (hidden Markov model(HMM) level 4) and the most highly expressed knownoncogene (34.5 fold change (FC) in lung relative tocompendium; 123.2 FC in lung relative to blood) (Figure2). In addition, many of the MAPK pathway constituentsare also highly expressed in the tumor. Interestingly,over-expression of the water channel protein Aqua-porin-5 (AQP5) has been implicated in multiple cancersand has been shown to activate Ras and its signalingpathways [30].Aberrations leading to increased activation of thePI3K/AKT pathway are common in human cancers andare reviewed in [31]. Inactivating mutations anddecreased expression (either by LOH or methylation) ofPTEN, a tumor suppressor that reverses the action ofPI3K, are the most frequently observed aberrations. Inthe patient tumor, PTEN was under-expressed (-109.7FC in lung relative to compendium; -440.1 FC in lungrelative to blood), and we note that PTEN maps to aregion of heterozygous loss in the tumor genome. SincePTEN mediates crosstalk between PI3K and RET signal-ing by negatively regulating SHC and ERK [32] and up-regulated RET can also activate the PI3K/AKT pathway[33], loss of PTEN would up-regulate both the PI3K/AKT and RET-MAPK pathways, leading to decreasedapoptosis, increased protein synthesis and cellular prolif-eration. However, in the patient, we observed LOH dele-tion in AKT1, under-expression of AKT2, mTOR, elF4E,and over-expression of the negative regulators eIF4EBP1and NKX3-1. These changes mitigate the effect ofPTEN loss on the PI3K/AKT pathway and suggest thatthe loss of PTEN serves primarily to further activate theRET pathway to drive tumor growth. The high expres-sion of RET (which, like EGFR, activates the RAS/ERKpathway) provides a plausible explanation of the failureof erlotinib to control proliferation of this tumor. PTENloss has also been implicated in resistance to the EGFRinhibitors gefitinib [34] and erlotinib [35], to which thetumor was determined to be insensitive. Lastly, themutated RB1 may also play a role in the observed erloti-nib insensitivity, as the loss of both RB1 and PTEN asseen in this tumor has previously been implicated ingefitinib resistance [36].Therapeutic interventionThe integration of copy number, expression and muta-tional data allowed for a compelling hypothesis of themechanism driving the tumor and allowed identificationof drugs that target the observed aberrations (Table S1in Additional file 1). The major genomic abnormalitiesdetected in the lung tumor sample were the up-regula-tion of the MAPK pathways through RET over-expres-sion and PTEN deletion. Fluorescent in situhybridization (FISH) and immunohistochemical analysiswere used to confirm the status of RET and PTEN (Fig-ure 3). Consistent with these observations, clinicaladministration of the RET inhibitor sunitinib had theeffect of shrinking the tumors. The patient gave his fulland informed consent to initiate therapy with this medi-cation and was fully aware that adenocarcinoma of theJones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 5 of 12Figure 2 Cancer signaling pathways affected within the tumor. (a) Pre-treatment: overall, the down-regulation of PTEN and up-regulation ofthe RET signaling pathway appear to be driving tumor proliferation. Increased signaling independent of EGFR is consistent with the observederlotinib insensitivity of the tumor. (b) Post-versus pre-treatment: after treatment with the RET inhibitors sunitinib and sorafenib, there is amarked increase in the signaling of pathway constituents, increasing tumor proliferation. Black and red pathway arrows represent activation andinhibition, respectively. Dotted arrows represent indirect interactions. The number of arrows denoting significantly over- or under-expressedgenes are quantified using fold change of tumor versus compendium in (a), and primary tumor versus the tumor recurrence in (b): 1 arrow is FC≥2; 2 arrows is FC ≥10; and 3 arrows is FC ≥50. CNV, copy number variation.Jones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 6 of 12tongue is not an approved indication for sunitinib. Thedrug was administered using standard dosing at 50 mg,orally, every day for 4 weeks followed by a planned2 weeks off of the drug. After 28 days on sunitinib and12 days off the patient had a PET-CT scan and this wascompared to the baseline pretreatment scan (Figure 4).Using Response Evaluation Criteria in Solid Tumors(RECIST) criteria, the lung metastases had decreased insize by 22% and no new lesions had appeared. This