Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Trinh, Huong Ly
Master of Science - MSc
University of British Columbia
Background: Metabolomics offers a promising approach to identifying biomarkers for timely intervention and enhanced screening for individuals at risk of breast cancer. In this nested case-control study, we used self-reported risk factors and prospectively collected blood samples to identify metabolites associated with breast cancer and develop predictive models to assess population risk profiles. Methods: Participants from two Canadian prospective cohorts, BC Generations Project and Alberta’s Tomorrow Project, completed baseline health and lifestyle questionnaires and donated blood samples at enrollment. 593 women who subsequently developed breast cancer cases were matched one-to-one with healthy controls based on cohort, age at blood collection, time of blood collection, and menopause status. Quadrupole time-of-flight mass spectrometry was used for untargeted metabolomics profiling of baseline serum samples. Logistic regression was used to estimate odds ratios corresponding to a one standard deviation increase of metabolite levels after adjusting for matching variables and selected health and lifestyle risk factors. Associations were also assessed for matched pairs with postmenopausal cancer, invasive ductal carcinoma, and hormone receptor-positive cancer. Five classifiers – lasso, random forest (RF), and partial least squares discriminant analysis (PLS-DA), and support vector machines (SVMs) with linear and radial basis functions – were used to develop risk prediction models. Results: 24 metabolites were significantly associated with risk of developing breast cancer, including 13 associated with decreased risk and 11 associated with increased risk. No associations were attenuated by more than 10% when adjusted for risk factors. Among the 24 metabolites, associations with postmenopausal breast cancer, invasive ductal carcinoma, and ER+/PR+ breast cancer were found for 10, 10, and 4 metabolites, respectively. Prediction models using these metabolites had moderate discrimination, with highest AUC of 0.63. Lasso and SVM with linear basis function outperformed the other three algorithms. Conclusion: Overall, our results suggest metabolomics data shows potential as breast cancer biomarkers. More studies are needed to confirm the identities of detected metabolites and validate their associations with breast cancer risk. Future analyses that incorporate larger sample sizes and longitudinal samples will further provide more insights into the metabolic mechanisms surrounding cancer development.
THE SERUM METABOLOME AND BREAST CANCER RISK by Huong Ly Trinh B.A., Saint Olaf College, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Huong Ly Trinh, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The Serum Metabolome and Breast Cancer Risk submitted by Huong Ly Trinh in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Dr. Parveen Bhatti, Associate Professor, School of Population and Public Health, UBC Supervisor Dr. Andrew Roth, Assistant Professor, Departments of Computer Science and Pathology and Laboratory Medicine, UBC Supervisory Committee Member Dr. Rachel Murphy, Associate Professor, School of Population and Public Health, UBC Supervisory Committee Member Dr. Amee Manges, Professor, School of Population and Public Health, UBC Additional Examiner iii Abstract Background: Metabolomics offers a promising approach to identifying biomarkers for timely intervention and enhanced screening for individuals at risk of breast cancer. In this nested case-control study, we used self-reported risk factors and prospectively collected blood samples to identify metabolites associated with breast cancer and develop predictive models to assess population risk profiles. Methods: Participants from two Canadian prospective cohorts, BC Generations Project and Alberta’s Tomorrow Project, completed baseline health and lifestyle questionnaires and donated blood samples at enrollment. 593 women who subsequently developed breast cancer cases were matched one-to-one with healthy controls based on cohort, age at blood collection, time of blood collection, and menopause status. Quadrupole time-of-flight mass spectrometry was used for untargeted metabolomics profiling of baseline serum samples. Logistic regression was used to estimate odds ratios (OR) corresponding to a one standard deviation increase of metabolite levels after adjusting for matching variables and selected health and lifestyle risk factors. Associations were also assessed for matched pairs with postmenopausal cancer, invasive ductal carcinoma, and hormone receptor-positive cancer. Five classifiers – lasso, random forest (RF), and partial least squares discriminant analysis (PLS-DA), and support vector machines (SVMs) with linear and radial basis functions – were used to develop risk prediction models. Results: 24 metabolites were significantly associated with risk of developing breast cancer, including 13 associated with decreased risk and 11 associated with increased risk. No associations were attenuated by more than 10% when adjusted for risk factors. Among the 24 metabolites, associations with postmenopausal breast cancer, invasive ductal carcinoma, and ER+/PR+ breast cancer were found for 10, 10, and 4 metabolites, respectively. Prediction models using these metabolites had moderate discrimination, with highest AUC of 0.63. Lasso and SVM with linear basis function outperformed the other three algorithms. iv Conclusion: Overall, our results suggest metabolomics data shows potential as breast cancer biomarkers. More studies are needed to confirm the identities of detected metabolites and validate their associations with breast cancer risk. Future analyses that incorporate larger sample sizes and longitudinal samples will further provide more insights into the metabolic mechanisms surrounding cancer development. v Lay summary The objective of this project was to identify small molecules circulating in the bloodstream, or metabolites, that are associated with risk of developing breast cancer, and develop models that use these metabolites to predict future occurrence of breast cancer. We found that breast cancer risk was associated with abnormal levels of several lipids, amino acids, steroid hormones, and exogenous metabolites. Prediction models using these metabolites were moderately successful in predicting future occurrence of breast cancer. Larger scale studies are needed to validate our findings and develop models for specific subtypes of breast cancer. vi Preface This study was approved by the BC Cancer/University of British Columbia Research Ethics Board (certificate number H21-03002). Data was collected by the BC Generations Project (BCGP) and Alberta’s Tomorrow Project (ATP). The work presented within this thesis was conducted under the supervision of Dr. Parveen Bhatti, with guidance from supervisory committee members Dr. Andrew Roth and Dr. Rachel Murphy. The ethics application and data requests were completed by Dr. Bhatti. BCGP data linkage and cleaning were conducted by Jessica Chu, BCGP Data Coordinator, and Jaclyn Parks, BCGP Methodologist and Access Officer. ATP data were prepared by Cara McGinley and Ashley Brown, Data Analysts, and delivered by Laura Grant, ATP Access Officer. Serum samples were prepared for shipment by Treena MacDonald, BCGP Biosamples Project Manager. Metabolomics profiling was completed by General Metabolics LLC (Boston, MA). The study design was conceived by Drs. Bhatti, Murphy, and Roth, with input from Ly Trinh. Literature review and statistical analyses were conducted by Ly Trinh, with assistance from Dr. Bhatti and Dr. Roth. Writing was completed by Ly Trinh and reviewed by the committee members. vii Table of contents Abstract .................................................................................................................................................... iii Lay summary ............................................................................................................................................ v Preface ..................................................................................................................................................... vi Table of contents ..................................................................................................................................... vii List of tables ............................................................................................................................................. ix List of figures ............................................................................................................................................ x List of abbreviations ................................................................................................................................ xi Acknowledgements ................................................................................................................................ xiii Dedication .............................................................................................................................................. xiv Chapter 1: Introduction ............................................................................................................................. 1 1.1 Risk factors ..................................................................................................................................... 1 1.2 Breast cancer subtypes .................................................................................................................... 4 1.3 Risk prediction ................................................................................................................................ 5 1.4 Metabolomics and breast cancer risk prediction ............................................................................. 8 1.5 Research objectives ....................................................................................................................... 11 Chapter 2: Methods ................................................................................................................................. 12 2.1 Study population ........................................................................................................................... 12 2.2 Study design .................................................................................................................................. 12 2.3 Health and lifestyle data ................................................................................................................ 13 2.4 Metabolomics data ........................................................................................................................ 14 2.5 Statistical analysis ......................................................................................................................... 16 Chapter 3: Results ................................................................................................................................... 20 3.1 Participant characteristics ............................................................................................................. 20 3.2 Metabolite data .............................................................................................................................. 21 viii 3.3 Associations with breast cancer .................................................................................................... 21 3.4 Breast cancer risk prediction ......................................................................................................... 22 Chapter 4: Discussion ............................................................................................................................. 25 4.1 Metabolite associations with breast cancer risk ............................................................................ 25 4.2 Breast cancer risk prediction ......................................................................................................... 31 4.3 Strengths and limitations ............................................................................................................... 31 4.4 Future research and applications ................................................................................................... 33 Chapter 5: Conclusion ............................................................................................................................. 35 Tables ...................................................................................................................................................... 36 Figures .................................................................................................................................................... 46 Bibliography ........................................................................................................................................... 49 Appendix ................................................................................................................................................. 69 ix List of tables Table 1. Characteristics of study population ............................................................................................... 36 Table 2. Metabolites associated with breast cancer risk (q<0.1) ................................................................ 40 Table 3. Metabolite associations with postmenopausal breast cancer risk ................................................. 41 Table 4. Metabolites associations with invasive ductal carcinoma risk ...................................................... 42 Table 5. Metabolites associations with ER+/PR+ cancer risk .................................................................... 43 Table 6. AUC of risk prediction results for all participants and subgroups ................................................ 44 Table 7. Variable importance ranking for each classifier ........................................................................... 45 x List of figures Figure 1. PCA plots of study samples ......................................................................................................... 46 Figure 2. RLE plots for 200 random samples ............................................................................................. 47 Figure 3. Performance of breast cancer prediction ..................................................................................... 48 xi List of abbreviations ATP Alberta’s Tomorrow Project AUC Area under the Receiver Operating Characteristics Curve BC British Columbia BCGP British Columbia Generations Project BCRAT Breast Cancer Risk Assessment Tool BMI Body Mass Index BOADICEA Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm CanPath Canadian Partnership for Tomorrow’s Health ChEBI Chemical Entities of Biological Interest CI Confidence Interval CV Coefficient of Variation DHEA/S Dehydroepiandrosterone and Sulfate Metabolite ER Estrogen Receptor FDR False Discovery Rate GC-MS Gas Chromatography-Mass Spectrometry GMet General Metabolics LLC HER2 Human Epidermal Growth Factor Receptor 2 HMDB Human Metabolome Database HRT Hormone Replacement Therapy IBIS International Breast Cancer Intervention Study IDC Infiltrating Ductal Carcinoma ILC Infiltrating Lobular Carcinoma KEGG Kyoto Encyclopedia of Genes and Genomes xii LASSO Least Absolute Shrinkage and Selection Operator LC-MS Liquid Chromatography-Mass Spectrometry MAGL Monoacylglycerol lipase MICE Multiple Imputation by Chained Equations MS Mass Spectrometry NCCN National Comprehensive Cancer Network OBSP Ontario Breast Screening Program OR Odds Ratio PCA Principal Component Analysis PE Phosphatidylethanolamine PLS-DA Partial Least-Squares Discriminant Analysis PR Progesterone Receptor PSE Passive Smoking Exposure Q-TOF Quadrupole Time-of-Flight RF Random Forest RLE Relative Log Expression SD Standard Deviation SVM Support Vector Machine TNBC Triple-Negative Breast Cancer xiii Acknowledgements I would like to express my utmost appreciation to my supervisor, Dr. Parveen Bhatti, for his tremendous guidance throughout my program. Thank you to my supervisory committee members, Dr. Rachel Murphy and Dr. Andrew Roth, for their support on my thesis. I am grateful to the teams at BCGP and ATP – especially Dr. Bhatti, Jaclyn Parks, Jessica Chu, Treena MacDonald, Laura Game, Laura Grant, Will Rosner, Cara McGinley, and Ashley Brown – for facilitating all communications, data access, and logistics behind this project. BC Generations Project is only possible because of the commitment of its research participants, its staff and its funders: BC Cancer Agency, BC Cancer Foundation, Canadian Partnership Against Cancer and Health Canada. Cancer registry data was obtained through linkage with the BC Cancer Surveillance & Outcomes, Data and Analytics. Alberta’s Tomorrow Project is only possible because of the commitment of its research participants, its staff and its funders: Alberta Health, Alberta Cancer Foundation, Canadian Partnership Against Cancer and Health Canada, and substantial in kind funding from Alberta Health Services. Cancer registry data was obtained through linkage with Surveillance & Reporting, Cancer Research & Analytics, Cancer Care Alberta. The views expressed herein represent the views of the author(s) and not of BC Generations Project, Alberta’s Tomorrow Project or any of their funders. xiv Dedication I dedicate this work to my family for making me. To my Canadasians – Allyson, Cindy, and Hiro – for your friendship from the start. To Hoda, for the countless video calls that kept me sane. And to Ahmad, for being my rock through this entire process and beyond. 1 Chapter 1: Introduction Breast cancer is the most prevalent cancer among women in Canada, with one in eight women projected to be diagnosed with the disease in their lifetime (1). Breast cancer is most commonly diagnosed between the ages of 50 and 79, with only 17% of new cases diagnosed among women under age 50 (1). Over the past three decades, breast cancer incidence has remained stable while mortality rates have steadily declined (1). Nonetheless, prognosis remains poor for cancers diagnosed at later stages; five-year survival for stage IV breast cancer is only 23% (2). In addition, risks of recurrence and second primary cancers are elevated among survivors (3,4). Adverse health effects associated with breast cancer and its treatment also negatively impact quality of life (5). Women with breast cancer have increased risks of osteoporosis, lymphedema, respiratory and urinary infections, sleep disorders, depression, anxiety, and neurocognitive dysfunction (5–8). In light of these challenges, prevention of breast cancer is critical. 