wasin contrast to the 16% growth seen in the previousmonth prior to initiation of sunitinib and the growthwhile on erlotinib. Because of typical side effects, hisdose of sunitinib was reduced to 37.5 mg daily for4 weeks out of 6. Repeated scanning continued to showdisease stabilization and the absence of new tumornodules for 5 months.Cancer recurrenceAfter 4 months on sunitinib, the patient’s CT scanshowed evidence of growth in the lung metastases. Hewas then switched to sorafenib and sulindac, as thesewere medications that were also thought to be of poten-tial benefit given his initial genomic profiling (Table S1in Additional file 1). Within 4 weeks a CT scan showeddisease stabilization and he continued on these agentsfor a total of 3 months when he began to develop symp-toms of disease progression. At this point he was notedto have developed recurrent disease at his primary siteon the tongue, a rapidly growing skin nodule in theneck, and progressive and new lung metastases. Atumor sample was removed from the metastatic skinnodule and was subjected to both WTSS and genomicsequencing. There were 1,262,856,802 and 5,022,407,10850-bp reads that were aligned from the transcriptomeand genomic DNA, respectively. Nine new non-synon-ymous protein coding changes were detected that werenot present within either the pre-treatment tumor orthe normal DNA in addition to the four somaticchanges determined in the pre-treatment tumor (Table1). Reexamination of the sequence reads from the initialtumor analysis did not reveal the presence of any ofthese nine new mutated alleles even at the single readlevel. Extensive copy number variations were alsoobserved in the post-treatment sample not presentbefore treatment (Figure 1), including the arising ofcopy number neutral regions of LOH on chromosomes4, 7 and 11. In the tumor recurrence, 0.13% of the gen-ome displayed high levels of amplification, compared to0.05% in the initial tumor sample (Table S6 in Addi-tional file 1). Also, 24.8% of the initial tumor showed acopy number loss whereas 28.8% of the tumor recur-rence showed such a loss (Table S6 in Additional file 1).We identified eight regions where the copy number sta-tus changed from a loss to a gain in the tumor recur-rence and twelve regions where the copy numberchanged from a gain to a loss (Table S7 in Additionalfile 1). Indicative of heterogeneity in the tumor sample,the initial tumor showed 18.8% of the genome withincomplete LOH, whereas in the recurrence 15% of thetumor displayed an incomplete LOH signal. In thetumor recurrence 22.2% of the tumor showed a com-plete LOH signal, up from 5.1% in the original tumor(Table S7 Additional file 1). The previous observed pat-tern of focal amplification and loss of 18q in the initialtumor was recapitulated in the tumor recurrence, indi-cating that this specific pattern was reproduciblebetween samples and not likely due to heterogeneity inFigure 3 Fluorescent in situ hybridization (FISH) andimmunohistochemical analysis of the sublingualadenocarcinoma. (a) Hematoxylin and eosin stained section oftumor (20× objective). (b) Striking amplification of RBBP8 (40×, withRBBP8 probe in red). (c) Focal nuclear and cytoplasmic expressionof PTEN (20×) is associated with (d) a missing red signal indicatingmonoallelic loss of PTEN (100×; the orange gene-specific probesignals are decreased in number compared to the centromericprobe). (e) Diffuse, strong cytoplasmic expression of RET (20×) isassociated with (f) amplification of the RET gene (40× with bacterialartificial chromosomes flanking the RET gene labeled in red andgreen).Jones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 7 of 12the original tumor sample (Figure S3b in Additional file1). There were 459 differentially expressed genes (385up-regulated, 74 down-regulated) in the metastatic skinnodule versus the blood/compendium. Of these, 209overlapped with the differentially expressed genes in thelung tumor versus blood/compendium set. In the skinmetastasis relative to lung there were 6,440 differentiallyexpressed genes (4,676 up-regulated, 1,764 down-regu-lated; Additional file 2). The 23 amplified, over-expressed or mutated genes in cancer pathways targeta-ble by approved drugs are listed in Table S3 in Addi-tional file 1. The cancer recurrence exhibited strong up-regulation of transcripts from genes in both the MAPK/ERK and PI3K/AKT pathways (Figure 2b). There arestriking increases in expression of the receptor tyrosinekinases (EGFR, platelet-derived growth factor receptor(PDGFR)B) and their growth factor ligands (epidermalgrowth factor, GFRA1 (GDNF family receptor alpha 1),neurturin (NRTN)). Other genes within these pathways,such as AKT1, MEK1 and PDGFA, also appear amplifiedin copy number in the skin tumor compared to the lungtumor. Sunitinib resistance has been observed to bemediated by IL8 in renal cell carcinoma [37]. This isreflected in the tumor data, where IL8 became highlyover-expressed in the cancer recurrence (FC 861.1 inskin tumor relative to lung tumor). Pathway analysisalso shows IL8 signaling to be significant in the suniti-nib-resistant skin tumor compared to the lung tumor(Table S6 in Additional file 1). Though the mechanismof resistance is still unclear, IL8 has been observed totransactivate EGFR and downstream ERK, stimulatingcell proliferation in cancer cells [38]. Taken together,these data suggest that the mechanisms of resistance tothe RET targeting selective kinase inhibitors sunitiniband sorafenib are the up-regulation of the targetedMAPK/ERK pathway and the parallel PI3K/AKT path-way. We speculate that perhaps only a cocktail of tar-geted drugs (that is, to RET, EGFR, mTOR, and so on)would be able to mitigate the proliferation of the tumorcells.ConclusionsHigh-throughput sequencing of the patient’s tumor andnormal DNA provided a comprehensive determinationof copy number alterations, gene expression levels andprotein coding mutations in the tumor. Correlation ofthe up-regulated and amplified gene products withknown cancer-related pathways provided a putativemechanism of oncogenesis that was validated throughthe successful administration of targeted therapeuticcompounds. In this case, known targets of sunitinib andsorafenib were up-regulated, implying that the tumorwould be sensitive to this drug. Sequence analysis of theprotein coding regions was also able to determine thatthe drug binding sites for sunitinib were intact. Clearly,many other changes have occurred within the tumorthat likely contribute to the pathogenesis of the diseaseand our understanding of cancer biology is far fromcomplete. It is possible, therefore, that these drugs mayhave elicited the observed clinical benefit for reasonsFigure 4 PET-CT scans of the patient. (a) 1 October 2008, 1 month before sunitinib initiation. (b) 29 October 2008, baseline before sunitinibinitiation on 30 October 2008. (c) 9 December 2008, 4 weeks on sunitinib.Jones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 8 of 12unrelated to our hypothesis. However, this analysis didprovide clinically useful information and provided therationale for a therapeutic regime that, whilst not cura-tive, did establish stable disease for several months. Wepropose that complete genetic characterization in thismanner represents a tractable methodology for thestudy of rare cancer types and can aid in the determina-tion of relevant therapeutic approaches in the absenceof established interventions. Furthermore, the establish-ment of repositories containing the genomic and tran-scriptomic information of individual cancers coupledwith their clinical responses to therapeutic interventionwill be a key factor in furthering the utility of thisapproach. We envisage that as sequencing costs con-tinue to decline, whole genome characterization willbecome a routine part of cancer pathology.Materials and methodsFor detailed methodology see Additional file 1. A sum-mary of the sites used for genomic and transcriptomicanalyses is shown in Figure S6 in Additional file 1. Gen-ome sequence data have been deposited at the EuropeanGenome-Phenome Archive (EGA) [39], which is hostedby the European Bioinformatics Institute (EBI), underthe accession number [EBI:EGAS00000000074].Sample preparationTumor DNA was extracted from formalin-fixed, paraf-fin-embedded lymph node sections (slides) using theQiagen DNeasy Blood and Tissue Kit (Qiagen, Missis-sauga, ON, Canada). Normal DNA was prepared fromleukocytes using the Gentra PureGene blood kit as perthe manufacturer’s instructions (Qiagen). Genome DNAlibrary construction and sequencing were carried outusing the Genome Analyzer II (Illumina, Hayward, CA,USA) as per the manufacturer’s instructions. TumorRNA was derived from fine needle aspirates of lungmetastases and normal RNA was extracted from leuko-cytes using Trizol (Invitrogen, Burlington, ON Canada})and the processing for transcriptome analysis was con-ducted as previously described [15,16,40]. The relapsesample was obtained by surgical