1.1 Risk factors 1.1.1 Demographic factors Risk of breast cancer increases with age: the 10-year probability of being diagnosed with the disease rises from 0.5% at age 30 to 4.1% at age 70, increasing by an average of 0.9% per decade (9). Compared to women with a high school education or lower, women with a university education or higher have a 22% greater risk of developing breast cancer (10,11). Lower income is associated with a 5-15% reduction in risk, but not with survival (10,12–16). Women of white non-Hispanic ethnicity have the highest incidence of breast cancer (1,9). Some subgroups with European ancestries, such as Ashkenazi Jews and French Canadians, are more likely to carry genetic mutations that increase the risk of breast cancer (17,18). There is some limited evidence suggesting that in Canada, South Asian women are more likely to develop more aggressive subtypes of 2 the disease that are diagnosed at later stages (15,19,20). Overall, the effects of ethnicity and demographic factors are attenuated, but not diminished, when accounting for other factors that have been associated with breast cancer, such as low parity and late childbirth, hormone replacement therapy, and alcohol consumption (11,16,21,22). 1.1.2 Hereditary factors Compared to having no female first-degree relatives with a history of breast cancer, having one, two, and three affected female first-degree relatives increases the risk of developing the disease by 1.8, 2.9, and 3.9 times, respectively (23). Up to 25% of familial breast cancers are attributable to genetic mutations; mutations in BRCA1 and BRCA2 account for approximately 15-17% of cases, and a smaller proportion can be attributed to other rare but highly penetrant mutations in genes such as PTEN, TP53, and CDH1 (24–29). 1.1.3 Reproductive factors Early menarche and late menopause are associated with higher breast cancer risk: every year younger at menarche increases risk by 5%, while every year older at menopause increases risk by 2.9% (23). Parous women have a 34% reduced risk of developing breast cancer compared to nulliparous women, and among parous women, increasing parity decreases risk (30). Earlier age at first full-term pregnancy is protective against breast cancer: women who have their first full-term pregnancy at age 20 or younger have about half the risk of those whose first full-term pregnancy is at age 30 or older (31,32). Every 12 months of breastfeeding reduces breast cancer risk by 4.3% (33). Current use of combined estrogen and progestin and progestin-only oral contraceptives is associated with 24% and 26% increased risks of breast cancer, respectively (34–38). This effect diminishes 10 years after discontinuation of oral contraceptives. Similar associations have been observed for other forms of 3 hormonal birth control, such as injections and intrauterine devices (37,38). Hormone replacement therapy (HRT) increases cancer risk by approximately 2.3% per year of use (39). The effect varies with type of therapy: compared to HRT non-users, estrogen therapy increases risk by 17% in years 1-4 and 33% in years 5-14, while estrogen combined with progestin increases risk by 60% in years 1-4 and 108% in years 5-14 (40). Past users, especially those who used HRT for 10 or more years, have an elevated risk of developing breast cancer even more than a decade after discontinuing treatment (40). 1.1.4 Lifestyle factors Obesity has been consistently linked with breast cancer risk with associations differing by menopause status (41). Each five-unit increase in BMI is associated with a 12% greater risk of breast cancer among postmenopausal women and an 8% decreased risk of breast cancer in premenopausal women (42). Alcohol consumption has been consistently shown to elevate breast cancer risk (43–47). Current light, moderate, and heavy drinkers have 4%, 23%, and 61% increased risks of developing breast cancer, respectively, compared to non-drinkers (47,48). Each additional 10g of alcohol intake per day - or one extra drink per day - is associated with a 7-10% greater risk of breast cancer (45,46). Binge drinking (defined as having four or more drinks per occasion for females) increases breast cancer risk by 29% (49,50). Ever smoking and current smoking are associated with 9 and 11% increased risks of breast cancer, respectively (51). Longer smoking durations, smoking more than 20 pack-years, smoking before first full-term pregnancy, and early smoking initiation are all associated with significant increases in risk (51–55). Passive smoke exposure (PSE) has been associated with 16-27% increased risks in breast cancer among non-smokers (51,52,56). 4 1.2 Breast cancer subtypes Breast cancer is a heterogeneous disease. It is well-recognized that breast cancer subtypes vary in their clinicopathological presentations and outcomes (57,58). In addition to differing clinical trajectories, research has suggested that associations with breast cancer risk factors differ by subtype (59,60). 1.2.1 Histological subtypes Approximately 70-85% of all invasive breast cancers are infiltrating ductal carcinomas (IDCs), which originate from the milk ducts of the breast (61–64). Approximately 10-15% of cases are infiltrating lobular carcinomas (ILC), which arise in glands that produce milk (61–63,65). The remaining subtypes, such as medullary, mucinous, papillary, and metaplastic, are relatively rare, but have distinct pathological and clinical features (66,67). Mutations in BRCA1 and TP53 are predominantly associated with IDC, while mutations in CDH1 are exclusively associated with ILC (59,68). HRT is associated with increased breast cancer risk for both subtypes, but users of combined estrogen-progestin therapy, especially continuous users for five or more years, have a greater increase in ILC than IDC risk (59,69). Early age at menarche, late age at first birth, and late age at menopause are also associated with greater risk of ILC than IDC (59). 1.2.2 Molecular subtypes Breast cancer is also classified according to expression of hormone receptors (70–72). Luminal breast cancers, which are characterized by high expression of estrogen receptors (ER+) and/or progesterone receptors (PR+), and low expression of human epidermal growth factor receptor 2 (HER2-), are the most common type of breast cancer, making up 60-75% of all tumors (9,71,73,74). Luminal cancers are generally low-grade, slow-growing tumors with favorable prognosis (75). HER2+ cancers, which are characterized by high HER2 expression and low ER/PR expression, account for 10-20% of all breast 5 cancers (73,74,76–78). The overexpression of HER2 often results in faster-growing tumors, poorer survival, and higher frequencies of relapse and metastasis (75). Triple-negative breast cancers (TNBCs), which are characterized by the lack of expression of ER, PR, and HER2 constitute about 10-20% of all breast cancers (58,74,78,79). TNBCs are poorly differentiated, highly proliferative, and advanced at time of diagnosis, leading to poor prognosis (75). Luminal tumors are associated with older age at diagnosis, postmenopausal status, white non-Hispanic ethnicity, and higher socioeconomic status, while TNBC tumors occur more often in younger, premenopausal patients of African ancestry (80–83). Having a family history of breast cancer elevates risk across all subtypes, although family history is more frequently observed in patients with core basal-phenotype tumors, a type of TNBC to which BRCA1 carriers are particularly prone (74,84). Breast cancers with a TP53 mutation are more likely to be HER2+ (85). Greater luminal breast cancer risk is associated with nulliparity, older age at first birth, and lower number of births among parous women (74,86). There is suggestive evidence that younger age at menarche, older age at menopause, and use of HRT also increases risk of luminal breast cancer (86). TNBC is associated with younger age at menarche and use of oral contraceptives (74). Obesity is associated with decreased risk of TNBC among women under the age of 50 (74,86). Luminal breast cancer, on the other hand, is significantly associated with obesity among women over the age of 50 (74,86). Alcohol use is suggestively associated with increased risk of luminal and HER2+ cancers (86,87). 1.3 Risk prediction 1.3.1 Interventions to modify risk factors Prophylactic bilateral mastectomy may be recommended for women with a strong family history of breast cancer or with high-penetrance breast cancer gene mutations (88). For high-risk women who are not good 6 candidates for surgery, chemopreventive agents, including selective ER modulators and aromatase inhibitors, may be efficacious alternatives (89,90). However, these medications are only associated with reductions in ER+ breast cancer risk, need to be taken daily for five years, and can double the risk of endometrial cancer and thrombosis among users. Uptake and adherence are often low due to side effects and a lack of familiarity among physicians in implementing chemoprevention as part of clinical care (91). In the general population, interventions to reduce breast cancer risk are challenging to deliver (92). For example, among postmenopausal overweight and obese women, weight loss is associated with breast cancer risk reduction (93,94). However, weight loss interventions tend to have high dropout rates, and most participants have little success in sustaining weight loss beyond the duration of the program (95). Adherence – a critical predictor of long-term success – is significantly improved in programs with close monitoring, social support, or regimens tailored to individual preferences, but such measures are resource-intensive and difficult to scale (95,96). A more effective strategy to reduce the population-burden of breast cancer may be to focus efforts on identifying women with the highest likelihood of developing the disease. These women could then be targeted with resource intensive behavioral and clinical interventions to reduce breast cancer risk, in addition to enhanced screening activities to detect and treat breast cancer as early as possible (97). 1.3.2 Breast cancer risk prediction Multiple statistical models have been developed in the past three decades to predict breast cancer risk. Risk prediction models traditionally use regression-based methods to assign weights to known risk factors and quantify the probability of developing breast cancer in an individual’s lifetime. The predicted risk scores are then used to stratify populations into low-, moderate-, and high-risk categories so that interventions can be targeted to each stratum. The most common statistic to validate model performance and compare competing models is the concordance statistic, also referred to as the area under the receiver 7 operating characteristic (AUC). An AUC of 0.5 indicates that the model does not discriminate between people who develop the condition from those who do not; in other words, that the model does not perform better than random chance. An AUC of 1.0, on the other hand, indicates perfect separation. It is generally accepted that an AUC < 0.60 indicates poor discrimination, an AUC 0.60-0.75 indicates moderate discrimination, and an AUC > 0.75 indicates high discrimination (98). Below is a summary of three existing breast cancer risk models that are most commonly recommended for identifying women at risk of breast cancer. Other risk prediction models have been developed from different cohorts around the world but have not undergone similar levels of validation (99–103). The Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model, is a logistic regression model published in 1989 using age, age at menarche, history of breast diseases and biopsies, age at first birth, and family history to predict risk of breast cancer among Caucasian women ages 20-79 (104). At least eight subsequent studies validated the initial model in different cohorts in the US and Europe, and added more risk factors such as BMI, alcohol consumption, and HRT use (105–112). The United States’ National Comprehensive Cancer Network (NCCN) recommends the use of the BCRAT model to identify high-risk women for chemoprevention (113). The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) was developed and updated by Antoniou et al (114–116). This model utilizes Cox proportional hazards regression to estimate age-specific breast cancer incidence rate using information on major breast and ovarian cancer susceptibility genes (114–116). Later additions to the model included mammographic density, hormonal and reproductive events, and lifestyle factors (117,118). The BOADICEA system is also recommended for familial risk screening by the NCCN, as well as Canada’s Ontario Breast Screening Program (OBSP) and the United Kingdom’s National Institute for Health and Care Excellence and the European Society of Medical Oncology (113,119–122). 8 The International Breast Cancer Intervention Study (IBIS) model, also known as the Tyrer-Cuzick model, is based on a Bayesian statistical analysis of data from a multiethnic cohort study (123). In addition to age, reproductive events, family history, previous breast diseases, and body mass index (BMI) - the IBIS model and subsequent validations also include presence of BRCA gene mutations (123–125). Guidelines from OBSP and the US Preventive Services Task Force suggest that physicians use the IBIS model and other risk assessment tools to refer patients with hereditary risk factors for genetic counseling (120,126). Despite extensive validation and modification, these risk models only have low to moderate discriminatory accuracy, with AUCs of 0.59-0.71 (127). In addition, modifications prevent these risk assessment models from being routinely used in clinical care. More recently, machine learning models have been shown to outperform traditional risk models when validated on the same data set, but most models still achieve only moderate accuracy (average AUC 0.73, range 0.61-0.89) (128–132). Low predictive performance implies that models may miss a substantial proportion of high-risk women, while some low-risk women may be subject to unnecessary resource-intensive interventions. Adding mammographic features and biomarkers such as single nucleotide polymorphisms have contributed only minor improvements in predictive accuracy (127,132). 1.4 Metabolomics and breast cancer risk prediction Metabolomics involves the analysis of low-molecular-weight molecules, or metabolites, in biological samples. Metabolites can be isolated from biofluids such as blood, urine, saliva, and sweat, and measured through high-throughput platforms, mainly mass spectrometry or nuclear magnetic resonance spectroscopy (133). Metabolomics is a highly promising approach to predicting cancer risk since the metabolome provides a functional readout of genetic and environmental interactions. As the downstream process of DNA transcription results in metabolites, the metabolome is largely defined by the genome, and alterations in gene expression profile (134). Metabolism is also affected by biological processes 9 associated with age, sex, hormones, diet, and other lifestyle and environmental factors (135–138). Therefore, modifications of physiological mechanisms that would lead to the initiation and progression of cancer may be reflected in changes to the metabolome. Until recently, metabolomics studies have relied on post-diagnostic samples, which are likely affected by cancer treatment and thus not reliably reflective of pre-diagnostic risk. Researchers have now started to take advantage of existing samples collected from asymptomatic individuals before disease occurrence to determine pre-diagnostic risk profiles more accurately (139). Recent technological advances have also improved detection limits and significantly reduced sample processing times, allowing for high-speed profiling of thousands of metabolites in large samples within the same batch (140,141). Machine learning algorithms with the capacity to handle high-dimensional data are also becoming more common in metabolomics studies (142). Multiple studies of metabolomics, measured in pre-diagnostic blood samples, and breast cancer risk have been recently published (143–163). For example, in an American study of 782 cases and controls matched by birth date, race/ethnicity, and date of blood draw, 24 of 1,275 measured metabolites were significantly associated with breast cancer after correction for multiple testing (157). Comparing the highest to lowest tertile of metabolite concentrations, cysteine, a major product of amino acid metabolism, had the strongest positive association (odds ratio [OR] = 1.74, 95% confidence interval [CI]: 1.21-2.52) (157). Positive associations were also found for four androgens and two glycerophosphocholines (GPCs) (157). Most of the identified metabolites that were inversely associated with breast cancer were lipids: 3,4-methylene heptanoyl carnitine had the strongest inverse association (OR = 0.58, CI: 0.42-0.81) (157). Pre-diagnostically measured metabolites associated with breast cancer in more than one study include: amino acids such as glutamine, glycine, and arginine; lipids such as stearic acid, palmitic acid, and linoleic acid; and the estrogen precursor dehydroepiandrosterone and its sulfate metabolite (DHEA/S). Methods for preprocessing and statistical testing varied considerably between studies, which may increase 10 heterogeneity and limit generalizability in results. Only two studies reported breast cancer risk prediction results. One study of 791 matched cases and controls evaluated 43 metabolites, which demonstrated low prediction accuracy for breast cancer risk (AUC = 0.51) (151). A second study, which evaluated 129 metabolites among 419 cases and controls, achieved high predictive performance with AUC of 0.89 (146). 11 1.5 Research objectives The primary objectives of this study were to: 1. Investigate associations between pre-diagnostic metabolite measures and subsequent breast cancer risk 2. Identify combinations of metabolites and known risk factors that are predictive of breast cancer risk Secondary objectives included exploring whether associations with metabolites differed by menopause status and major histological and molecular subtypes of breast cancer. 12 Chapter 2: Methods 2.1 Study population The study was approved by the BC Cancer/University of British Columbia Research Ethics Board (certificate number H21-03002). Data for the study came from two prospective longitudinal population cohorts, the BC Generations Project (BCGP) and Alberta’s Tomorrow Project (ATP). BCGP consists of approximately 30,000 participants aged 35-69 years, recruited from across British Columbia between 2009 and 2016. ATP consists of approximately 52,000 participants aged 35-69 years, recruited from across Alberta between 2000 and 2015 (164,165). The two cohorts are part of the Canadian Partnership for Tomorrow’s Health (CanPath) through which data collected from participants have been harmonized (166). Participants completed baseline and follow-up health and lifestyle questionnaires. These questionnaires collected health and reproductive history, social demographic characteristics, and behavioral factors. Participants also provided height and weight measurements, either via in-person assessments at a study center or through self-report (166). Participants also provided blood samples, which were divided into aliquots of plasma, serum, buffy coat, and red blood cells and stored at -80°C (167–169). 2.2 Study design This study was a nested case-control design within ATP and BCGP. Participants were eligible to be included in the current study if they were female, provided blood samples at time of recruitment, completed a questionnaire at enrollment, and were cancer-free at time of enrollment. A total of 17,921 BCGP and 18,574 ATP participants met these eligibility criteria. Among these participants, cases were selected if they were diagnosed with incident invasive breast cancer after enrolment. Incident invasive breast cancer cases were identified through annual linkage with each province’s respective Cancer Registry (International Classification of Diseases for Oncology, Third Edition site code: C50.x; 13 morphology codes: 80103, 80323, 80413, 81403, 82003, 82113, 82553, 84013, 84803, 85003, 85033, 85043, 85073, 85203, 85223, 85233, 85243, 85403, 84413, 85753, 89833). Based on morphology codes, cases were classified into four histological subgroups: ductal (85003, 85043, 85073), lobular (85203), mixed ductal/lobular (8522, 8523, 8524, 8255), and other carcinomas (170). In addition, cases with known hormone receptor expression status were categorized as: triple-negative, HER2-positive (regardless of ER/PR status), and ER/PR-positive but HER2-negative (72). Each case was matched to a single control, who was cancer free at the time of case diagnosis except for nonmelanoma skin cancer, on source cohort (to limit impact of systematic differences between the cohorts on study results), baseline menopause status (to limit confounding effects), age at blood collection (± 2 years) (to limit confounding effects), and year of blood collection (± 2 years) (to limit impact of sample storage time on study results). If menopause status was missing on the baseline questionnaire, but self-reported as premenopausal on the follow-up questionnaire, the participant was classified as premenopausal. All other participants with missing menopause status were classified as premenopausal if age at baseline was under 51 and postmenopausal if age at baseline was 51 – median age at natural menopause reported in Canada – or older (171). 2.3 Health and lifestyle data Body mass index (BMI, measured in kg/m2) was calculated based on height and weight data collected during in-person assessments (897 participants). For those without in-person height and weight data, self-reported measures from the baseline questionnaire were utilized (201 participants). Other factors obtained from the baseline questionnaire included age, ethnicity, highest attained education, household income, family history of breast cancer, ages at menarche and menopause, history of hormone replacement therapy, history of hormonal contraceptive use, parity and childbirth, BMI, alcohol consumption, and exposure to active and passive smoking. Missing covariate data were imputed using the Multiple 14 Imputation by Chained Equations (MICE) algorithm (172). All variables were assumed to be missing at random. All risk factors and the outcome were used as auxiliary variables for imputation. Ten imputed datasets were generated, each with thirty iterations (173). 2.4 Metabolomics data 2.4.1 Metabolomics profiling Non-fasting serum samples (20 μl each) were plated on 96-well plates and shipped to General Metabolics LLC (GMet; Boston, Massachusetts). For a randomly selected five percent of participants, an additional sample was included as a blind duplicate for quality control assessment. Metabolites were measured using previously described mass spectrometry (MS) methods (174). Briefly, each sample was diluted with 180μl of 80% ethanol in water. 5μl of the resulting solution was injected twice, consecutively, at a flow rate of 0.150 ml/minute, into an Agilent 6550 iFunnel Quadrupole time-of-flight (Q-TOF) mass spectrometer. Study samples were arranged across 15 plates: plates 1-9 contained BCGP samples and plates 11-16 contained ATP samples. Blanks, pooled study samples, and National Institute of Standards and Technology standard reference materials were injected after every 48 study samples for quality assurance purposes. Metabolites were detected as peaks with specific mass-to-charge ratios (m/z), and the intensity of each peak was a measure of the relative abundance of each particular metabolite. All peaks with m/z between 50 and 1,000 were recorded for each sample. For those samples with missing values for a particular metabolite (i.e., no peak detected in the sample), the missing values were backfilled using the measured intensity in the sample at the recorded m/z for that metabolite. Mass spectra data were processed by GMet using MATLAB (The Mathworks, Natick, MA, USA). Metabolite annotations were identified by comparing measured m/z to theoretical m/z within 0.001 Da recorded in the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and cross-referenced against the Chemical Entities of Biological Interest 15 (ChEBI) database (175–177). Since no chromatographic separation was performed in tandem with mass spectrometry, it was not possible to distinguish between metabolites with identical molecular formulas. Therefore, multiple metabolites may have been identified for each signal, and, in such cases, a likely metabolite name was assigned based on GMet’s schema that uses existing knowledge of metabolite abundance and co-occurrence to identify metabolites. 2.4.2 Quality control and preprocessing Quality control analysis, data preprocessing, and statistical analyses were performed in R version 4.3.2. Metabolites that were missing in over 50% of the study samples were excluded (178). The 50% missing threshold was selected to retain a sufficiently large number of metabolites for analysis while mitigating potential bias resulting from the imputation process, and has been applied in previous metabolomics studies (179–181). Coefficients of variation (CVs) were calculated to assess reproducibility based on the blind duplicate samples. For each pair of duplicate samples, the CV of each metabolite was computed by dividing the standard deviation (SD) by the mean ion intensity, and median CVs across all samples were calculated. Metabolites with median CVs above 20% were excluded; CVs of up to 20% generally indicate acceptable laboratory error (182). Metabolite intensity measures were normalized using a cubic spline method (183,184). Cubic spline normalization aligns the distribution of intensities for each metabolite across all spectra to a predetermined baseline distribution based on the geometric mean level of each metabolite. For each sample, two sets of quantiles, one from the sample and the other from the baseline, were fitted to estimate parameters for a natural cubic spline (i.e., a curve consisting of piecewise cubic functions). The spline parameters were estimated for each interval between consecutive quantiles. New quantiles for that sample were obtained by shifting the original quantiles by a small offset, and this process was repeated on the 16 newly modified quantiles. Results from five iterations were averaged to obtain new, normalized quantile estimates. Principal component analysis (PCA) and relative log expression (RLE) plots were used to visualize data grouping patterns and evaluate the effects of normalization (185–187). We performed PCA on metabolite intensities before and after normalization and plotted the first two components as grouped by cohort. Clear separations into different groupings would indicate the presence of a cohort effect (187). RLE plots are boxplots that visualize unwanted technical variation in the data (186). To construct RLE plots, the intensity of each metabolite in each sample was subtracted from the median across all samples to obtain deviation from the median; we then randomly selected 200 samples and generated a boxplot of all metabolite deviations for each of those samples. Boxplots that deviated from a zero median indicated presence of unwanted variations (186,187). After normalization, duplicate ion levels for each metabolite were averaged to obtain one single measurement per participant. To account for the matched study design, the mean intensity measure of each metabolite in a matched pair was set to zero (188). In addition, each metabolite intensity was divided by the standard deviation across all samples in order to assess breast cancer risk associated with one standard deviation increase in metabolite intensity. 2.5 Statistical analysis Associations of each metabolite with breast cancer risk were estimated using unconditional logistic regression. The OR of breast cancer associated with one SD increase in each metabolite level was adjusted for matching factors: cohort, menopause status, blood collection age, and year of blood collection. Multiple testing was accounted for by applying the Benjamini-Hochberg procedure with the false discovery rate (FDR, also known as q-value) set at 0.1 (189). For metabolites that were deemed 17 significant after multiple testing correction, we fitted separate logistic regression models in which variables for known breast cancer risk factors were added, one at a time, to investigate the potential for confounding effects. Continuous risk factors included age at baseline, number of live births, years of HRT, duration of smoking, and duration of PSE during childhood, during adulthood at home, and during adulthood at work. Categorical risk factors included ethnicity, education levels, household income levels, age at menarche, age at first pregnancy, age at menopause, history of contraceptive use, history of HRT use, breastfeeding duration, BMI, frequency of alcohol consumption, frequency of binge drinking, smoking status, and age at smoking initiation. Analyses were completed on each of the imputed data sets and the resulting OR estimates were averaged. Any risk factors that altered the average OR estimate by 10% or more were considered a confounder and included in the final logistic regression model to obtain fully adjusted ORs. A pooled 95% CI was generated using Rubin’s Rule (190). Statistically significant metabolites from the overall analysis were also evaluated in the following subgroups: participants who were postmenopausal at baseline, cases diagnosed with invasive ductal carcinomas along with their matched controls, and cases with ER+ and/or PR+ cancers and their matched controls. Risk prediction models were developed using five classifiers that have been previously used in metabolomics studies: 1) least absolute shrinkage and selection operator (lasso), 2) random forest (RF), 3) partial least squares discriminant analysis (PLS-DA), 4) support vector machine (SVM) with linear kernels and 5) SVM with radial kernels (142). Lasso, a regression and variable selection technique, adds a penalty term to the traditional logistic regression to artificially shrink variable coefficients, and less predictive variables have zero coefficients and are removed, resulting in a simple, interpretable model with reduced risk of overfitting (191). PLS-DA is both a dimensionality reduction and classification method. Independent variables are transformed into a smaller set of components – such that the maximal covariance is preserved between the predictors and outcome – which are then used to create prediction scores for the outcome (192). RF is a versatile tree-based ensemble of cross-validated decision trees built from bootstrapped samples of the data, wherein final predictions are aggregated by voting from decision 18 trees (193). SVM is a classification method that, similar to logistic regression, seeks to create hyperplanes that optimize separation between different classes, but does so using statistical instead of probabilistic equations (194). SVM uses kernel functions, which are transformations that map the data into higher-dimensional spaces to make class separation easier; two of the most commonly used kernel methods in SVM are linear and, for data that is not linearly separable, radial basis functions (195). For each method, prediction models were generated using the metabolites found to be statistically significantly associated with breast cancer in the overall logistic regression analysis, with and without established breast cancer risk factors. For each model, 20% of the data was removed from training and used only for validating the best-performing model from each classifier, and 10-fold cross-validation was performed on the remaining 80% training data. Pair identifiers were used to split the data to preserve the matched design. For cross-validation, data were randomly split into 10 equal-sized subsets, each also maintaining case-control matching. Nine subsets were used to train the algorithms across a grid of hyperparameters, and the tenth subset was used to test performance. This process was repeated 10 times, and the hyperparameters that resulted in the highest cross-validated AUC were used to predict outcome in the 20% holdout validation data set, wherein AUC, sensitivity, specificity, and F1 score were calculated (196). 100 models were performed on 100 independent training-validation partitions. The empirical mean and 95% CI for predictive performance were calculated. For each model, we also calculated a method-dependent variable importance score for each variable to assess the contribution of each predictor to overall performance. Variable importance was calculated as the absolute value of the penalized coefficients corresponding to the tuned model for lasso, the weighted sum of the absolute coefficients for PLS-DA, the out-of-bag (OOB) accuracy for random forest, and the one-dimensional sensitivity analysis measure based on average absolute deviation (AAD) from the median for both SVMs (197,198). The 19 variables importance scores were calculated for each iteration and each variable was assigned a rank, with 1 being the most influential, based on averaged variable importance score across 100 iterations. 20 Chapter 3: Results 3.1 Participant characteristics A total of 593 matched cases and controls – consisting of 226 cases and controls from ATP and 367 cases and controls from BCGP – were included in the study. Characteristics of cases and controls are summarized in Table 1. Cases were diagnosed on average 5.3 years after enrolment, with a mean age at diagnosis of 62.5 (7.9) years. Most cases were invasive ductal carcinomas (74.5%), followed by lobular (12.3%), mixed ductal/lobular (7.6%), and others (5.6%). ER/PR-positive tumors were the most common molecular subtype (77.5%), followed by HER2-positive (12.4%) and triple-negative (10.1%) tumors. The distributions of most breast cancer risk factors were generally similar between cases and controls. Mean (SD) age at baseline was 57.3 years (7.9) for cases and 57.2 (8.0) for controls. Most participants reported being of non-Hispanic white ethnicity (93%) and were postmenopausal at baseline (71.8%). Most participants (76.0% cases, 76.8% controls) had completed at least some post-secondary education, and similar proportions of both groups earned $50,000 or more annually (75.1% cases, 76.4% controls). A greater proportion of cases (25.3%) than controls (18.7%) had a family history of breast cancer. Age at menarche was 12-13 for the majority of participants (59.7% cases, 57.6% controls). Most participants (91.2% cases, 87.8% controls) used at least one form of hormonal contraceptive. Similar proportions of cases and controls (83.7% and 84.7%, respectively) reported having at least one pregnancy. Age at first pregnancy ranged from 14 to 43 years of age, with slightly more cases than controls being over age 25. Approximately 80% of participants had at least one live birth and the median breastfeeding duration per birth was just under 6 months. Among cases and controls, 39.5% and 37.2%, respectively, reported using HRT. The average length of HRT use was 2.6 years for cases and 2.0 years for controls. 21 Approximately half of participants (53.0% cases, 50.8% controls) were overweight or obese. Most participants consumed alcohol at least once per month, with 25% reporting drinking four or more times a week. Over half of participants also reported binge drinking (≥ 4 drinks per occasion), although only a small percentage (6.8% cases, 5.8% controls) engaged in binge drinking on a weekly basis. Only 5% of participants reported being current daily or occasional smokers, and 54% never smoked. Among smokers, the majority initiated in their teenage years. Mean (SD) duration of smoking was 7.0 (11.8) years for cases and 6.6 (11.0) years for controls. 3.2 Metabolite data A total of 1,677 metabolites were annotated. The median number of potential names for each detected metabolite was 4 (range 1-116), with 426 (25.4%) metabolites having only one annotation. Among the 1,677 metabolites, 854 (50.9%) were detected among at least 50% of study samples. Median CVs among duplicate samples ranged from 1.1% to 31.9%, with 17 metabolites (2.0%) having CVs greater than 20%. These metabolites were excluded, leaving 837 metabolites for analysis. PCA plots before normalization (Figures 1a and 1c) showed that metabolite measures from some ATP samples, especially those in plate 11 and, to a lesser extent, plate 13 were separate from the rest of the data, indicating presence of a cohort effect. The separation was significantly reduced after normalization (Figures 1b and 1d). Similarly, RLE plots showed that boxplots before normalization were more deviated from zero than after normalization (Figure 2). 3.3 Associations with breast cancer After correcting for multiple comparisons, a total of 24 metabolites were statistically significantly associated with breast cancer risk (Table 2). Thirteen metabolites were associated with reduced breast cancer risk, with ORs ranging from 0.63 to 0.84. Monomethyl sulfate had the strongest inverse association (OR=0.62 [95% CI: 0.51-0.78]). Eleven metabolites were positively associated with breast 22 cancer risk, with ORs ranging from 1.20 to 1.23. Corchoionoside A had the strongest positive association (1.23 [1.09-1.40]). No confounders of these associations were identified. 3.3.1 Subgroup analyses Among postmenopausal participants (426 case-control pairs), significant associations were observed for ten of the 24 metabolites (Table 3). Nine metabolites were associated with decreased risk, with associations ranging from 0.57 [0.44-0.75] for monomethyl sulfate to 0.84 [0.73-0.96] for 2-isopropyl-1,4-benzenediol. Two metabolites were associated with higher risk: PE (18:1/18:1) (1.23 [1.08-1.42]) and p-coumaroyl aspartate (1.23 [1.08-1.41]). Of the 24 metabolites, ten were statistically significantly associated with ductal breast cancer risk, including 5 positive and 5 inverse associations. Monomethyl sulfate (0.65 [0.50-0.83]) and lauryl diethanolamide (0.65 [0.51-0.81]) demonstrated the strongest inverse associations, while the strongest positive associations were observed for 1a,24R,25-trihydroxyvitamin D2 (1.35 [1.18-1.56]), PE (16:1/22:6) (1.33 [1.16-1.52]), and PE (22:6/18:1) (1.34 [1.18-1.54]). Four of the 24 metabolites were found to be statistically significantly associated with risk of ER+/PR+ breast cancer (Table 5). Notably, metabolites with the strongest associations in the overall analysis - monomethyl sulfate, atractyloside G, PE (22:6/18:1), and corchoionoside A - were not found to be significantly associated with ER+/PR+ breast cancer risk. All four significant metabolites were protective, with the strongest associations observed for cinnamate (0.67, 0.53-0.85). 