excision of the skinmetastasis under local anesthetic 5 days after cessationwith sorafenib/sulindac treatment. DNA was extractedusing the Gentra PureGene Tissue kit and RNA wasextracted using the Invitrogen Trizol kit, and the geno-mic library and transcriptome library were constructedas previously described.Mutation detection and copy number analysisDNA sequences were aligned to the human reference,HG18, using MAQ version 0.7.1 [41]. To identify muta-tions and quantify transcript levels, WTSS data werealigned to the genome and a database of exon junctions[15]. SNPs from the tumor tissue whole genome shot-gun sequencing and WTSS were detected using MAQSNP filter parameters of consensus quality = 30 anddepth = 8 and minimum mapping quality = 60. Allother parameters were left as the default settings. Addi-tional filters to reduce false positive variant callsincluded: the base quality score (MAQ qcal) of a varianthad to be ≥20; and at least one-third of the reads at avariant position were required to possess the variantbase pair. SNPs present in dbSNP [42] and establishedindividual genomes [9,43,44] were subtracted as well asthose detected in the normal patient DNA. SNPs pre-sent in the germline sample (blood) were detected usingMAQ parameters at lower threshold of consensusquality = 10 and depth = 1 and minimum mappingquality = 20 in order to reduce false positive somaticmutations. Initially, non-synonymous coding SNPs wereidentified using Ensembl versions 49 and 50; theupdated analysis presented here used version 52_36n.Candidate protein coding mutations were validated byPCR using primers using either direct Sanger sequen-cing or sequencing in pools on an Illumina GAiix. Inthe latter case, amplicons were designed such that theputative variant was located within the read length per-formed (75 bp). For copy number analysis, sequencequality filtering was used to remove all reads of lowsequence quality (Q ≤ 10). Due to the varying amountsof sequence reads from each sample, aligned referencereads were first used to define genomic bins of equalreference coverage to which depths of alignments ofsequence from each of the tumor samples were com-pared. This resulted in a measurement of the relativenumber of aligned reads from the tumors and referencein bins of variable length along the genome, where binwidth is inversely proportional to the number ofmapped reference reads. A HMM was used to classifyand segment continuous regions of copy number loss,neutrality, or gain using methodology outlined pre-viously [45]. The sequencing depth of the normal gen-ome provided bins that covered over 2.9 gigabases ofthe HG18 reference. The five states reported by theHMM were: loss (1), neutral (2), gain (3), amplification(4), and high-level amplification (5). LOH informationwas generated for each sample from the lists of genomicSNPs that were identified through the MAQ pipeline.This analysis allows for classification of each SNP aseither heterozygous or homozygous based on thereported SNP probabilities. For each sample, genomicbins of consistent SNP coverage are used by an HMMto identify genomic regions of consistent rates of het-erozygosity. The HMM partitioned each tumor genomeinto three states: normal heterozygosity, increasedhomozygosity (low), and total homozygosity (high). Weinfer that a region of low homozygosity represents aJones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 9 of 12state where only a portion of the cellular populationhad lost a copy of a chromosomal region.Gene expression analysisTranscript expression was assessed at the gene levelbased on the total number of bases aligning to Ensembl(v52) [46] gene annotations. The corrected and normal-ized values for tumor gene expression (both skin andlung metastases) were then used to identify genes differ-entially expressed with respect to the patient’s germline(blood) and a compendium of 50 previously sequencedWTSS libraries. This compendium was composed of 19cell lines and 31 primary samples representing at least 19different tissues and 25 tumor types as well as 6 normalor benign samples (Table S4 in Additional file 1). Tumorversus compendium comparisons used outlier statisticsand tumor versus blood used Fisher’s exact test. We firstfiltered out genes with less than 20% non-zero dataacross the compendium. This was necessary to avoidcases where a small expression value in the tumorreceives an inflated rank when all other libraries reportedzero expression (a problem common to sequencing-based expression techniques when libraries have insuffi-cient depth). Next, we defined over-expressed genes asthose with outlier and Fisher P-values < 0.05 and FC fortumor versus compendium and tumor versus blood > 2and > 1.5, respectively. Similar procedures were used todefine under-expressed genes. In addition to lung/skinmetastasis versus compendium/normal blood we alsocompared the skin and lung metastases directly. Pathwayanalysis was performed for all gene lists using the Inge-nuity Pathway Analysis software [47] (Table S5 in Addi-tional file 1). P-values for differential expression andpathways analyses were corrected with the Benjamini andHochberg method [48]. Overlaps were determined withthe BioVenn web tool [49].Additional materialAdditional file 1: Supplementary methods, tables and figures.Additional file 2: Supplementary expression data for identificationof differentially expressed genes.Abbreviationsbp: base pair; EGFR: epidermal growth factor receptor; ERK: extracellularsignal-regulated kinase; FC: fold change; HMM: hidden Markov model; IL:interleukin; LOH: loss of heterozygosity; MAPK: mitogen-activated proteinkinase; mTOR: mammalian target of rapamycin; PET-CT: positron emissiontomography-computed tomography; PI3K: phosphoinositide 3-kinase; PTEN:phosphatase and tensin homolog; SNP: single-nucleotide polymorphism;WTSS: whole transcriptome shotgun sequencing.AcknowledgementsSJMJ, RAH and MAM are scholars of the Michael Smith Foundation forHealth Research. We thank Dr Simon Sutcliffe for helpful discussion in theexperimental design and Dr Joseph Connors for critical reading of themanuscript. We acknowledge the expert technical assistance of the staffwithin the Library preparation and DNA sequencing groups at the GenomeSciences Centre.Author details1Genome Sciences Centre, British Columbia Cancer Agency, 570 West 7thAvenue, Vancouver, BC, V5Z 4S6, Canada. 2British Columbia Cancer Agency,600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada. 3Vancouver GeneralHospital, West 12th Avenue, Vancouver, BC, V5Z 1M9, Canada. 4Centre forTranslational and Applied Genomics of British Columbia Cancer Agency andthe Provincial Health Services Authority Laboratories, 600 West 10th Avenue,Vancouver, V5Z 4E6, BC, Canada. 5Molecular Oncology, BC Cancer ResearchCentre, 601 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.Authors’ contributionsSJMJ, JL, and MAM participated in experimental design, analysis and draftedthe manuscript. YYL, OLG, YSB, RC and IB undertook analysis and aided inmanuscript preparation. JA, MB, TC, EC, AF, MG, RDM, SPS, NT and RVcontributed to the computational analysis. JY, MM, NM, MS, JT, TT, and DGHcontributed to the clinical assessment of the tumor material. MM, TJP, TS,AT, TZ, YZ, RAM, MH and RAH conducted the molecular biology processingand sequencing of the clinical samples. All authors read and approved thefinal manuscript.Competing interestsThe authors declare that they have no competing interests.Received: 12 April 2010 Revised: 8 July 2010 Accepted: 9 August 2010Published: 9 August 2010References1. Krizman DB, Wagner L, Lash A, Strausberg RL, Emmert-Buck MR: The CancerGenome Anatomy Project: EST sequencing and the genetics of cancerprogression. Neoplasia 1999, 1:101-106.2. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of geneexpression. Science 1995, 270:484-487.3. 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Terry J, Saito T, Subramanian S, Ruttan C, Antonescu CR, Goldblum JR,Downs-Kelly E, Corless CL, Rubin BP, van de Rijn M, Ladanyi M, Nielsen TO:TLE1 as a diagnostic immunohistochemical marker for synovial sarcomaemerging from gene expression profiling studies. Am J Surg Pathol 2007,31:240-246.51. Terry J, Barry TS, Horsman DE, Hsu FD, Gown AM, Huntsman DG,Nielsen TO: Fluorescence in situ hybridization for the detection of t(X;18)(p11.2;q11.2) in a synovial sarcoma tissue microarray using a breakapart-style probe. Diagn Mol Pathol 2005, 14:77-82.52. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ,Marra MA: Circos: an information aesthetic for comparative genomics.Genome Res 2009, 19:1639-1645.doi:10.1186/gb-2010-11-8-r82Cite this article as: Jones et al.: Evolution of an adenocarcinoma inresponse to selection by targeted kinase inhibitors. Genome Biology 201011:R82.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitJones et al. Genome Biology 2010, 11:R82http://genomebiology.com/2010/11/8/R82Page 12 of 12


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