3.4 Breast cancer risk prediction The AUCs of the breast cancer risk prediction models utilizing different combinations of classifiers and predictors are reported in Table 6. Risk prediction models in this study had low to moderate 23 discrimination. The best performing models were generated using lasso (metabolite predictors only) and SVM with linear kernel (metabolite predictors only); both models achieved an AUC of 0.63. PLS-DA and RF had comparable predictive performance with AUCs between 0.60 and 0.62. The worst-performing model was SVM with radial kernel with only metabolite predictors (AUC 0.56). Predictive performance did not improve with the addition of health and lifestyle risk factors. The same patterns were observed across classifiers for sensitivity, specificity, and F1 score, which are shown along with AUC in Figure 3. For the various breast cancer subgroups, predictive performance was comparable to overall prediction, with AUCs of 0.58-0.63 for postmenopausal breast cancer, 0.57-0.63 for ductal breast cancer, and 0.57-0.62 for ER+/PR+ breast cancer (Table 6). Similar to overall prediction, SVM-radial had slightly lower performance than the other classifiers in all subgroups. Sensitivity, specificity, and F1 scores are visualized in Appendix Figures 2-4. 3.4.1 Variable importance Variable importance rankings for the significant metabolites in predictions of overall breast cancer risk are visualized in Table 7. Since health and lifestyle risk factors did not improve predictive performance, we did not calculate variable importance of these predictors. Among the metabolites, monomethyl sulfate and lauryl diethanolamide were consistently the most influential variables. Monomethyl sulfate ranked first for lasso and SVM-linear, third in random forest, and fourth for PLS-DA; lauryl diethanolamide ranked first for PLS-DA and random forest, and second for lasso and SVM-linear. Both were among the top 10 most important features for SVM-radial. Other important metabolites across all classifiers included celerin and arachidonate, which were both among the top 10 for all algorithms. Metabolites that were considered least important included oxalurate, 2-isopropyl-1,4-benzenediol, and PE (22:5/18:1), which were among the five lowest ranked metabolites for multiple classifiers. 24 Some metabolites had substantially different ranks based on the choice of algorithm. Corchoionoside A ranked fourth and fifth for lasso and SVM-linear, respectively, but was among the least important for PLS-DA and random forest and middling for SVM-radial. 2,3,5-Trihydroxytoluene was the most influential metabolite for SVM-radial and ranked seventh for SVM-linear but was among the least influential predictors for random forest. Atractyloside G ranked second for SVM-radial, sixth for SVM-linear, and seventh for lasso, but second lowest for PLS-DA. PE (18:1/18:1) was important only for PLS-DA. 25 Chapter 4: Discussion 4.1 Metabolite associations with breast cancer risk We observed statistically significant associations with breast cancer risk for 24 metabolites. Of the 24 significant metabolites, 10 were also associated with risk of postmenopausal breast cancer, 10 with risk of invasive ductal breast cancer, and 4 with risk of ER+/PR+ breast cancer. Multiple exogenous metabolites were observed to be associated with risk of breast cancer in our study. In fact, the strongest association with breast cancer risk was a protective effect observed with monomethyl sulfate, which was also found to be inversely associated with postmenopausal breast cancer and invasive ductal carcinoma risks in subgroup analyses. Monomethyl sulfate is used in pharmaceutical manufacturing (199). Literature on monomethyl sulfate and human health is sparse. A previous study linked increased serum monomethyl sulfate levels to higher risk of polycystic ovary syndrome and lower risk of premature ovarian failure (200). Acetyl tributyl citrate (ATBC) was associated with increased breast cancer risk in our study. ATBC is a common plasticizer in food wrappings, children’s toys, and medical coatings (201). It is also approved as a food flavoring additive, but humans may be exposed to higher than approved doses since it has been reported to migrate from packaging into food (202). Evidence from animal and in vivo studies suggest that ATBC may disrupt ovarian function and stimulate tissue growth (203,204). Two previous studies nested in the same cohort found associations between breast cancer risk and increased ATBC levels in plasma samples (153,154). We observed cinnamate to be associated with a decreased risk of breast cancer. In subgroup analyses, cinnamate was also associated with decreased risks of postmenopausal breast cancer and ER+/PR+ breast cancer. Cinnamate is a naturally occurring compound in berries, coffee, and leafy brassicas (205). 26 Cinnamate has demonstrated anticarcinogenic and antigenotoxic properties in animal and in vitro studies, and derivatives of cinnamic acid have been incorporated in the design of anti-tumor agents or used in combination with conventional drugs, especially for TNBC and HER2+ tumors (206–211). We also observed fuscaxanthone C (a xanthone extracted from the stem of the mangosteen plant) to be associated with a decreased risk of breast cancer, and celerin (a coumarin detected in celery), fusicoccin H, atractyloside G, and corchoionoside A (glycosides produced by fungi and plants that are used in traditional medicine in some countries) to be associated with increased risks of breast cancer (212–216). In subgroup analyses, fusicoccin H was also associated with increased risk of invasive ductal carcinoma. Some limited literature exists on the cytotoxic and anti-proliferative effects of these compounds on cancer cells and their potential as therapeutic candidates. For example, fusicoccin H and other fusicoccanes have been shown to stabilize protein-protein interactions between the 14-3-3 protein family, which are known to regulate fundamental cellular processes, with its binding partners, thereby hampering processes regulated by 14-3-3 proteins that, if left unchecked, might promote cancer cell proliferation and migration (214). Subcutaneous and oral xanthones from the mangosteen plant, including fuscaxanthone C, have been shown in several in vivo and in vitro studies to suppress tumor metastasis and induce apoptosis in several carcinoma and adenocarcinoma models (217). We observed a metabolite of vitamin D2, 1a,24R,25-trihydroxyvitamin D2, to be associated with increased risk of breast cancer (218). In subgroup analyses, it was associated with an increased risk of invasive ductal carcinoma. While no reports on the impacts of 1a,24R,25-trihydroxyvitamin D2 on cancer were identified, other vitamin D2 metabolites, such as 1,24(S)-dihydroxyvitamin D2, have been observed to have antitumor activity in animal breast cancer cells, and circulating vitamin D levels have been previously associated with decreased risks of breast cancer in previous pooled- and meta-analyses (219–223). As shown in Appendix Table 1, the metabolite has five other potential identities, including monoacylglycerol (0:0/24:6/0:0), a lipid that is hydrolyzed by monoacylglycerol lipase (MAGL) into free fatty acids in the body (224). MAGL plays a crucial roles in regulating lipids in adipose tissues and are 27 significantly overexpressed in aggressive cancer cells, including breast cancer (224). The other four potential annotations belong to a class of mushroom-derived compounds called ergostane steroids, some of which have been found to reverse multidrug resistance in cancer cells (225). Three benzenoid metabolites, 2,3,5-trihydroxytoluene, N-benzoyl-D-arginine-4-nitroanilide, and 2-isopropyl-1,4-benzenediol, were found to be associated with decreased risks of breast cancer in our study. In subgroup analyses, 2,3,5-trihydroxytoluene and 2-isopropyl-1,4-benzenediol were associated with decreased risk of invasive ductal carcinoma, and N-benzoyl-D-arginine-4-nitroanilide was associated with a decreased risk of postmenopausal breast cancer and of ER+/PR+ tumors. There is sparse information on the sources of these specific metabolites in human serum and any potential health impacts. 2-isopropyl-1,4-benzenediol does belong to a subclass of compounds called cumenes, some of which serve as chemical intermediates in chemical production and are natural petroleum biproducts (226). Exposure to cumenes has been linked to increased cancer risk in animal studies (226). While the metabolite putatively identified as 2-isopropyl-1,4-benzenediol has 14 other potential identities (Appendix Table 1), all are benzenoid metabolites. Two amino acids, oxalurate and p-coumaroyl aspartate, were associated with breast cancer risk in our study; oxalurate was associated with a decreased risk and p-coumaroyl aspartate was associated with an increased risk. In subgroup analyses, both oxalurate and p-coumaroyl aspartate were associated with decreased risks of postmenopausal breast cancer risk, and p-coumaroyl aspartate was associated with a decreased risk of invasive ductal carcinoma. No existing literature supporting a relationship between oxalurate and cancer was identified, but several of its precursors have been linked to cancer. One study suggested that parabanic acid, which produces oxaluric acid through hydrolysis, is an indicator of oxidative stress, which influences cancer initiation and progression (227,228). Similar to our finding with p-coumaroyl aspartate, aspartate has been previously associated with increased risk of breast cancer (229–231). Aspartate is an important supplier of nitrogen and carbon to nucleotide synthesis and is a precursor 28 in asparagine synthesis for regulating anabolic metabolism and cell growth (232). Several metabolic pathways that regulate aspartate uptake and utilization by tumor cells have been proposed as biomarkers of early breast cancer progression (232–234). We observed statistically significant associations of eight lipid metabolites with breast cancer risk. Four phosphatidylethanolamines, PE (22:5/18:1), PE (16:1/22:6), PE (18:1/18:1), and PE (22:6/18:1), were associated with increased breast cancer risks. In subgroup analyses, PE (18:1/18:1) was associated with increased risk of postmenopausal breast cancer, and PE (16:1/22:6) and PE (22:6/18:1) were associated with increased risks of invasive ductal carcinoma. While previous studies have identified numerous phospholipids – including phosphatidylethanolamines – to be associated with breast cancer risk, unlike our findings, mostly inverse relationships between these lipids and breast cancer risk have been observed (143,150,157,161). Each of the putatively identified phosphatidylethanolamines have multiple other potential identities (Appendix Table 1); however, all are phosphatidylethanolamines. Lauryl diethanolamide – a derivative of lauric acid, which is commonly found in coconut and palm kernel oils – was associated with a decreased risk of breast cancer (235). In subgroup analyses, it was associated with decreased risks of postmenopausal breast cancer, invasive ductal carcinoma, and ER+/PR+ breast cancer. One previous study linked breast cancer risk with lower plasma lauric acid concentrations (236). In another study, lauric acid-derived lauryl carnitine levels were elevated in breast cancer plasma samples, but the association was only significant for TNBC (237). In cancer cells, laurate has been shown to exert anti-proliferative effects by promoting oxidative stress-induced cell death (238,239). This anticancer property might be mediated by the epidermal growth factor receptor, which is frequently overexpressed in TNBC, indicating lauric acid as a potential biomarker for this specific subtype (239,240). We did not have sufficient numbers of TNBC cases to explore this association. 29 Associations with decreased breast cancer risk were also observed for 10-hydroxymyristic acid methyl ester, a fatty alcohol of myristic acid which is abundant in milk fat (241). In subgroup analysis, 10-hydroxymyristic acid methyl ester was found to be associated with decreased risk of invasive ductal carcinoma. Contrary to our finding, circulating plasma myristic acid levels were observed to be associated with postmenopausal breast cancer risk in a previous study (242). A meta-analysis also reported a positive association between overall cancer risk and myristic acid levels, but the authors did not differentiate between dietary and blood measurements. Myristic acid is a substrate of protein lipidation, which mediates multiple cellular processes including regulation of oncogenic signaling. Dysregulation of these pathways can indicate mutations of oncogenes that are involved in maintenance of normal cell functions and signal tumorigenesis (243,244). Among the two other fatty acids that share the same molecular formula as 10-hydroxymyristic acid methyl ester, higher dietary intake and circulating levels of pentadecanoic acid have been linked to decreased risk of metabolic syndrome, which is prevalent among breast cancer patients (245). We observed arachidonate, an omega-6 polyunsaturated fatty acid, to be associated with a decreased risk of breast cancer. In subgroup analyses, it was associated with a decreased risk of postmenopausal breast cancer. Our overall finding is consistent with results from one previous study (246). Two previous studies found postmenopausal breast cancer risk to be associated with lower blood arachidonate levels, but a third reported the opposite relationship (247–249). Another study found arachidonic acid to be upregulated in serum samples of ductal carcinoma patients (250). Arachidonate is involved in both pro- and anti-inflammatory pathways (251). On the one hand, arachidonate facilitates production of pro-inflammatory enzymes, which are found in substantially higher levels in breast tumor tissues, and inhibition of cyclooxygenase expression is one of the hypothesized mechanisms by which non-steroidal anti-inflammatory drugs reduce breast cancer risk (252,253). On the other hand, through the lipoxygenase pathways, arachidonate also results in anti-inflammatory lipoxins which may suppress proliferation of blood vessels that support tumor growth (251). 30 The final lipid associated with lowered breast cancer risk is isopropylmaleate, a fatty acyl that has not been linked to cancer in any previous literature (254). In subgroup analyses, isopropylmaleate was associated with decreased risk of postmenopausal breast cancer. There are nine other potential identities for this metabolite (Appendix Table 1), including dimethyl citraconate, which has been reported to modulate cell inflammation, and succinylacetone, which has been shown to suppress proliferation of colon cancer cells (255–257). Two steroid hormones metabolites, 16a-hydroxyestrone (16a-OHE1) and 11-oxo-androsterone glucuronide, were found to be inversely associated with breast cancer risk. In subgroup analyses, 16a-OHE1 was associated with decreased risks of postmenopausal breast cancer and invasive ductal carcinoma, and 11-oxo-androsterone glucuronide was associated with a decreased risk of ER+/PR+ breast cancer. The association with 16a-OHE1 is counterintuitive given that it exhibits estrogenic properties. However, the metabolite identified as 16a-OHE1 may be 2-OHE1 (Appendix Table 1), which is protective against breast cancer since it does not stimulate cell division and, by binding with estrogen receptors, can prevent the proliferative effect of 16a-OHE1 (258). Some previous analyses of serum metabolites have observed that increased 2-OHE1 levels and a high 2:16 OHE1 ratio lower risk of breast cancer, but unlike our findings, those results were largely significant only when restricted to postmenopausal and/or ER+/PR+ cases (259–262). 11-oxo-androsterone glucuronide is a metabolite of androgen 11-oxo-androsterone generated in the liver (263). No studies have identified 11-oxo-androsterone as a biomarker for breast cancer. Other androgens have been hypothesized to increase breast cancer risk, but the relationship between androgens and breast cancer is mixed. Only testosterone is consistently associated with increased breast cancer risk among premenopausal women (264). DHEA/S increases postmenopausal breast cancer risk, but evidence linking DHEA/S to breast cancer among premenopausal women is inconsistent and varies with age (264). In other studies, androgens have exerted anti-proliferative and stimulatory effects on tumor growth depending on ER status (265). 31 4.2 Breast cancer risk prediction Five classifiers were used to develop breast cancer risk prediction models. Discrimination was moderate, with SVM (linear kernel) and lasso slightly outperforming random forest, PLS-DA, and SVM with radial kernel. Overall, the choice of method did not make a significant difference on predictive performance in this study. In addition, no improvement was observed by the addition of health and lifestyle risk factors to the models. Few studies using prospectively collected metabolomics data have investigated predictions of breast cancer occurrence. Two previous matched-pairs predictions using PLS-DA both reported low to average predictive performance. Jobard et al previously predicted breast cancer using 791 pairs matched by age at blood collection, county of residence, collection center, menopause status, and fasting status, with an AUC of 0.61 (151). Mrowiec et al reported prediction accuracies of 52-55% when predicting breast cancer risk among subgroups of age-matched women (158). One case-cohort study reported a predictive AUC of 0.89 using PLS-DA, but the unmatched design means there may have been larger differences between their study groups compared to ours (146). 4.3 Strengths and limitations To our knowledge, this is the first serum metabolomics investigation of breast cancer risk conducted in Canada. The study population was well characterized with detailed breast cancer risk factor data that had been collected and harmonized across two prospective cohort studies. The availability of pre-diagnostic serum samples was a major strength as it allowed us to avoid reverse causation bias. In addition, the Q-TOF MS assay was a highly efficient and cost-effective approach with which to conduct untargeted metabolomics analyses and identify promising markers of cancer risk. The high-throughput nature of the method limits batch effects that tend to be an important issue for methods incorporating chromatography. 32 The study also employed a wide variety of classification methods to develop risk prediction models with extensive cross-validation. Each method had its own strengths and limitations. SVMs can classify both linear and non-linear data based on the choice of kernel basis function, but they are computationally intensive and there are no intuitive methods to measure feature importance (266). Random forest is a versatile method for both linear and non-linear data, is not prone to overfitting, and has versatile functionality for variable importance ranking, but they tend to be very sensitive to training data and error-prone to testing data (266). In addition, neither random forest nor SVM has built-in mechanisms for dimensionality reductions. Lasso, on the other hand, is a combined variable selector and classifier, thereby eliminating the need for an additional pre-processing step before prediction (267). However, it is only applicable to linear data and sensitive to overfitting and collinearity problems (267). PLS-DA is also a dimensionality reduction and classification approach, but by combining predictors into projections, it can handle collinearity (268). However, similar to lasso, it is applicable only to linear data and is prone to overfitting (268). Metabolomics data were generated at a single time point and, therefore, may not be reflective of the variability of an individual’s metabolomic profile over time (138). In addition, unwanted variation from a subset of the ATP experimental samples was reduced but not entirely eliminated by normalization and may have introduced variability that impacted our ability to detect significant associations. This variation was non-differential between cases and controls meaning that it unlikely biased our results. A substantial percentage of cases (34.7%) had missing hormone receptor status. No systematic differences were found between cases with known hormone receptor status and cases without, and the percentages of each subtype of hormone receptor are comparable to prevalences reported in literature (58). Interpretation of results is limited by lack of data on serum concentrations of metabolites which can be generated via incorporation of chromatography. In addition, because our method did not incorporate chromatography, we were not able to accurately identify all metabolites. Based on guidelines set forth by 33 the Metabolomics Standards Initiative, confidence in our annotation corresponds to level 4, the lowest level (269). However, in practice, the confidence level is likely higher given GMet’s schema that uses existing knowledge of metabolite abundance and co-occurrence to identify metabolites (135). Ultimately, a follow-up study incorporating chromatography will be needed to specifically identify any metabolites of interest. 4.4 Future research and applications A larger study incorporating participants from other CanPath cohorts would provide a considerably larger sample size, improving our ability to detect important metabolites while reducing false-positive findings. Larger studies would benefit from the efficiency and sensitivity of high-throughput untargeted Q-TOF MS and the linked and harmonized CanPath data. Outcomes from such a study could be used to validate current findings and results from other prospective cohorts of comparable sample sizes (270). In addition, a longitudinal analysis that utilizes repeated blood samples collected from multiple time points would also provide valuable insights into the temporal metabolomic alterations associated with breast cancer onset and risk factors. Understanding intra-personal variability in metabolomics levels would be valuable in investigating a disease that takes years to manifest. BCGP and ATP participants are not entirely representative of the larger populations of British Columbia and Alberta, with participants tending to be mostly women, older, of higher income, more educated, and mostly of European descent—all risk factors that are represented in luminal cancers more frequently than in TNBCs. Having fewer participants that represent the risk profiles associated with the more aggressive subtypes means findings are less generalizable to that subtype. In light of increases in early-onset breast cancer incidence in the past two decades, more efforts are needed to expand our understanding of this subpopulation (271). 34 More generally, a multi-omics approach would provide a more comprehensive risk profile of breast cancer oncogenesis. While the metabolome can be representative of both endogenous and exogenous influences, it does not fully account for the complex transformations associated with cancer development that occur at the genetic, transcriptional, and proteomic levels (272). Integrated proteomics and metabolomics data have been shown to produce high discrimination power in breast cancer risk prediction (273). Microbiomics, an emerging field that investigates the human microbiome system, would also provide understanding of dynamic mechanisms underlying nutrient uptake, inflammation, and immunomodulation, all of which are intricately linked to the development of cancer cells (274). 35 Chapter 5: Conclusion Using an untargeted metabolomics approach, our prospective study identified 24 circulating serum metabolites that were associated with incident breast cancer risk. The 13 metabolites that decreased breast cancer risk and 11 metabolites that increased risk putatively belong to classes of compounds that have been previously linked to the disease through various complex and interconnected metabolic pathways. Our findings replicate previous associations with amino acids, free fatty acids and phospholipids, and we identified potentially novel associations with steroid and xenobiotic metabolites that require further validation from a targeted follow-up analysis and corroboration from other cohorts. This study also used five machine learning classification methods to predict breast cancer risk, with moderate predictive performance across all classifiers. Ultimately, this study reaffirms metabolomics as a valuable source of biomarker detection for breast cancer that can be integrated with other omics approach to create comprehensive risk profiles and accurately assess future breast cancer development. 36 Tables Table 1. Characteristics of study population Variable Case (n=593) Control (n=593) Matching factors Cohort, n (%) ATP 226 (38.1%) 226 (38.1%) BCGP 367 (61.9%) 367 (61.9%) Menopause status, n (%) Premenopausal 167 (28.2%) 167 (28.2%) Postmenopausal 426 (71.8%) 426 (71.8%) Age at blood collection, mean (SD) 58.1 (7.9) 58.0 (8.0) Year of blood collection, n (%) 2009 83 (14.0%) 85 (14.3%) 2010 67 (11.3%) 63 (10.6%) 2011 98 (16.5%) 109 (18.4%) 2012 92 (15.5%) 92 (15.5%) 2013 139 (23.4%) 137 (23.1%) 2014 92 (15.5%) 98 (16.5%) 2015 21 (3.5%) 7 (1.2%) 2016 1 (0.2%) 2 (0.3%) Cancer-related factors Age at diagnosis, mean (SD) 62.5 (7.9) - Time to diagnosis, mean (SD) 5.3 (2.6) Histological subtype, n (%) Ductal 442 (74.5%) - Lobular 73 (12.3%) - Mixed ductal/lobular 45 (7.6%) - Other 33 (5.6%) - Molecular subtype, n (%) Triple-negative 39 (10.1%) - HER2-positive 48 (12.4%) - ER+/PR+/HER2- 300 (77.5%) - Missing 206 Demographic factors Baseline age, mean (SD) 57.3 (7.9) 57.2 (8.0) Ethnicity, n (%) White 481 (93.0%) 479 (92.8%) Non-white 36 (7.0%) 37 (7.2%) Missing 76 77 Income level, n (%) 37 < $49,999 135 (24.8%) 131 (23.5%) $50,000 – $99,999 214 (39.3%) 217 (38.9%) ≥ $100,000 195 (35.8%) 210 (37.6%) Missing 49 35 Education level, n (%) High school or less 141 (24.0%) 137 (23.2%) Some post-secondary 229 (38.9%) 239 (40.5%) Bachelor’s or higher 218 (37.1%) 214 (36.3%) Missing 5 3 Familial and reproductive factors Family history of breast cancer, n (%) Yes 148 (25.3%) 108 (18.7%) No 436 (74.7%) 471 (81.3%) Missing 9 14 Age at menarche, n (%) < 12 years 99 (17.3%) 93 (16.2%) 12-13 years 341 (59.7%) 330 (57.6%) ≥ 14 years 131 (22.9%) 150 (26.2%) Missing 22 20 Age at menopause, n (%) Premenopausal 167 (30.0%) 167 (29.9%) < 45 years 84 (15.1%) 73 (13.1%) 45-54 years 229 (41.2%) 252 (45.2%) ≥ 55 years 76 (13.7%) 66 (11.8%) Missing 37 35 Number of pregnancies, mean (SD) 2.3 (1.5) 2.4 (1.6) Missing 3 3 Number of live births, mean (SD) 1.7 (1.2) 1.9 (1.2) Missing 3 3 Age at first pregnancy, n (%) Never pregnant 96 (16.3%) 90 (15.3%) < 20 years 84 (14.3%) 103 (17.5%) 20-24 years 164 (27.8%) 178 (30.2%) 25-29 years 142 (24.1%) 132 (22.4%) ≥ 30 years 103 (17.5%) 87 (14.7%) Missing 4 3 Breastfeeding duration per live birth, n (%) No live births 124 (22.7%) 110 (19.9%) 0-5 months 236 (43.1%) 259 (46.8%) 6-11 months 141 (25.8%) 137 (24.7%) ≥ 12 months 46 (8.4%) 48 (8.7%) Missing 46 39 Hormonal contraceptive use, n (%) Ever 539 (91.2%) 520 (87.8%) 38 Never 52 (8.8%) 72 (12.2%) Missing 2 1 HRT use, n (%) Ever 232 (39.5%) 220 (37.2%) Never 356 (60.5%) 372 (62.5%) Missing 5 1 Years of HRT, mean (SD) 2.6 (5.1) 2.0 (4.6) Missing 26 18 Lifestyle factors BMI, n (%) <18.5 kg/m2 6 (1.1%) 8 (1.5%) 18.5-24.9 kg/m2 251 (45.9%) 263 (47.7%) 25.0-29.9 kg/m2 175 (32.0%) 163 (29.6%) ≥30.0 kg/m2 115 (21.0%) 117 (21.2%) Missing 46 42 Alcohol frequency per week, n (%) Never 55 (9.4%) 70 (11.8%) 1-3 times/month 208 (35.4%) 197 (33.3%) 1-3 times/week 177 (30.1%) 180 (30.5%) ≥ 4 times/week 148 (25.2%) 144 (24.4%) Missing 5 2 Binge drinking frequency, n (%) Never 315 (58.6%) 311 (57.2%) 1-11 times/year 154 (28.6%) 158 (29.0%) 1-3 times/month 32 (5.9%) 43 (7.9%) ≥ 1 time/week 37 (6.8%) 32 (5.8%) Missing 55 49 Smoking status, n (%) Never smoker 315 (54.3%) 311 (54.0%) Past smoker 237 (40.9%) 237 (41.1%) Current smoker 28 (4.8%) 28 (4.9%) Missing 13 17 Smoking duration in years, mean (SD) 7.0 (11.8) 6.6 (11.0) Missing 43 54 Age at smoking initiation, n (%) Never smoker 315 (57.2%) 311 (57.1%) < 15 years 34 (6.2%) 40 (7.3%) 15-19 years 157 (28.5%) 145 (26.6%) 20-24 years 35 (6.4%) 39 (7.2%) ≥ 25 years 10 (1.8%) 10 (1.8%) Missing 42 48 Childhood PSE duration, mean (SD) 11.1 (7.9) 11.0 (7.9) Missing 32 33 Adult PSE duration at home, mean (SD) 8.0 (11.1) 7.4 (10.5) 39 Missing 21 19 Adult PSE duration at work, mean (SD) 5.9 (8.3) 5.6 (8.4) Missing 48 44 40 Table 2. Metabolites associated with breast cancer risk (q<0.1) Metabolite Putative annotation OR (95% CI) p-value q-value Ion 63 Monomethyl sulfate 0.63 (0.51-0.78) <0.001 0.012 Ion 729 Lauryl diethanolamide 0.67 (0.56-0.80) <0.001 0.012 Ion 184 Cinnamate 0.77 (0.64-0.90) 0.002 0.099 Ion 1139 N-Benzoyl-D-arginine-4-nitroanilide 0.81 (0.72-0.91) <0.001 0.099 Ion 724 16a-Hydroxyestrone 0.81 (0.72-0.92) <0.001 0.099 Ion 809 Arachidonate 0.82 (0.73-0.92) <0.001 0.099 Ion 1260 Fuscaxanthone C 0.83 (0.74-0.93) 0.002 0.099 Ion 147 2,3,5-Trihydroxytoluene 0.83 (0.74-0.93) 0.002 0.099 Ion 227 Isopropylmaleate 0.83 (0.74-0.94) 0.002 0.099 Ion 630 10-Hydroxymyristic acid methyl ester 0.83 (0.74-0.94) 0.003 0.099 Ion 200 2-Isopropyl-1,4-benzenediol 0.84 (0.74-0.94) 0.003 0.099 Ion 123 Oxalurate 0.84 (0.74-0.94) 0.003 0.099 Ion 1370 11-Oxo-androsterone glucuronide 0.84 (0.75-0.94) 0.003 0.099 Ion 1625 PE (22:5/18:1) 1.20 (1.07-1.34) 0.002 0.099 Ion 689 p-Coumaroyl aspartate 1.20 (1.07-1.34) 0.002 0.099 Ion 634 Celerin 1.20 (1.07-1.35) 0.002 0.099 Ion 1282 1a,24R,25-Trihydroxyvitamin D2 1.21 (1.08-1.36) 0.002 0.099 Ion 1612 PE (16:1/22:6) 1.21 (1.08-1.36) 0.001 0.099 Ion 1158 Acetyl tributyl citrate 1.21 (1.07-1.38) 0.003 0.099 Ion 1377 Fusicoccin H 1.21(1.08-1.37) 0.001 0.099 Ion 1600 PE (18:1/18:1) 1.21 (1.08-1.36) 0.001 0.099 Ion 1194 Atractyloside G 1.22 (1.08-1.38) 0.002 0.099 Ion 1624 PE (22:6/18:1) 1.22 (1.09-1.37) <0.001 0.099 Ion 1110 Corchoionoside A 1.23 (1.09-1.40) 0.001 0.099 41 Table 3. Metabolite associations with postmenopausal breast cancer risk Metabolite Putative annotation OR (95% CI) p-value q-value Ion 63 Monomethyl sulfate* 0.57 (0.44-0.75) <0.001 0.022 Ion 729 Lauryl diethanolamide* 0.61 (0.49-0.76) <0.001 0.013 Ion 184 Cinnamate* 0.68 (0.54-0.84) <0.001 0.038 Ion 1139 N-Benzoyl-D-arginine-4-nitroanilide* 0.80 (0.69-0.92) 0.003 0.095 Ion 724 16a-Hydroxyestrone* 0.75 (0.64-0.88) <0.001 0.038 Ion 809 Arachidonate* 0.77 (0.66-0.88) <0.001 0.038 Ion 1260 Fuscaxanthone C 0.84 (0.73-0.97) 0.016 0.181 Ion 147 2,3,5-Trihydroxytoluene 0.81 (0.70-0.94) 0.005 0.121 Ion 227 Isopropylmaleate* 0.77 (0.67-0.89) <0.001 0.038 Ion 630 10-Hydroxymyristic acid methyl ester 0.83 (0.72-0.95) 0.008 0.134 Ion 200 2-Isopropyl-1,4-benzenediol 0.84 (0.73-0.96) 0.012 0.165 Ion 123 Oxalurate* 0.80 (0.69-0.93) 0.003 0.095 Ion 1370 11-Oxo-androsterone glucuronide 0.90 (0.78-1.03) 0.133 0.401 Ion 1625 PE (22:5/18:1) 1.12 (0.97-1.28) 0.121 0.390 Ion 689 p-Coumaroyl aspartate* 1.23 (1.08-1.41) 0.003 0.095 Ion 634 Celerin 1.21 (1.05-1.40) 0.008 0.138 Ion 1282 1a,24R,25-Trihydroxyvitamin D2 1.16 (1.01-1.32) 0.031 0.246 Ion 1612 PE (16:1/22:6) 1.12 (0.97-1.28) 0.113 0.381 Ion 1158 Acetyl tributyl citrate 1.15 (0.99-1.34) 0.061 0.309 Ion 1377 Fusicoccin H 1.08 (0.94-1.24) 0.265 0.519 Ion 1600 PE (18:1/18:1)* 1.23 (1.08-1.42) 0.003 0.095 Ion 1194 Atractyloside G 1.19 (1.03-1.39) 0.020 0.196 Ion 1624 PE (22:6/18:1) 1.13 (0.99-1.30) 0.077 0.331 Ion 1110 Corchoionoside A 1.19 (1.02-1.39) 0.025 0.220 * Statistically significant associations (q<0.1) 42 Table 4. Metabolites associations with invasive ductal carcinoma risk Metabolite Putative annotation OR (95% CI) p-value q-value Ion 63 Monomethyl sulfate* 0.65 (0.50-0.83) 0.001 0.051 Ion 729 Lauryl diethanolamide* 0.65 (0.51-0.81) <0.001 0.040 Ion 184 Cinnamate 0.84 (0.69-0.98) 0.054 0.344 Ion 1139 N-Benzoyl-D-arginine-4-nitroanilide 0.84 (0.73-0.96) 0.014 0.197 Ion 724 16a-Hydroxyestrone* 0.79 (0.68-0.91) 0.001 0.051 Ion 809 Arachidonate 0.82 (0.71-0.94) 0.005 0.121 Ion 1260 Fuscaxanthone C 0.87 (0.76-0.99) 0.037 0.306 Ion 147 2,3,5-Trihydroxytoluene* 0.81 (0.71-0.93) 0.003 0.095 Ion 227 Isopropylmaleate 0.84 (0.73-0.96) 0.010 0.159 Ion 630 10-Hydroxymyristic acid methyl ester* 0.81 (0.71-0.93) 0.003 0.098 Ion 200 2-Isopropyl-1,4-benzenediol* 0.78 (0.68-0.90) <0.001 0.044 Ion 123 Oxalurate 0.86 (0.75-0.98) 0.022 0.230 Ion 1370 11-Oxo-androsterone glucuronide 0.89 (0.78-1.01) 0.081 0.389 Ion 1625 PE (22:5/18:1) 1.20 (1.05-1.36) 0.008 0.144 Ion 689 p-Coumaroyl aspartate* 1.26 (1.10-1.44) 0.001 0.048 Ion 634 Celerin 1.18 (1.03-1.35) 0.016 0.214 Ion 1282 1a,24R,25-Trihydroxyvitamin D2* 1.35 (1.18-1.56) <0.001 0.012 Ion 1612 PE (16:1/22:6)* 1.33 (1.16-1.52) <0.001 0.015 Ion 1158 Acetyl tributyl citrate 1.18 (1.03-1.37) 0.022 0.230 Ion 1377 Fusicoccin H* 1.29 (1.13-1.49) <0.001 0.040 Ion 1600 PE (18:1/18:1) 1.19 (1.04-1.37) 0.010 0.163 Ion 1194 Atractyloside G 1.19 (1.03-1.37) 0.017 0.221 Ion 1624 PE (22:6/18:1)* 1.34 (1.18-1.54) <0.001 0.012 Ion 1110 Corchoionoside A 1.23 (1.07-1.44) 0.006 0.127 * Statistically significant associations (q<0.1) 43 Table 5. Metabolites associations with ER+/PR+ cancer risk Metabolite Putative annotation OR (95% CI) p-value q-value Ion 63 Monomethyl sulfate 0.88 (0.66-1.19) 0.413 0.615 Ion 729 Lauryl diethanolamide* 0.72 (0.58-0.88) 0.002 0.087 Ion 184 Cinnamate* 0.67 (0.53-0.85) 0.001 0.068 Ion 1139 N-Benzoyl-D-arginine-4-nitroanilide* 0.75 (0.63-0.89) 0.001 0.068 Ion 724 16a-Hydroxyestrone 0.83 (0.69-0.98) 0.031 0.193 Ion 809 Arachidonate 0.90 (0.77-1.05) 0.171 0.387 Ion 1260 Fuscaxanthone C 0.82 (0.69-0.96) 0.016 0.150 Ion 147 2,3,5-Trihydroxytoluene 0.92 (0.78-1.09) 0.349 0.571 Ion 227 Isopropylmaleate 0.79 (0.68-0.93) 0.004 0.118 Ion 630 10-Hydroxymyristic acid methyl ester 0.80 (0.69-0.93) 0.005 0.118 Ion 200 2-Isopropyl-1,4-benzenediol 0.81 (0.69-0.96) 0.015 0.147 Ion 123 Oxalurate 0.88 (0.75-1.03) 0.102 0.305 Ion 1370 11-Oxo-androsterone glucuronide* 0.76 (0.65-0.89) 0.001 0.068 Ion 1625 PE (22:5/18:1) 1.13 (0.97-1.32) 0.119 0.329 Ion 689 p-Coumaroyl aspartate 1.22 (1.05-1.42) 0.008 0.122 Ion 634 Celerin 1.27 (1.08-1.49) 0.004 0.113 Ion 1282 1a,24R,25-Trihydroxyvitamin D2 1.14 (0.98-1.33) 0.099 0.302 Ion 1612 PE (16:1/22:6) 1.12 (0.96-1.31) 0.149 0.356 Ion 1158 Acetyl tributyl citrate 1.16 (0.98-1.39) 0.092 0.296 Ion 1377 Fusicoccin H 1.06 (0.90-1.26) 0.482 0.653 Ion 1600 PE (18:1/18:1) 1.25 (1.06-1.46) 0.007 0.118 Ion 1194 Atractyloside G 1.19 (1.00-1.42) 0.047 0.219 Ion 1624 PE (22:6/18:1) 1.15 (0.99-1.35) 0.071 0.257 Ion 1110 Corchoionoside A 1.13 (0.95-1.35) 0.172 0.387 * Statistically significant associations (q<0.1) 44 Table 6. AUC of risk prediction results for all participants and subgroups Sample Predictors Lasso PLS-DA RF SVM linear SVM radial Full Metabolites + risk factors 0.62 (0.53-0.71) 0.60 (0.52-0.69) 0.61 (0.54-0.69) 0.63 (0.53-0.71) 0.60 (0.52-0.66) Metabolites 0.63 (0.55-0.70) 0.62 (0.56-0.70) 0.61 (0.50-0.68) 0.63 (0.55-0.70) 0.56 (0.48-0.66) Subgroup Postmenopausal Metabolites 0.63 (0.52-0.74) 0.63 (0.52-0.74) 0.58 (0.47-0.69) 0.63 (0.54-0.75) 0.58 (0.47-0.72) Ductal Metabolites 0.63 (0.52-0.72) 0.62 (0.52-0.71) 0.61 (0.47-0.70) 0.62 (0.49-0.71) 0.57 (0.48-0.68) ER+/PR+ Metabolites 0.61 (0.45-0.74) 0.62 (0.46-0.73) 0.59 (0.46-0.69) 0.61 (0.45-0.74) 0.57 (0.44-0.69) 45 Table 7. Variable importance ranking for each classifier Variable Putative annotation Lasso PLS-DA RF SVM linear SVM radial Ion 63 Monomethyl sulfate 1 4 3 1 10 Ion 123 Oxalurate 20 20 22 14 16 Ion 147 2,3,5-Trihydroxytoluene 12 12 21 7 1 Ion 184 Cinnamate 5 19 9 3 13 Ion 200 2-Isopropyl-1,4-benzenediol 24 14 17 23 22 Ion 227 Isopropylmaleate 17 21 14 19 5 Ion 630 10-Hydroxymyristic acid methyl ester 15 7 11 17 14 Ion 634 Celerin 6 3 6 8 4 Ion 689 p-Coumaroyl aspartate 9 6 2 12 7 Ion 724 16a-Hydroxyestrone 13 9 24 10 19 Ion 729 Lauryl diethanolamide 2 1 1 2 9 Ion 809 Arachidonate 8 2 7 9 3 Ion 1110 Corchoionoside A 4 24 20 5 15 Ion 1139 N-Benzoyl-D-arginine-4-nitroanilide 16 18 15 15 12 Ion 1158 Acetyl tributyl citrate 3 22 19 4 11 Ion 1194 Atractyloside G 7 23 18 6 2 Ion 1260 Fuscaxanthone C 23 17 23 16 6 Ion 1282 1a,24R,25-Trihydroxyvitamin D2 10 8 5 11 18 Ion 1370 11-Oxo-androsterone glucuronide 11 10 8 18 21 Ion 1377 Fusicoccin H 14 11 4 13 8 Ion 1600 PE (18:1/18:1) 18 5 12 24 24 Ion 1612 PE (16:1/22:6) 19 16 13 22 23 Ion 1624 PE (22:6/18:1) 22 13 16 20 17 Ion 1625 PE (22:5/18:1) 21 15 10 21 20 46 Figures Figure 1. PCA plots of study samples (A) before normalization, grouped by cohort; (B) after normalization, grouped by cohort; (C) before normalization, grouped by plate; (D) after normalization, grouped by plate 47 Figure 2. RLE plots for 200 random samples (A) before normalization; (B) after cubic spline normalization 48 Figure 3. Performance of breast cancer prediction (A) metabolites only (n=24); (B) metabolites with health and lifestyle risk factors (n=47) 49 Bibliography 1. Canadian Cancer Statistics Advisory Committee in, collaboration with the Canadian Cancer Society, Statistics, Canada and the Public Health Agency of Canada. Canadian Cancer Statistics 2021 [Internet]. Toronto, ON: Canadian Cancer Society; 2021. Available from: https://cdn.cancer.ca/-/media/files/research/cancer-statistics/2021-statistics/2021-pdf-en-final.pdf 2. Ellison LF, Saint-Jacques N. Five-year cancer survival by stage at diagnosis in Canada. Health Rep. 2023 Jan 18;34(1):3–15. 3. Molina-Montes E, Requena M, Sánchez-Cantalejo E, Fernández MF, Arroyo-Morales M, Espín J, et al. Risk of second cancers cancer after a first primary breast cancer: A systematic review and meta-analysis. Gynecol Oncol. 2015 Jan 1;136(1):158–71. 4. Colleoni M, Sun Z, Price KN, Karlsson P, Forbes JF, Thürlimann B, et al. Annual Hazard Rates of Recurrence for Breast Cancer During 24 Years of Follow-Up: Results From the International Breast Cancer Study Group Trials I to V. J Clin Oncol. 2016 Mar 20;34(9):927–35. 5. Heins MJ, de Ligt KM, Verloop J, Siesling S, Korevaar JC, Berendsen A, et al. Adverse health effects after breast cancer up to 14 years after diagnosis. The Breast. 2022 Feb 1;61:22–8. 6. Bodai BI, Tuso P. Breast cancer survivorship: a comprehensive review of long-term medical issues and lifestyle recommendations. Perm J. 2015;19(2):48–79. 7. Carreira H, Williams R, Müller M, Harewood R, Stanway S, Bhaskaran K. Associations Between Breast Cancer Survivorship and Adverse Mental Health Outcomes: A Systematic Review. JNCI J Natl Cancer Inst. 2018 Nov 7;110(12):1311–27. 8. Lovelace DL, McDaniel LR, Golden D. Long-Term Effects of Breast Cancer Surgery, Treatment, and Survivor Care. J Midwifery Womens Health. 2019;64(6):713–24. 9. Surveillance Research Program, National Cancer Institute. SEER*Explorer: An interactive website for SEER cancer statistics [Internet]. 2023 [cited 2023 Aug 14]. Available from: https://seer.cancer.gov/statistics-network/explorer/ 10. Akinyemiju TF, Genkinger JM, Farhat M, Wilson A, Gary-Webb TL, Tehranifar P. Residential environment and breast cancer incidence and mortality: a systematic review and meta-analysis. BMC Cancer. 2015 Mar 28;15(1):191. 11. Dong JY, Qin LQ. Education level and breast cancer incidence: a meta-analysis of cohort studies. Menopause N Y N. 2020 Jan;27(1):113–8. 12. Borugian MJ, Spinelli JJ, Abanto Z, Xu CL, Wilkins R. Breast cancer incidence and neighbourhood income. Health Rep. 2011 Jun;22(2):7–13. 13. Mackillop WJ, Zhang-Salomons J, Boyd CJ, Groome PA. Associations between community income and cancer incidence in Canada and the United States. Cancer. 2000 Aug 15;89(4):901–12. 14. Gorey KM. Breast cancer survival in Canada and the USA: meta-analytic evidence of a Canadian advantage in low-income areas. Int J Epidemiol. 2009 Dec 1;38(6):1543–51. 50 15. Shuldiner J, Liu Y, Lofters A. Incidence of breast and colorectal cancer among immigrants in Ontario, Canada: a retrospective cohort study from 2004-2014. BMC Cancer. 2018 May 8;18(1):537. 16. Lundqvist A, Andersson E, Ahlberg I, Nilbert M, Gerdtham U. Socioeconomic inequalities in breast cancer incidence and mortality in Europe—a systematic review and meta-analysis. Eur J Public Health. 2016 Oct 1;26(5):804–13. 17. Tonin P, Weber B, Offit K, Couch F, Rebbeck TR, Neuhausen S, et al. Frequency of recurrent BRCA1 and BRCA2 mutations in Ashkenazi Jewish breast cancer families. Nat Med. 1996 Nov;2(11):1179–83. 18. Tonin PN, Mes-Masson AM, Futreal PA, Morgan K, Mahon M, Foulkes WD, et al. Founder BRCA1 and BRCA2 Mutations in French Canadian Breast and Ovarian Cancer Families. Am J Hum Genet. 1998 Nov;63(5):1341–51. 19. Ginsburg OM, Fischer HD, Shah BR, Lipscombe L, Fu L, Anderson GM, et al. A population-based study of ethnicity and breast cancer stage at diagnosis in Ontario. Curr Oncol. 2015 Apr;22(2):97–104. 20. Wan D, Villa D, Woods R, Yerushalmi R, Gelmon K. Breast Cancer Subtype Variation by Race and Ethnicity in a Diverse Population in British Columbia. Clin Breast Cancer. 2016 Jun 1;16(3):e49–55. 21. Heck KE, Pamuk ER. Explaining the Relation Between Education and Postmenopausal Breast Cancer. Am J Epidemiol. 1997 Feb 15;145(4):366–72. 22. Chlebowski RT, Luo J, Anderson GL, Barrington W, Reding K, Simon MS, et al. Weight loss and breast cancer incidence in postmenopausal women. Cancer. 2019;125(2):205–12. 23. Collaborative Group on Hormonal Factors in Breast Cancer. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58 209 women with breast cancer and 101 986 women without the disease. The Lancet. 2001 Oct 27;358(9291):1389–99. 24. Shiovitz S, Korde LA. Genetics of breast cancer: a topic in evolution. Ann Oncol. 2015 Jul;26(7):1291–9. 25. Anglian Breast Cancer Study Group. Prevalence and penetrance of BRCA1 and BRCA2 mutations in a population-based series of breast cancer cases. Br J Cancer. 2000 Nov;83(10):1301–8. 26. Peto J, Collins N, Barfoot R, Seal S, Warren W, Rahman N, et al. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst. 1999 Jun 2;91(11):943–9. 27. Tan MH, Mester JL, Ngeow J, Rybicki LA, Orloff MS, Eng C. Lifetime Cancer Risks in Individuals with Germline PTEN Mutations. Clin Cancer Res. 2012 Jan 15;18(2):400–7. 28. Birch JM, Alston RD, McNally RJ, Evans DGR, Kelsey AM, Harris M, et al. Relative frequency and morphology of cancers in carriers of germline TP53 mutations. Oncogene. 2001 Aug;20(34):4621–8. 51 29. Pharoah PDP, Guilford P, Caldas C. Incidence of gastric cancer and breast cancer in CDH1 (E-cadherin) mutation carriers from hereditary diffuse gastric cancer families. Gastroenterology. 2001 Dec 1;121(6):1348–53. 30. Li C, Fan Z, Lin X, Cao M, Song F, Song F. Parity and risk of developing breast cancer according to tumor subtype: A systematic review and meta-analysis. Cancer Epidemiol. 2021 Dec 1;75:102050. 31. Bernstein L. Epidemiology of endocrine-related risk factors for breast cancer. J Mammary Gland Biol Neoplasia. 2002 Jan;7(1):3–15. 32. Husby A, Wohlfahrt J, Øyen N, Melbye M. Pregnancy duration and breast cancer risk. Nat Commun. 2018 Oct 23;9(1):4255. 33. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50 302 women with breast cancer and 96 973 women without the disease. The Lancet. 2002 Jul;360(9328):187–95. 34. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and hormonal contraceptives: collaborative reanalysis of individual data on 53 297 women with breast cancer and 100 239 women without breast cancer from 54 epidemiological studies. The Lancet. 1996 Jun 22;347(9017):1713–27. 35. Hunter DJ, Colditz GA, Hankinson SE, Malspeis S, Spiegelman D, Chen W, et al. Oral Contraceptive Use and Breast Cancer: A Prospective Study of Young Women. Cancer Epidemiol Biomarkers Prev. 2010 Oct 6;19(10):2496–502. 36. Iversen L, Sivasubramaniam S, Lee AJ, Fielding S, Hannaford PC. Lifetime cancer risk and combined oral contraceptives: the Royal College of General Practitioners’ Oral Contraception Study. Am J Obstet Gynecol. 2017 Jun;216(6):580.e1-580.e9. 37. Mørch LS, Skovlund CW, Hannaford PC, Iversen L, Fielding S, Lidegaard Ø. Contemporary Hormonal Contraception and the Risk of Breast Cancer. N Engl J Med. 2017 Dec 7;377(23):2228–39. 38. Fitzpatrick D, Pirie K, Reeves G, Green J, Beral V. Combined and progestagen-only hormonal contraceptives and breast cancer risk: A UK nested case–control study and meta-analysis. PLOS Med. 2023 Mar 21;20(3):e1004188. 39. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52 705 women with breast cancer and 108 411 women without breast cancer. The Lancet. 1997 Oct 11;350(9084):1047–59. 40. Collaborative Group on Hormonal Factors in Breast Cancer. Type and timing of menopausal hormone therapy and breast cancer risk: individual participant meta-analysis of the worldwide epidemiological evidence. The Lancet. 2019 Sep 28;394(10204):1159–68. 52 41. Picon‐Ruiz M, Morata‐Tarifa C, Valle‐Goffin JJ, Friedman ER, Slingerland JM. Obesity and adverse breast cancer risk and outcome: Mechanistic insights and strategies for intervention. Ca. 2017;67(5):378–97. 42. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. The Lancet. 2008 Feb 16;371(9612):569–78. 43. Longnecker MP, Berlin JA, Orza MJ, Chalmers TC. A meta-analysis of alcohol consumption in relation to risk of breast cancer. JAMA. 1988 Aug 5;260(5):652–6. 44. Smith-Warner SA, Spiegelman D, Yaun SS, van den Brandt PA, Folsom AR, Goldbohm RA, et al. Alcohol and breast cancer in women: a pooled analysis of cohort studies. JAMA. 1998 Feb 18;279(7):535–40. 45. Collaborative Group on Hormonal Factors in Breast Cancer. Alcohol, tobacco and breast cancer – collaborative reanalysis of individual data from 53 epidemiological studies, including 58 515 women with breast cancer and 95 067 women without the disease. Br J Cancer. 2002 Nov 18;87(11):1234–45. 46. Key J, Hodgson S, Omar RZ, Jensen TK, Thompson SG, Boobis AR, et al. Meta-analysis of Studies of Alcohol and Breast Cancer with Consideration of the Methodological Issues. Cancer Causes Control. 2006 Aug 1;17(6):759–70. 47. Bagnardi V, Rota M, Botteri E, Tramacere I, Islami F, Fedirko V, et al. Alcohol consumption and site-specific cancer risk: a comprehensive dose–response meta-analysis. Br J Cancer. 2015 Feb 3;112(3):580–93. 48. Seitz HK, Pelucchi C, Bagnardi V, Vecchia CL. Epidemiology and Pathophysiology of Alcohol and Breast Cancer: Update 2012. Alcohol Alcohol. 2012 May 1;47(3):204–12. 49. Chen WY, Rosner B, Hankinson SE, Colditz GA, Willett WC. Moderate alcohol consumption during adult life, drinking patterns, and breast cancer risk. Jama. 2011 Nov 2;306(17):1884–90. 50. White AJ, DeRoo LA, Weinberg CR, Sandler DP. Lifetime Alcohol Intake, Binge Drinking Behaviors, and Breast Cancer Risk. Am J Epidemiol 2017 Sep 1;186(5):541-549. 51. Macacu A, Autier P, Boniol M, Boyle P. Active and passive smoking and risk of breast cancer: a meta-analysis. Breast Cancer Res Treat. 2015 Nov 1;154(2):213–24. 52. Collishaw NE, Norman F. Boyd, Kenneth P. Cantor, Hammond SK, Johnson KC, Millar J, et al. Canadian Expert Panel on Tobacco Smoke and Breast Cancer Risk [Internet]. Toronto, ON: Ontario Tobacco Research Unit; 2009 Apr. (OTRU Special Report Series). Available from: https://tobaccocontrol.bmj.com/content/20/1/e2.long 53. Gaudet MM, Carter BD, Brinton LA, Falk RT, Gram IT, Luo J, et al. Pooled analysis of active cigarette smoking and invasive breast cancer risk in 14 cohort studies. Int J Epidemiol. 2017 Jun 1;46(3):881–93. 53 54. Gaudet MM, Gapstur SM, Sun J, Diver WR, Hannan LM, Thun MJ. Active Smoking and Breast Cancer Risk: Original Cohort Data and Meta-Analysis. JNCI J Natl Cancer Inst. 2013 Apr 17;105(8):515–25. 55. Bjerkaas E, Parajuli R, Weiderpass E, Engeland A, Maskarinec G, Selmer R, et al. Smoking duration before first childbirth: an emerging risk factor for breast cancer? Results from 302,865 Norwegian women. Cancer Causes Control. 2013 Jul 1;24(7):1347–56. 56. Kim AS, Ko HJ, Kwon JH, Lee JM. Exposure to Secondhand Smoke and Risk of Cancer in Never Smokers: A Meta-Analysis of Epidemiologic Studies. Int J Environ Res Public Health. 2018 Sep;15(9):1981. 57. Biglia N, Maggiorotto F, Liberale V, Bounous VE, Sgro LG, Pecchio S, et al. Clinical-pathologic features, long term-outcome and surgical treatment in a large series of patients with invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC). Eur J Surg Oncol EJSO. 2013 May 1;39(5):455–60. 58. Orrantia-Borunda E, Anchondo-Nuñez P, Acuña-Aguilar LE, Gómez-Valles FO, Ramírez-Valdespino CA. Subtypes of Breast Cancer. In: Mayrovitz HN, editor. Breast Cancer. Brisbane (AU): Exon Publications; 2022 Aug 6. Chapter 3. 59. Dossus L, Benusiglio PR. Lobular breast cancer: incidence and genetic and non-genetic risk factors. Breast Cancer Res BCR. 2015;17:37. 60. Barnard ME, Boeke CE, Tamimi RM. Established breast cancer risk factors and risk of intrinsic tumor subtypes. Biochim Biophys Acta BBA - Rev Cancer. 2015 Aug 1;1856(1):73–85. 61. Li CI, Anderson BO, Daling JR, Moe RE. Trends in Incidence Rates of Invasive Lobular and Ductal Breast Carcinoma. JAMA. 2003 Mar 19;289(11):1421–4. 62. Eheman CR, Shaw KM, Ryerson AB, Miller JW, Ajani UA, White MC. The Changing Incidence of In situ and Invasive Ductal and Lobular Breast Carcinomas: United States, 1999-2004. Cancer Epidemiol Biomarkers Prev. 2009 Jun 8;18(6):1763–9. 63. Al-thoubaity FK. Molecular classification of breast cancer: A retrospective cohort study. Ann Med Surg. 2020 Jan;49:44–8. 64. Lee S. Canadian Cancer Society. [cited 2023 Aug 13]. Ductal carcinoma. Available from: https://cancer.ca/en/cancer-information/cancer-types/breast/what-is-breast-cancer/cancerous-tumours/ductal-carcinoma 65. Lee S. Canadian Cancer Society. [cited 2023 Aug 18]. Lobular carcinoma. Available from: https://cancer.ca/en/cancer-information/cancer-types/breast/what-is-breast-cancer/cancerous-tumours/lobular-carcinoma 66. Dieci MV, Orvieto E, Dominici M, Conte P, Guarneri V. Rare Breast Cancer Subtypes: Histological, Molecular, and Clinical Peculiarities. The Oncologist. 2014 Aug;19(8):805–13. 67. Jenkins S, Kachur ME, Rechache K, Wells JM, Lipkowitz S. Rare Breast Cancer Subtypes. Curr Oncol Rep. 2021 Mar 23;23(5):54. 54 68. Corso G, Intra M, Trentin C, Veronesi P, Galimberti V. CDH1 germline mutations and hereditary lobular breast cancer. Fam Cancer. 2016 Apr 1;15(2):215–9. 69. Rosenberg LU, Magnusson C, Lindström E, Wedrén S, Hall P, Dickman PW. Menopausal hormone therapy and other breast cancer risk factors in relation to the risk of different histological subtypes of breast cancer: a case-control study. Breast Cancer Res. 2006 Feb 17;8(1):R11. 70. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000 Aug;406(6797):747–52. 71. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001 Sep 11;98(19):10869–74. 72. Hortobagyi GN, Connolly JL, D’Orsi CJ, Edge SB, Mittendorf EA, Rugo HS, et al. Breast. In: Amin MB, Edge SB, Greene FL, Byrd DR, Brookland RK, Washington MK, et al., editors. AJCC Cancer Staging Manual. Cham: Springer International Publishing; 2017. p. 589–636. 73. Howlader N, Altekruse SF, Li CI, Chen VW, Clarke CA, Ries LAG, et al. US Incidence of Breast Cancer Subtypes Defined by Joint Hormone Receptor and HER2 Status. JNCI J Natl Cancer Inst. 2014 May 1;106(5):dju055. 74. Yang XR, Chang-Claude J, Goode EL, Couch FJ, Nevanlinna H, Milne RL, et al. Associations of Breast Cancer Risk Factors With Tumor Subtypes: A Pooled Analysis From the Breast Cancer Association Consortium Studies. JNCI J Natl Cancer Inst. 2011 Feb 2;103(3):250–63. 75. Johnson KS, Conant EF, Soo MS. Molecular Subtypes of Breast Cancer: A Review for Breast Radiologists. J Breast Imaging. 2021 Jan 1;3(1):12–24. 76. Cronin KA, Harlan LC, Dodd KW, Abrams JS, Ballard-Barbash R. Population-based Estimate of the Prevalence of HER-2 Positive Breast Cancer Tumors for Early Stage Patients in the US. Cancer Invest. 2010 Nov;28(9):963–8. 77. Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data. CMAJ Open. 2017 Sep 25;5(3):E734–9. 78. Acheampong T, Kehm RD, Terry MB, Argov EL, Tehranifar P. Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US From 2010 to 2016. JAMA Netw Open. 2020 Aug 17;3(8):e2013226. 79. Plasilova ML, Hayse B, Killelea BK, Horowitz NR, Chagpar AB, Lannin DR. Features of triple-negative breast cancer. Medicine (Baltimore). 2016 Sep 2;95(35):e4614. 80. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, et al. Triple-Negative Breast Cancer: Clinical Features and Patterns of Recurrence. Clin Cancer Res. 2007 Aug 1;13(15):4429–34. 81. Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, et al. Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study. JAMA. 2006 Jun 7;295(21):2492–502. 55 82. Parise CA, Bauer KR, Caggiano V. Variation in breast cancer subtypes with age and race/ethnicity. Crit Rev Oncol Hematol. 2010 Oct;76(1):44–52. 83. Newman LA. Disparities in Breast Cancer and African Ancestry: A Global Perspective. Breast J. 2015;21(2):133–9. 84. Pierobon M, Frankenfeld CL. Obesity as a risk factor for triple-negative breast cancers: a systematic review and meta-analysis. Breast Cancer Res Treat. 2013 Jan 1;137(1):307–14. 85. Cobain EF, Milliron KJ, Merajver SD. Updates on breast cancer genetics: Clinical implications of detecting syndromes of inherited increased susceptibility to breast cancer. Semin Oncol. 2016 Oct 1;43(5):528–35. 86. Gaudet MM, Gierach GL, Carter BD, Luo J, Milne RL, Weiderpass E, et al. Pooled Analysis of Nine Cohorts Reveals Breast Cancer Risk Factors by Tumor Molecular Subtype. Cancer Res. 2018 Oct 15;78(20):6011–21. 87. Hirko KA, Chen WY, Willett WC, Rosner BA, Hankinson SE, Beck AH, et al. Alcohol consumption and risk of breast cancer by molecular subtype: prospective analysis of the Nurses’ Health Study after 26 years of follow-up. Int J Cancer J Int Cancer. 2016 Mar 1;138(5):1094–101. 88. Carbine NE, Lostumbo L, Wallace J, Ko H. Risk‐reducing mastectomy for the prevention of primary breast cancer. Cochrane Database Syst Rev. 2018 Apr 5;4(4):CD002748. 89. Cuzick J, Sestak I, Bonanni B, Costantino JP, Cummings S, DeCensi A, et al. Selective oestrogen receptor modulators in prevention of breast cancer: an updated meta-analysis of individual participant data. The Lancet. 2013 May 25;381(9880):1827–34. 90. Olin JL, St. Pierre M. Aromatase Inhibitors in Breast Cancer Prevention. Ann Pharmacother. 2014 Dec 1;48(12):1605–10. 91. Smith SG, Sestak I, Forster A, Partridge A, Side L, Wolf MS, et al. Factors affecting uptake and adherence to breast cancer chemoprevention: a systematic review and meta-analysis. Ann Oncol. 2016 Apr 1;27(4):575–90. 92. Britt KL, Cuzick J, Phillips KA. Key steps for effective breast cancer prevention. Nat Rev Cancer. 2020 Aug;20(8):417–36. 93. Wolin KY, Colditz GA. Can weight loss prevent cancer? Br J Cancer. 2008 Oct;99(7):995–9. 94. Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE. Adult Weight Change and Risk of Postmenopausal Breast Cancer. JAMA. 2006 Jul 12;296(2):193–201. 95. Lemstra M, Bird Y, Nwankwo C, Rogers M, Moraros J. Weight loss intervention adherence and factors promoting adherence: a meta-analysis. Patient Prefer Adherence. 2016 Dec 31;10:1547–59. 96. Gibson AA, Sainsbury A. Strategies to Improve Adherence to Dietary Weight Loss Interventions in Research and Real-World Settings. Behav Sci. 2017 Sep;7(3):44. 56 97. Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Easton DF, French D, et al. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol. 2020 Nov;17(11):687–705. 98. Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, et al. Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature. JAMA. 2017 Oct 10;318(14):1377–84. 99. Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, et al. Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography. JNCI J Natl Cancer Inst. 2006 Sep 6;98(17):1204–14. 100. Rosner B, Colditz GA, Iglehart JD, Hankinson SE. Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses’ Health Study. Breast Cancer Res. 2008 Aug;10(4):R55. 101. Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017 Dec;19(1):29. 102. Ueda K, Tsukuma H, Tanaka H, Ajiki W, Oshima A. Estimation of individualized probabilities of developing breast cancer for Japanese women. Breast Cancer. 2003 Jan;10(1):54–62. 103. Wang Y, Gao Y, Battsend M, Chen K, Lu W, Wang Y. Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women. Tumor Biol. 2014 Nov;35(11):10861–9. 104. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting Individualized Probabilities of Developing Breast Cancer for White Females Who Are Being Examined Annually. JNCI J Natl Cancer Inst. 1989 Dec 20;81(24):1879–86. 105. Banegas MP, John EM, Slattery ML, Gomez SL, Yu M, LaCroix AZ, et al. Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women. J Natl Cancer Inst. 2017 Feb;109(2):djw215. 106. Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al. Projecting Absolute Invasive Breast Cancer Risk in White Women With a Model That Includes Mammographic Density. JNCI J Natl Cancer Inst. 2006 Sep 6;98(17):1215–26. 107. Decarli A, Calza S, Masala G, Specchia C, Palli D, Gail MH. Gail Model for Prediction of Absolute Risk of Invasive Breast Cancer: Independent Evaluation in the Florence–European Prospective Investigation Into Cancer and Nutrition Cohort. JNCI J Natl Cancer Inst. 2006 Dec 6;98(23):1686–93. 108. Gail MH, Costantino JP, Pee D, Bondy M, Newman L, Selvan M, et al. Projecting Individualized Absolute Invasive Breast Cancer Risk in African American Women. JNCI J Natl Cancer Inst. 2007 Dec 5;99(23):1782–92. 109. Matsuno RK, Costantino JP, Ziegler RG, Anderson GL, Li H, Pee D, et al. Projecting Individualized Absolute Invasive Breast Cancer Risk in Asian and Pacific Islander American Women. JNCI J Natl Cancer Inst. 2011 Jun 22;103(12):951–61. 57 110. Boyle P, Mezzetti M, La Vecchia C, Franceschi S, Decarli A, Robertson C. Contribution of three components to individual cancer risk predicting breast cancer risk in Italy. Eur J Cancer Prev Off J Eur Cancer Prev Organ ECP. 2004 Jun;13(3):183–91. 111. Novotny J, Pecen L, Petruzelka L, Svobodnik A, Dusek L, Danes J, et al. Breast cancer risk assessment in the Czech female population – an adjustment of the original Gail model. Breast Cancer Res Treat. 2006 Jan;95(1):29–35. 112. Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population. Breast Cancer Res Treat. 2005 Nov;94(2):115–22. 113. Bevers TB, Niell BL, Baker JL, Bennett DL, Bonaccio E, Camp MS, et al. NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023: Featured Updates to the NCCN Guidelines. J Natl Compr Canc Netw. 2023 Sep;21(9):900–9. 114. Antoniou AC, Pharoah PDP, McMullan G, Day NE, Ponder BAJ, Easton D. Evidence for further breast cancer susceptibility genes in addition toBRCA1 andBRCA2 in a population-based study. Genet Epidemiol. 2001 Jul;21(1):1–18. 115. Antoniou AC, Pharoah PDP, McMullan G, Day NE, Stratton MR, Peto J, et al. A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer. 2002 Jan;86(1):76–83. 116. Antoniou AC, Pharoah PPD, Smith P, Easton DF. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004 Oct;91(8):1580–90. 117. Antoniou AC, Cunningham AP, Peto J, Evans DG, Lalloo F, Narod SA, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer. 2008 Apr;98(8):1457–66. 118. Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019 Aug;21(8):1708–18. 119. Carver T, Hartley S, Lee A, Cunningham AP, Archer S, Babb de Villiers C, et al. CanRisk Tool-A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2021 Mar;30(3):469–73. 120. Ontario Breast Screening Program (OBSP) Guidelines Summary [Internet]. Cancer Care Ontario; 2015 [cited 2023 Sep 13]. Available from: https://www.cancercareontario.ca/sites/ccocancercare/files/assets/OBSPGuidelinesSummary.pdf 121. Recommendations | Familial breast cancer: classification, care and managing breast cancer and related risks in people with a family history of breast cancer | Guidance | NICE [Internet]. NICE; 2013 [cited 2023 Sep 13]. Available from: https://www.nice.org.uk/guidance/cg164/chapter/Recommendations 122. Ciardiello F, Arnold D, Casali PG, Cervantes A, Douillard JY, Eggermont A, et al. Delivering precision medicine in oncology today and in future—the promise and challenges of personalised 58 cancer medicine: a position paper by the European Society for Medical Oncology (ESMO). Ann Oncol. 2014 Sep;25(9):1673–8. 123. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004 Apr 15;23(7):1111–30. 124. Brentnall AR, Harkness EF, Astley SM, Donnelly LS, Stavrinos P, Sampson S, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015 Dec;17(1):147. 125. Quante AS, Whittemore AS, Shriver T, Strauch K, Terry MB. Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance. Breast Cancer Res. 2012 Dec;14(6):R144. 126. US Preventive Services Task Force, Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, et al. Risk Assessment, Genetic Counseling, and Genetic Testing for BRCA-Related Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2019 Aug 20;322(7):652. 127. Louro J, Posso M, Hilton Boon M, Román M, Domingo L, Castells X, et al. A systematic review and quality assessment of individualised breast cancer risk prediction models. Br J Cancer. 2019 Jul 2;121(1):76–85. 128. Stark GF, Hart GR, Nartowt BJ, Deng J. Predicting breast cancer risk using personal health data and machine learning models. Saito PTM, editor. PLOS ONE. 2019 Dec 27;14(12):e0226765. 129. Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Res. 2019 Dec;21(1):75. 130. Ming C, Viassolo V, Probst-Hensch N, Dinov ID, Chappuis PO, Katapodi MC. Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations. Br J Cancer. 2020 Sep 1;123(5):860–7. 131. Zou S, Lin Y, Yu X, Eriksson M, Lin M, Fu F, et al. Genetic and lifestyle factors for breast cancer risk assessment in Southeast China. Cancer Med. 2023;12(14):15504–14. 132. Gao Y, Li S, Jin Y, Zhou L, Sun S, Xu X, et al. An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review. JMIR Public Health Surveill. 2022 Dec 29;8(12):e35750. 133. David A, Rostkowski P. Analytical techniques in metabolomics. In: Environmental Metabolomics. Elsevier; 2020. p. 35–64. 134. Fiehn O. Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol. 2002 Jan 1;48(1):155–71. 135. Chen L, Zhernakova DV, Kurilshikov A, Andreu-Sánchez S, Wang D, Augustijn HE, et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat Med. 2022 Nov;28(11):2333–43. 59 136. Bermingham KM, Brennan L, Segurado R, Barron RE, Gibney ER, Ryan MF, et al. Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study. J Proteome Res. 2021 Aug 6;20(8):3992–4000. 137. Beuchel C, Becker S, Dittrich J, Kirsten H, Toenjes A, Stumvoll M, et al. Clinical and lifestyle related factors influencing whole blood metabolite levels – A comparative analysis of three large cohorts. Mol Metab. 2019 Aug 17;29:76–85. 138. Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, et al. Metabolomics in Epidemiology: Sources of Variability in Metabolite Measurements and Implications. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2013 Apr;22(4):631–40. 139. Manolio TA, Bailey-Wilson JE, Collins FS. Genes, environment and the value of prospective cohort studies. Nat Rev Genet. 2006 Oct;7(10):812–20. 140. Wishart DS. NMR metabolomics: A look ahead. J Magn Reson. 2019 Sep;306:155–61. 141. Collins SL, Koo I, Peters JM, Smith PB, Patterson AD. Current Challenges and Recent Developments in Mass Spectrometry–Based Metabolomics. Annu Rev Anal Chem. 2021 Jul 27;14(1):467–87. 142. Galal A, Talal M, Moustafa A. Applications of machine learning in metabolomics: Disease modeling and classification. Front Genet. 2022 Nov 24;13:1017340. 143. Brantley KD, Zeleznik OA, Rosner B, Tamimi RM, Avila-Pacheco J, Clish CB, et al. Plasma Metabolomics and Breast Cancer Risk over 20 Years of Follow-up among Postmenopausal Women in the Nurses’ Health Study. Cancer Epidemiol Biomarkers Prev. 2022 Apr 1;31(4):839–50. 144. Brantley KD, Zeleznik OA, Dickerman BA, Balasubramanian R, Clish CB, Avila-Pacheco J, et al. A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses’ Health Studies. Br J Cancer. 2022 Oct;127(6):1076–85. 145. Breeur M, Ferrari P, Dossus L, Jenab M, Johansson M, Rinaldi S, et al. Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition. BMC Med. 2022 Oct 19;20:351. 146. Bro R, Kamstrup-Nielsen MH, Engelsen SB, Savorani F, Rasmussen MA, Hansen L, et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics. 2015;11(5):1376–80. 147. Chadeau-Hyam M, Athersuch TJ, Keun HC, De Iorio M, Ebbels TMD, Jenab M, et al. Meeting-in-the-middle using metabolic profiling – a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers. 2011 Feb;16(1):83–8. 148. Chang VC, Rhee J, Berndt SI, Moore SC, Freedman ND, Jones RR, et al. Serum perfluorooctane sulfonate and perfluorooctanoate and risk of postmenopausal breast cancer according to hormone receptor status: An analysis in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Int J Cancer. 2023;153(4):775–82. 60 149. Go YM, Weinberg J, Teeny S, Cirillo PM, Krigbaum NY, Singer G, et al. Exposome epidemiology for suspect environmental chemical exposures during pregnancy linked to subsequent breast cancer diagnosis. Environ Int. 2023 Aug 1;178:108112. 150. His M, Viallon V, Dossus L, Gicquiau A, Achaintre D, Scalbert A, et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med. 2019 Sep 24;17:178. 151. Jobard E, Dossus L, Baglietto L, Fornili M, Lécuyer L, Mancini FR, et al. Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case–control study nested within the French E3N cohort. Br J Cancer. 2021 May;124(10):1734–43. 152. Kühn T, Floegel A, Sookthai D, Johnson T, Rolle-Kampczyk U, Otto W, et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med. 2016 Jan 28;14:13. 153. Lécuyer L, Dalle C, Lefevre-Arbogast S, Micheau P, Lyan B, Rossary A, et al. Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort. Cancer Epidemiol Biomarkers Prev. 2020 Feb 1;29(2):396–405. 154. Lécuyer L, Dalle C, Lyan B, Demidem A, Rossary A, Vasson MP, et al. Plasma Metabolomic Signatures Associated with Long-term Breast Cancer Risk in the SU.VI.MAX Prospective Cohort. Cancer Epidemiol Biomarkers Prev. 2019 Aug 1;28(8):1300–7. 155. Lécuyer L, Victor Bala A, Deschasaux M, Bouchemal N, Nawfal Triba M, Vasson MP, et al. NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer. Int J Epidemiol. 2018 Apr 1;47(2):484–94. 156. Moore SC, Playdon MC, Sampson JN, Hoover RN, Trabert B, Matthews CE, et al. A Metabolomics Analysis of Body Mass Index and Postmenopausal Breast Cancer Risk. JNCI J Natl Cancer Inst. 2018 Jun 1;110(6):588-597. 157. Moore SC, Mazzilli KM, Sampson JN, Matthews CE, Carter BD, Playdon MC, et al. A Metabolomics Analysis of Postmenopausal Breast Cancer Risk in the Cancer Prevention Study II. Metabolites. 2021 Feb 10;11(2):95. 158. Mrowiec K, Kurczyk A, Jelonek K, Debik J, Giskeødegård GF, Bathen TF, et al. Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study. Front Oncol. 2023 Mar 16;13:1116806. 159. Ong KR, Sims AH, Harvie M, Chapman M, Dunn WB, Broadhurst D, et al. Biomarkers of Dietary Energy Restriction in Women at Increased Risk of Breast Cancer. Cancer Prev Res (Phila Pa). 2009 Aug 4;2(8):720–31. 160. Playdon MC, Ziegler RG, Sampson JN, Stolzenberg-Solomon R, Thompson HJ, Irwin ML, et al. Nutritional metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr. 2017 Aug;106(2):637–49. 161. Stevens VL, Carter BD, Jacobs EJ, McCullough ML, Teras LR, Wang Y. A prospective case–cohort analysis of plasma metabolites and breast cancer risk. Breast Cancer Res BCR. 2023;25:5. 61 162. Tobias DK, Akinkuolie AO, Chandler PD, Lawler PR, Manson JE, Buring JE, et al. Markers of Inflammation and Incident Breast Cancer Risk in the Women’s Health Study. Am J Epidemiol. 2018 Apr;187(4):705–16. 163. Zhao H, Shen J, Ye Y, Wu X, Esteva FJ, Tripathy D, et al. Validation of plasma metabolites associated with breast cancer risk among Mexican Americans. Cancer Epidemiol. 2020 Dec;69:101826. 164. BC Generations Project [Internet]. [cited 2023 Aug 22]. The Project. Available from: https://www.bcgenerationsproject.ca/about/the-project/ 165. Ye M, Robson PJ, Eurich DT, Vena JE, Xu JY, Johnson JA. Cohort Profile: Alberta’s Tomorrow Project. Int J Epidemiol. 2017 Aug 1;46(4):1097–1098l. 166. Dummer TJB, Awadalla P, Boileau C, Craig C, Fortier I, Goel V, et al. The Canadian Partnership for Tomorrow Project: a pan-Canadian platform for research on chronic disease prevention. CMAJ Can Med Assoc J. 2018 Jun 11;190(23):E710–7. 167. BC Generations Project Open Access Biosample Information [Internet]. [cited 2023 Aug 22]. Available from: https://www.bcgenerationsproject.ca/researchers/bc-generations-project-open-access-biosample-information/ 168. Survey Questions Asked - Alberta’s Tomorrow Project [Internet]. [cited 2023 Aug 22]. Available from: https://myatpresearch.ca/survey-questions/ 169. Borugian MJ, Robson P, Fortier I, Parker L, McLaughlin J, Knoppers BM, et al. The Canadian Partnership for Tomorrow Project: building a pan-Canadian research platform for disease prevention. CMAJ. 2010 Aug 10;182(11):1197–201. 170. Morphology & Grade | SEER Training [Internet]. [cited 2023 Jun 21]. Available from: https://training.seer.cancer.gov/breast/abstract-code-stage/morphology.html 171. Costanian C, McCague H, Tamim H. Age at natural menopause and its associated factors in Canada: cross-sectional analyses from the Canadian Longitudinal Study on Aging. Menopause. 2018 Mar;25(3):265. 172. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011 Feb 24;20(1):40–9. 173. Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009 Jun 29;338:b2393. 174. Fuhrer T, Heer D, Begemann B, Zamboni N. High-Throughput, Accurate Mass Metabolome Profiling of Cellular Extracts by Flow Injection–Time-of-Flight Mass Spectrometry. Anal Chem. 2011 Sep 15;83(18):7074–80. 175. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018 Jan 4;46(D1):D608–17. 62 176. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016 Jan 4;44(D1):D457–62. 177. Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res. 2016 Jan 4;44(D1):D1214–9. 178. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011 Jul;6(7):1060–83. 179. Zhang G, Deng Q, Mandal R, Wishart DS, Ametaj BN. DI/LC-MS/MS-Based Metabolic Profiling for Identification of Early Predictive Serum Biomarkers of Metritis in Transition Dairy Cows. J Agric Food Chem. 2017 Sep 27;65(38):8510–21. 180. Gromski PS, Xu Y, Kotze HL, Correa E, Ellis DI, Armitage EG, et al. Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data. Metabolites. 2014 Jun 16;4(2):433–52. 181. Lee HJ, Kremer DM, Sajjakulnukit P, Zhang L, Lyssiotis CA. A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics. Metabolomics. 2019 Jul 9;15(7):103. 182. Reed GF, Lynn F, Meade BD. Use of Coefficient of Variation in Assessing Variability of Quantitative Assays. Clin Diagn Lab Immunol. 2002 Nov;9(6):1235–9. 183. Workman C, Jensen LJ, Jarmer H, Berka R, Gautier L, Nielser HB, et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 2002;3(9):research0048.1-research0048.16. 184. Kohl SM, Klein MS, Hochrein J, Oefner PJ, Spang R, Gronwald W. State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics. 2012 Jun;8(S1):146–60. 185. Hotelling H. Analysis of a complex of statistical variables into principal components. J Educ Psychol. 1933 Sep;24(6):417–41. 186. Gandolfo LC, Speed TP. RLE plots: Visualizing unwanted variation in high dimensional data. PLoS ONE. 2018 Feb 5;13(2):e0191629. 187. Reinhold D, Pielke-Lombardo H, Jacobson S, Ghosh D, Kechris K. Pre-analytic considerations for mass spectrometry based untargeted metabolomics data. Methods Mol Biol Clifton NJ. 2019;1978:323–40. 188. Stanfill B, Reehl S, Bramer L, Nakayasu ES, Rich SS, Metz TO, et al. Extending Classification Algorithms to Case-Control Studies. Biomed Eng Comput Biol. 2019 Jul 15;10:1179597219858954. 189. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol. 1995 Jan;57(1):289–300. 63 190. Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987. 258 p. (Wiley series in probability and mathematical statistics). 191. Tibshirani R. Regression Shrinkage and Selection Via the Lasso. J R Stat Soc Ser B Methodol. 1996;58(1):267–88. 192. Barker M, Rayens W. Partial least squares for discrimination. J Chemom. 2003;17(3):166–73. 193. Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998 Aug;20(8):832–44. 194. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory. New York, NY, USA: Association for Computing Machinery; 1992. p. 144–52. 195. Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 1999 Jul 1;12(6):783–9. 196. Hicks SA, Strümke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, et al. On evaluation metrics for medical applications of artificial intelligence. Sci Rep. 2022 Apr 8;12:5979. 197. Kuhn M. 15 Variable Importance. In: The caret Package [Internet]. [cited 2024 Mar 31]. Available from: https://topepo.github.io/caret/variable-importance.html 198. Paulo Cortez. rminer: Data Mining Classification and Regression Methods [Internet]. 2016 [cited 2024 Mar 15]. Available from: https://CRAN.R-project.org/package=rminer 199. Rahman MHA, Gullick DR, Hoerner J, Bartlett MG. Determination of genotoxic impurities monomethyl sulfate and dimethyl sulfate in active pharmaceutical ingredients. Anal Methods. 2017 Feb 16;9(7):1112–8. 200. Chen J, Zhou Q, Zhang Y, Tan W, Gao H, Zhou L, et al. Discovery of novel serum metabolic biomarkers in patients with polycystic ovarian syndrome and premature ovarian failure. Bioengineered. 2021 Jan 1;12(1):8778–92. 201. Review of Exposure and Toxicity Data for Phthalate Substitutes [Internet]. United States Consumer Product Safety Commision; 2010 Jan. Available from: https://www.cpsc.gov/content/Review-of-Exposure-and-Toxicity-Data-for-Phthalate-Substitutes 202. Takeshita A, Igarashi-Migitaka J, Nishiyama K, Takahashi H, Takeuchi Y, Koibuchi N. Acetyl Tributyl Citrate, the Most Widely Used Phthalate Substitute Plasticizer, Induces Cytochrome P450 3A through Steroid and Xenobiotic Receptor. Toxicol Sci. 2011 Oct 1;123(2):460–70. 203. Rasmussen LM, Sen N, Liu X, Craig ZR. Effects of oral exposure to the phthalate substitute acetyl tributyl citrate on female reproduction in mice. J Appl Toxicol. 2017;37(6):668–75. 204. Rasmussen LM, Sen N, Vera JC, Liu X, Craig ZR. Effects of in vitro exposure to dibutyl phthalate, mono-butyl phthalate, and acetyl tributyl citrate on ovarian antral follicle growth and viability. Biol Reprod. 2017 May 1;96(5):1105–17. 64 205. Anlar HG. Chapter 23 - Cinnamic acid as a dietary antioxidant in diabetes treatment. In: Preedy VR, editor. Diabetes (Second Edition). Academic Press; 2020. p. 235–43. 206. Clifford MN. Chlorogenic acids and other cinnamates - nature, occurrence, dietary burden, absorption and metabolism. J Sci Food Agric. 2000 May 15;80(7):1033–43. 207. Meirelles LE de F, de Souza MVF, Carobeli LR, Morelli F, Mari NL, Damke E, et al. Combination of Conventional Drugs with Biocompounds Derived from Cinnamic Acid: A Promising Option for Breast Cancer Therapy. Biomedicines. 2023 Jan 19;11(2):275. 208. Mirzaei S, Gholami MH, Zabolian A, Saleki H, Farahani MV, Hamzehlou S, et al. Caffeic acid and its derivatives as potential modulators of oncogenic molecular pathways: New hope in the fight against cancer. Pharmacol Res. 2021 Sep 1;171:105759. 209. Guo W, Yan M, Xu B, Chu F, Wang W, Zhang C, et al. Design, synthesis, and biological evaluation of the novel glycyrrhetinic acid-cinnamoyl hybrids as anti-tumor agents. Chem Cent J. 2016 Dec 1;10:78. 210. Badawi NM, Attia YM, El-Kersh DM, Hammam OA, Khalifa MKA. Investigating the Impact of Optimized Trans-Cinnamic Acid-Loaded PLGA Nanoparticles on Epithelial to Mesenchymal Transition in Breast Cancer. Int J Nanomedicine. 2022 Feb 18;17:733–50. 211. Kostrhunova H, Zajac J, Markova L, Brabec V, Kasparkova J. A Multi-action PtIV Conjugate with Oleate and Cinnamate Ligands Targets Human Epithelial Growth Factor Receptor HER2 in Aggressive Breast Cancer Cells. Angew Chem Int Ed. 2020;59(47):21157–62. 212. Chin YW, Kinghorn AD. Structural Characterization, Biological Effects, and Synthetic Studies on Xanthones from Mangosteen (Garcinia mangostana), a Popular Botanical Dietary Supplement. Mini-Rev Org Chem. 2008 Nov 1;5(4):355–64. 213. Salehi B, Venditti A, Frezza C, Yücetepe A, Altuntaş Ü, Uluata S, et al. Apium Plants: Beyond Simple Food and Phytopharmacological Applications. Appl Sci. 2019 Jan;9(17):3547. 214. Marra M, Camoni L, Visconti S, Fiorillo A, Evidente A. The Surprising Story of Fusicoccin: A Wilt-Inducing Phytotoxin, a Tool in Plant Physiology and a 14-3-3-Targeted Drug. Biomolecules. 2021 Sep 21;11(9):1393. 215. Mbaveng AT, Hamm R, Kuete V. 19 - Harmful and Protective Effects of Terpenoids from African Medicinal Plants. In: Kuete V, editor. Toxicological Survey of African Medicinal Plants. Elsevier; 2014. p. 557–76. 216. Yang M, Li X, Zhang L, Wang C, Ji M, Xu J, et al. Ethnopharmacology, Phytochemistry, and Pharmacology of the Genus Glehnia: A Systematic Review. Evid-Based Complement Altern Med ECAM. 2019 Dec 14;2019:1253493. 217. Gutierrez-Orozco F, Failla ML. Biological Activities and Bioavailability of Mangosteen Xanthones: A Critical Review of the Current Evidence. Nutrients. 2013 Aug 13;5(8):3163–83. 218. Urushino N, Yasuda K, Ikushiro S, Kamakura M, Ohta M, Sakaki T. Metabolism of 1α,25-dihydroxyvitamin D2 by human CYP24A1. Biochem Biophys Res Commun. 2009 Jun;384(2):144–8. 65 219. Zinser GM, Tribble E, Valrance M, Urben CM, Knutson JC, Mazess RB, et al. 1,24(S)-Dihydroxyvitamin D2, an Endogenous Vitamin D2 Metabolite, Inhibits Growth of Breast Cancer Cells and Tumors. Anticancer Res. 2005 Jan 1;25(1A):235–41. 220. Estébanez N, Gómez-Acebo I, Palazuelos C, Llorca J, Dierssen-Sotos T. Vitamin D exposure and Risk of Breast Cancer: a meta-analysis. Sci Rep. 2018 Jun 13;8:9039. 221. Hossain S, Beydoun MA, Beydoun HA, Chen X, Zonderman AB, Wood RJ. Vitamin D and breast cancer: A systematic review and meta-analysis of observational studies. Clin Nutr ESPEN. 2019 Apr;30:170–84. 222. Song D, Deng Y, Liu K, Zhou L, Li N, Zheng Y, et al. Vitamin D intake, blood vitamin D levels, and the risk of breast cancer: a dose-response meta-analysis of observational studies. Aging. 2019 Dec 28;11(24):12708–32. 223. Visvanathan K, Mondul AM, Zeleniuch-Jacquotte A, Wang M, Gail MH, Yaun SS, et al. Circulating vitamin D and breast cancer risk: an international pooling project of 17 cohorts. Eur J Epidemiol. 2023 Jan;38(1):11–29. 224. Nomura DK, Long JZ, Niessen S, Hoover HS, Ng SW, Cravatt BF. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell. 2010 Jan 1;140(1):49. 225. Zhabinskii VN, Drasar P, Khripach VA. Structure and Biological Activity of Ergostane-Type Steroids from Fungi. Molecules. 2022 Mar 24;27(7):2103. 226. Jahnke GD, Atwood S, Ewens A, Garner S, Peters A, Spencer DL, et al. Report on Carcinogens Monograph on Cumene. National Toxicology Program; 2013. 227. Iida S, Ohkubo Y, Yamamoto Y, Fujisawa A. Parabanic acid is the singlet oxygen specific oxidation product of uric acid. J Clin Biochem Nutr. 2017 Nov;61(3):169–75. 228. Hayes JD, Dinkova-Kostova AT, Tew KD. Oxidative Stress in Cancer. Cancer Cell. 2020 Aug 10;38(2):167–97. 229. Pietzke M, Arroyo SF, Sumpton D, Mackay GM, Martin-Castillo B, Camps J, et al. Stratification of cancer and diabetes based on circulating levels of formate and glucose. Cancer Metab. 2019 Apr 24;7:3. 230. Lin Y, Yang Z, Li J, Sun Y, Zhang X, Qu Z, et al. Effects of glutamate and aspartate on prostate cancer and breast cancer: a Mendelian randomization study. BMC Genomics. 2022 Mar 16;23:213. 231. Poschke I, Mao Y, Kiessling R, de Boniface J. Tumor-dependent increase of serum amino acid levels in breast cancer patients has diagnostic potential and correlates with molecular tumor subtypes. J Transl Med. 2013 Nov 16;11:290. 232. Krall AS, Mullen PJ, Surjono F, Momcilovic M, Schmid EW, Halbrook CJ, et al. Asparagine couples mitochondrial respiration to ATF4 activity and tumor growth. Cell Metab. 2021 May 4;33(5):1013-1026.e6. 66 233. Huang S, Chong N, Lewis NE, Jia W, Xie G, Garmire LX. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis. Genome Med. 2016 Mar 31;8(1):34. 234. Chen C, Ye L, Yi J, Liu T, Li Z. FN1 mediated activation of aspartate metabolism promotes the progression of triple-negative and luminal a breast cancer. Breast Cancer Res Treat. 2023 Oct 1;201(3):515–33. 235. Dayrit FM. The Properties of Lauric Acid and Their Significance in Coconut Oil. J Am Oil Chem Soc. 2015 Jan 1;92(1):1–15. 236. Roopashree PG, Shetty SS, Shetty VV, Nalilu SK. Medium-Chain Fatty Acids and Breast Cancer Risk by Receptor and Pathological Subtypes. Nutrients. 2022 Dec 16;14(24):5351. 237. Shen J, Yan L, Liu S, Ambrosone CB, Zhao H. Plasma Metabolomic Profiles in Breast Cancer Patients and Healthy Controls: By Race and Tumor Receptor Subtypes. Transl Oncol. 2013 Dec 1;6(6):757–65. 238. Lappano R, Sebastiani A, Cirillo F, Rigiracciolo DC, Galli GR, Curcio R, et al. The lauric acid-activated signaling prompts apoptosis in cancer cells. Cell Death Discov. 2017 Sep 18;3:17063. 239. Sheela D, Narayanankutty A, Nazeem P, Raghavamenon A, Muthangaparambil S. Lauric acid induce cell death in colon cancer cells mediated by the epidermal growth factor receptor downregulation: An in silico and in vitro study. Hum Exp Toxicol. 2019 Jul 1;38(7):753–61. 240. Masuda H, Zhang D, Bartholomeusz C, Doihara H, Hortobagyi GN, Ueno NT. Role of Epidermal Growth Factor Receptor in Breast Cancer. Breast Cancer Res Treat. 2012 Nov;136(2):10.1007/s10549-012-2289–9. 241. Verruck S, Balthazar CF, Rocha RS, Silva R, Esmerino EA, Pimentel TC, et al. Dairy foods and positive impact on the consumer’s health. In: Advances in Food and Nutrition Research. Elsevier; 2019. p. 95–164. 242. Matta M, Deubler E, Chajes V, Vozar B, Gunter MJ, Murphy N, et al. Circulating plasma phospholipid fatty acid levels and breast cancer risk in the Cancer Prevention Study-II Nutrition Cohort. Int J Cancer. 2022;151(12):2082–94. 243. Nagarajan SR, Butler LM, Hoy AJ. The diversity and breadth of cancer cell fatty acid metabolism. Cancer Metab. 2021 Jan 7;9(1):2. 244. Kamaruzman NI, Aziz NA, Poh CL, Chowdhury EH. Oncogenic Signaling in Tumorigenesis and Applications of siRNA Nanotherapeutics in Breast Cancer. Cancers. 2019 May;11(5):632. 245. Venn-Watson S, Lumpkin R, Dennis EA. Efficacy of dietary odd-chain saturated fatty acid pentadecanoic acid parallels broad associated health benefits in humans: could it be essential? Sci Rep. 2020 May 18;10(1):8161. 246. Zhang Y, Song L, Liu N, He C, Li Z. Decreased serum levels of free fatty acids are associated with breast cancer. Clin Chim Acta. 2014 Nov;437:31–7. 67 247. London SJ, Sacks FM, Stampfer MJ, Henderson IC, Maclure M, Tomita A, et al. Fatty Acid Composition of the Subcutaneous Adipose Tissue and Risk of Proliferative Benign Breast Disease and Breast Cancer. JNCI J Natl Cancer Inst. 1993 May 19;85(10):785–93. 248. Zaridze DG, Chevchenko VE, Levtshuk AA, Lifanova YE, Maximovitch DM. Fatty acid composition of phospholipids in erythrocyte membranes and risk of breast cancer. Int J Cancer. 1990 May 15;45(5):807–10. 249. Aro A, Männistö S, Salminen I, Ovaskainen ML, Kataja V, Uusitupa M. Inverse Association Between Dietary and Serum Conjugated Linoleic Acid and Risk of Breast Cancer in Postmenopausal Women. Nutr Cancer. 2000 Nov;38(2):151–7. 250. Xu S, Chen T, Dong L, Li T, Xue H, Gao B, et al. Fatty acid synthase promotes breast cancer metastasis by mediating changes in fatty acid metabolism. Oncol Lett. 2020 Nov 11;21(1):1–1. 251. Sala A, Proschak E, Steinhilber D, Rovati GE. Two-pronged approach to anti-inflammatory therapy through the modulation of the arachidonic acid cascade. Biochem Pharmacol. 2018 Dec 1;158:161–73. 252. Hwang D, Byrne J, Scollard D, Levine E. Expression of Cyclooxygenase-1 and Cyclooxygenase-2 in Human Breast Cancer. JNCI J Natl Cancer Inst. 1998 Mar 18;90(6):455–60. 253. Takkouche B, Regueira-Mendez C, Etminan M. Breast Cancer and Use of Nonsteroidal Anti-inflammatory Drugs: A Meta-analysis. JNCI J Natl Cancer Inst. 2008 Oct 15;100(20):1439–47. 254. Liebich HM, Först C. Basic profiles of organic acids in urine. J Chromatogr. 1990 Jan 26;525(1):1–14. 255. Chen F, Elgaher W a. M, Winterhoff M, Büssow K, Waqas FH, Graner E, et al. Citraconate inhibits ACOD1 (IRG1) catalysis, reduces interferon responses and oxidative stress, and modulates inflammation and cell metabolism. Nat Metab. 2022 May;4(5):534–46. 256. Wang J, Wang W, Zhu F, Duan Q. The role of branched chain amino acids metabolic disorders in tumorigenesis and progression. Biomed Pharmacother. 2022 Sep 1;153:113390. 257. Lee PJ, Woo SJ, Yoo HM, Cho N, Kim HP. Differential Mechanism of ATP Production Occurs in Response to Succinylacetone in Colon Cancer Cells. Molecules. 2019 Oct 3;24(19):3575. 258. Bradlow HL, Telang NT, Sepkovic DW, Osborne MP. 2-hydroxyestrone: the “good” estrogen. J Endocrinol. 1996 Sep;150 Suppl:S259-265. 259. Arslan AA, Koenig KL, Lenner P, Afanasyeva Y, Shore RE, Chen Y, et al. Circulating Estrogen Metabolites and Risk of Breast Cancer in Postmenopausal Women. Cancer Epidemiol Biomarkers Prev. 2014 Jul 1;23(7):1290–7. 260. Fuhrman BJ, Schairer C, Gail MH, Boyd-Morin J, Xu X, Sue LY, et al. Estrogen Metabolism and Risk of Breast Cancer in Postmenopausal Women. JNCI J Natl Cancer Inst. 2012 Feb 22;104(4):326–39. 68 261. Dallal CM, Tice JA, Buist DSM, Bauer DC, Lacey JV Jr, Cauley JA, et al. Estrogen metabolism and breast cancer risk among postmenopausal women: a case–cohort study within B~FIT. Carcinogenesis. 2014 Feb 1;35(2):346–55. 262. Obi N, Vrieling A, Heinz J, Chang-Claude J. Estrogen metabolite ratio: Is the 2-hydroxyestrone to 16α-hydroxyestrone ratio predictive for breast cancer? Int J Womens Health. 2011 Feb 8;3:37–51. 263. Labrie F, Bélanger A, Bélanger P, Bérubé R, Martel C, Cusan L, et al. Androgen glucuronides, instead of testosterone, as the new markers of androgenic activity in women. J Steroid Biochem Mol Biol. 2006 Jun 1;99(4):182–8. 264. Hankinson SE, Eliassen AH. Circulating Sex Steroids and Breast Cancer Risk in Premenopausal Women. Horm Cancer. 2010 Feb 1;1(1):2–10. 265. Chiodo C, Morelli C, Cavaliere F, Sisci D, Lanzino M. The Other Side of the Coin: May Androgens Have a Role in Breast Cancer Risk? Int J Mol Sci. 2021 Dec 31;23(1):424. 266. Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019 Dec 21;19(1):281. 267. Vidaurre D, Bielza C, Larrañaga P. A Survey of L1 Regression. Int Stat Rev. 2013 Dec;81(3):361–87. 268. Ruiz-Perez D, Guan H, Madhivanan P, Mathee K, Narasimhan G. So you think you can PLS-DA? BMC Bioinformatics. 2020 Dec;21(S1):2. 269. Salek RM, Steinbeck C, Viant MR, Goodacre R, Dunn WB. The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience. 2013 Dec 1;2(1):2047-217X-2–13. 270. His M, Gunter MJ, Keski-Rahkonen P, Rinaldi S. Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention. J Clin Oncol. 2024 Jan 1;42(1):103–15. 271. Zhao J, Xu L, Sun J, Song M, Wang L, Yuan S, Zhu Y, Wan Z, Larsson SC, Tsilidis KK, Dunlop MG. The Global Trends in Incidence, Death, Burden and Risk Factors of Early-onset Cancer From 1990 to 2019. BMJ oncology. 2023 Sep 5;2:e000049. 272. Knox SS. From “omics” to complex disease: a systems biology approach to gene-environment interactions in cancer. Cancer Cell Int. 2010 Apr 26;10(1):11. 273. Oktay K, Santaliz-Casiano A, Patel M, Marino N, Storniolo AMV, Torun H, et al. A Computational Statistics Approach to Evaluate Blood Biomarkers for Breast Cancer Risk Stratification. Horm Cancer. 2019 Dec 19;11(1):17–33. 274. Xavier JB, Young VB, Skufca J, Ginty F, Testerman T, Pearson AT, et al. The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View. Trends Cancer. 2020 Mar;6(3):192–204. 69 Appendix Table 1. Compound annotations of significant metabolites at q<0.1 Ion Formula Compound ID Compound Name 63 CH4O4S C02704 Monomethyl sulfate 123 C3H4N2O4 C00802 Oxalureate 147 C7H8O3 C03338 2,3,5-Trihydroxytoluene C10792 Gentisyl alcohol C14110 4-Hydroxymethylcatechol C16401 2,4,6-Trihydroxytoluene C18317 2,4,5-Trihydroxytoluene C20362; Ethyl maltol HMDB0034246 Furfuryl acetate HMDB0059862 Ethyl furoate HMDB0059880 Ethyl 3-furoate HMDB0125014 4-methoxybenzene-1,3-diol HMDB0125538 3-methoxybenzene-1,2-diol HMDB0132905 5-methoxybenzene-1,3-diol HMDB0133970 2-methoxybenzene-1,3-diol HMDB0137137 4-methoxybenzene-1,2-diol HMDB0137138 2-methoxybenzene-1,4-diol 184 C9H8O2 C00423 trans-Cinnamate C02274 Dihydrocoumarin C05608 4-Hydroxycinnamyl aldehyde C07720 3-Hydroxy-1-indanone C07728 3-Isochromanone C10438 Cinnamic acid C17268 Pyruvophenone CHEBI:16151 3,4-dihydrocoumarin 70 CHEBI:28353 4-hydroxycinnamaldehyde CHEBI:35699 cis-cinnamic acid CHEBI:63552 1-phenyl-1,2-propanedione HMDB0031725 (E)-3-(2-Hydroxyphenyl)-2-propenal HMDB0032947 Di-2-furanylmethane HMDB0035243 1-Phenyl-1,2-propanedione HMDB0036626 3,4-Dihydro-2H-1-benzopyran-2-one HMDB0040986 (E)-3-(4-Hydroxyphenyl)-2-propenal HMDB0128078 3-phenylprop-2-enoic acid HMDB0134039 3-(2-hydroxyphenyl)prop-2-enal HMDB0135273 (2Z)-2-hydroxy-3-phenylprop-2-enal HMDB0135274 (2E)-3-(3-hydroxyphenyl)prop-2-enal HMDB0135275 3-phenyloxirane-2-carbaldehyde HMDB0135648 3-(4-hydroxyphenyl)prop-2-enal 200 C9H12O2 C01014 3-Isopropylcatechol CHEBI:38547 2-isopropoxyphenol HMDB0032132 2-Isopropyl-1,4-benzenediol HMDB0032599 3-(4-Hydroxyphenyl)-1-propanol HMDB0036169 3-(5-Methyl-2-furanyl)butanal HMDB0036227 2-Propenyl 2,4-hexadienoate HMDB0039801 1-(2-Furanyl)-1-pentanone HMDB0039956 4-(Ethoxymethyl)phenol HMDB0040175 4-Ethyl-2-methoxyphenol HMDB0040216 2,6,6-Trimethyl-2-cyclohexene-1,4-dione HMDB0040449 2-(Ethoxymethyl)phenol HMDB0041326 (Dimethoxymethyl)benzene HMDB0041804 2-Isopropoxyphenol HMDB0059853 3,5-Dimethoxytoluene HMDB0125537 3-ethyl-5-methoxyphenol 227 C7H10O4 C02631 2-Isopropylmaleate C04670 (1S,4S)-4-Hydroxy-3-oxocyclohexane-1-carboxylate 71 C04815 5-D-(5/6)-5-C-(Hydroxymethyl)-2,6-dihydroxycyclohex-2-en-1-one C05126 Dimethyl citraconate CHEBI:17275 2-isopropylmaleic acid CHEBI:87897 4,6-dioxoheptanoic acid HMDB0000635 Succinylacetone HMDB0036380 xi-2,3-Dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one HMDB0059815 Ethyl 5-oxotetrahydro-2-furancarboxylate 630 C15H30O3 HMDB0059866 Ethyl 3-hydroxytridecanoate HMDB0031061 10-Hydroxymyristic acid methyl ester HMDB0061657 3-hydroxypentadecanoic acid 634 C15H16O4 C09392 8-Deoxylactucin C09495 Linderane C09680 Hemigossypol C09923 Cyperine C13634 2,3-Bis(4-hydroxyphenyl)-1,2-propanediol C16812 Homobaldrinal HMDB0030110 Celerin HMDB0030578 Apigravin HMDB0030690 (E)-Suberenol HMDB0030788 Citrusal HMDB0030853 (R)-Meranzin HMDB0030854 (S)-Auraptenol HMDB0030948 Dihydrowyerone HMDB0032953 7-Methoxy-5-prenyloxycoumarin HMDB0033015 5,6-Dihydroyangonin HMDB0033016 7,8-Dihydroyangonin HMDB0034133 3-(1,1-Dimethylallyl)scopoletin HMDB0035254 7-Hydroxy-5-isopropyl-2-methoxy-3-methyl-1,4-naphthoquinone HMDB0036050 Anhydromarasmone HMDB0039142 Wyerol HMDB0039592 Pergillin 72 HMDB0041192 Kanzonol Q HMDB0129916 5-[2-(3-hydroxyphenyl)ethyl]-3-methoxybenzene-1,2-diol HMDB0129917 3-[2-(3-hydroxyphenyl)ethyl]-5-methoxybenzene-1,2-diol HMDB0129918 4-[2-(3-hydroxy-5-methoxyphenyl)ethyl]benzene-1,2-diol HMDB0129919 3-[2-(3-hydroxy-5-methoxyphenyl)ethyl]benzene-1,2-diol HMDB0129920 2-[2-(3-hydroxy-5-methoxyphenyl)ethyl]benzene-1,4-diol HMDB0129922 2-[2-(3-hydroxyphenyl)ethyl]-6-methoxybenzene-1,4-diol HMDB0131062 6-hydroxy-7-methoxy-3-(2-methylbut-3-en-2-yl)-2H-chromen-2-one HMDB0131065 8-hydroxy-7-methoxy-3-(2-methylbut-3-en-2-yl)-2H-chromen-2-one HMDB0135757 5-[2-(2-hydroxyphenyl)ethyl]-3-methoxybenzene-1,2-diol HMDB0135758 3-[2-(2-hydroxyphenyl)ethyl]-5-methoxybenzene-1,2-diol HMDB0135759 2-[2-(2-hydroxyphenyl)ethyl]-6-methoxybenzene-1,4-diol HMDB0135760 3-[1-hydroxy-2-(2-hydroxyphenyl)ethyl]-5-methoxyphenol HMDB0135761 3-[2-hydroxy-2-(2-hydroxyphenyl)ethyl]-5-methoxyphenol 689 C13H13NO6 HMDB0029234 N-[4''-hydroxy-(E)-cinnamoyl]-L-aspartic acid 724 C18H22O3 C05298 2-Hydroxyestrone C05300 16alpha-Hydroxyestrone C07621 Methallenestril C14383 16-Ketoestradiol C14496 Estrololactone C18506 Furamethrin CHEBI:1156 2-hydroxyestrone CHEBI:87628 16beta-hydroxyestrone HMDB0005895 4-Hydroxyestrone HMDB0060084 17beta-Estradiol-2,3-quinone HMDB0060085 17beta-Estradiol-3,4-quinone 729 C16H33NO3 HMDB0032358 Lauroyl diethanolamide 809 C20H32O2 C00219 Arachidonate C09057 Acutilol A C09124 2-Ketoepimanool C11897 Taxa-4(20),11(12)-dien-5alpha,13alpha-diol 73 C14493 Methandriol C14605 Metholone C14956 3alpha-Hydroxy-2alpha-methyl-5alpha-androstan-17-one C15176 17-Methyl-5alpha-androst-2-ene-1alpha,17beta-diol C15252 17beta-Hydroxy-4alpha-methyl-5alpha-androstan-3-one C15367 3beta-Methoxyandrost-5-en-16beta-ol C17920 Dihydroabietic acid C20329 Abieta-7,13-dien-18,18-diol CHEBI:71488 all-cis-8,11,14,17-icosatetraenoic acid HMDB0001043 Arachidonic acid HMDB0002177 Cis-8,11,14,17-Eicosatetraenoic acid HMDB0006036 Mesterolone HMDB0014996 Drostanolone HMDB0036702 Sideridiol HMDB0036721 ent-17-Hydroxy-16beta-kauran-19-al HMDB0036754 Yucalexin P21 HMDB0036829 Copalic acid HMDB0036830 Junicedral HMDB0037824 7,13-Eperudien-15-oic acid HMDB0039687 Oryzalexin S HMDB0039702 Oryzalexin E 1110 C19H32O8 C17452 Rehmaionoside C HMDB0031676 5a,6a-Epoxy-7E-megastigmene-3a,9e-diol 3-glucoside (Synonym: Corchoionoside A) HMDB0036318 9,13-Dihydroxy-4-megastigmen-3-one 9-glucoside HMDB0036846 Icariside B8 HMDB0038306 5a,6a-Epoxy-7E-megastigmene-3b,9e-diol 9-glucoside HMDB0040614 Dihydroroseoside 1139 C19H22N6O4 C04303 N-Benzoyl-D-arginine-4-nitroanilide 1158 C20H34O8 HMDB0034159 Acetyl tributyl citrate 1194 C21H36O8 C17864 Atractyloside G 74 1260 C26H30O6 C17446 Kurarinone HMDB0036597 1-Hydroxy-3,6,7-trimethoxy-2,8-diprenylxanthone (Synonym: Fuscaxanthone C) HMDB0040613 Kanzonol G HMDB0134749 5,7-dihydroxy-3-[3-hydroxy-4-methoxy-5-(3-methylbut-2-en-1-yl)phenyl]-8-(3-methylbut-2-en-1-yl)-3,4-dihydro-2H-1-benzopyran-4-one 1282 C28H44O4 HMDB0006227 1a,24R,25-Trihydroxyvitamin D2 HMDB0011560 MAG (0:0/24:6(6Z,9Z,12Z,15Z,18Z,21Z)/0:0) HMDB0032667 5,9-Epidioxy-3-hydroxyergost-7-en-6-one HMDB0032691 (3beta,5alpha,6alpha,7beta,14alpha,22E,24R)-5,6-Epoxyergosta-8,22-diene-3,7,14-triol HMDB0033647 5,6:8,9-Diepoxyergost-22-ene-3,7beta-diol HMDB0038977 (3beta,5alpha,6beta,9alpha,22E,24R)-5,9-Epidioxyergosta-7,22-diene-3,6-diol 1370 C25H36O9 HMDB0010338 11-Oxo-androsterone glucuronide HMDB0033654 11-Deacetylvaltrate 11-(3-hydroxy-3-methylbutanoate) HMDB0036161 4-Propanoyl-HT2 toxin HMDB0038559 8-Hexanoylneosolaniol 1377 C26H42O8 C09093 Fusicoccin H 1600 C41H78NO7P CHEBI:85020 1-[(1Z,11Z)-octadecadienyl]-2-oleoyl-sn-glycero-3-phosphoethanolamine CHEBI:90483 1-(1Z-octadecenyl)-2-linoleoyl-sn-glycero-3-phosphoethanolamine HMDB0009050 PE (18:1(11Z)/P-18:1(11Z)) HMDB0009051 PE (18:1(11Z)/P-18:1(9Z)) HMDB0009083 PE (18:1(9Z)/P-18:1(11Z)) HMDB0009084 PE (18:1(9Z)/P-18:1(9Z)) HMDB0009115 PE (18:2(9Z,12Z)/P-18:0) HMDB0009312 PE (20:2(11Z,14Z)/P-16:0) HMDB0011349 PE (P-16:0/20:2(11Z,14Z)) HMDB0011376 PE (P-18:0/18:2(9Z,12Z)) HMDB0011407 PE (P-18:1(11Z)/18:1(11Z)) HMDB0011408 PE (P-18:1(11Z)/18:1(9Z)) HMDB0011440 PE (P-18:1(9Z)/18:1(11Z)) HMDB0011441 PE (P-18:1(9Z)/18:1(9Z)) 75 1612 C43H74NO7P C00350 Phosphatidylethanolamine CHEBI:90479 1-(1Z-hexadecenyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphoethanolamine HMDB0005780 PE (O-16:1(1Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) HMDB0009479 PE (20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(11Z)) HMDB0009480 PE (20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(9Z)) HMDB0009708 PE (22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-16:0) HMDB0011420 PE (P-18:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z)) HMDB0011453 PE (P-18:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z)) 1624 C45H76NO7P CHEBI:90487 1-(1Z,9Z-octadecadienyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphoethanolamine HMDB0009710 PE (22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-18:1(11Z)) HMDB0009711 PE (22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-18:1(9Z)) HMDB0011427 PE (P-18:1(11Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) HMDB0011460 PE (P-18:1(9Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 1625 C45H78NO7P CHEBI:78263 1-(1Z-octadecenyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphoethanolamine zwitterion HMDB0009644 PE (22:5(4Z,7Z,10Z,13Z,16Z)/P-18:1(11Z)) HMDB0009645 PE (22:5(4Z,7Z,10Z,13Z,16Z)/P-18:1(9Z)) HMDB0009677 PE (22:5(7Z,10Z,13Z,16Z,19Z)/P-18:1(11Z)) HMDB0009678 PE (22:5(7Z,10Z,13Z,16Z,19Z)/P-18:1(9Z)) HMDB0009709 PE (22:6(4Z,7Z,10Z,13Z,16Z,19Z)/P-18:0) HMDB0011394 PE (P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) HMDB0011425 PE (P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)) HMDB0011426 PE (P-18:1(11Z)/22:5(7Z,10Z,13Z,16Z,19Z)) HMDB0011458 PE (P-18:1(9Z)/22:5(4Z,7Z,10Z,13Z,16Z)) HMDB0011459 PE (P-18:1(9Z)/22:5(7Z,10Z,13Z,16Z,19Z)) 76 Figure 1. Percentage changes in metabolite coefficient estimates when adjusting for matching factors and risk factors 77 Figure 2. Performance of postmenopausal breast cancer prediction 78 Figure 3. Performance of invasive ductal carcinoma prediction 79 Figure 4. Performance of ER+/PR+ breast cancer prediction
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bhatti, Parveen
The serum metabolome and breast cancer risk
Text