Using Metabolomics to Explore the Role of Postmenopausal Adiposity in Breast Cancer Risk

JNCI: Journal of the National Cancer Institute, Jun 2018

Lasky-Su, Jessica A, Zeleznik, Oana A, Eliassen, A Heather

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Using Metabolomics to Explore the Role of Postmenopausal Adiposity in Breast Cancer Risk

JNCI J Natl Cancer Inst ( Using Metabolomics to Explore the Role of Postmenopausal Adiposity in Breast Cancer Risk Jessica A. Lasky-Su 0 1 Oana A. Zeleznik 0 1 A. Heather Eliassen heather.eliassen@channing 0 1 0 The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions , please 1 Affiliations of authors: Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School , Boston, MA (JALS, OAZ , AHE); Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, AHE , USA - Excess adiposity after menopause is a well-established, and potentially modifiable, breast cancer risk factor ( 1 ). Breast cancer has a clear hormonal origin, and adiposity is likely to influence breast cancer risk through estrogens given that aromatization of androgens in adipose tissue is the primary estrogen source after menopause. In fact, overweight and obese postmenopausal women have 50% higher circulating estrogen levels compared with lean women ( 2 ). Underpinning the prominent hormonal hypothesis, excess adiposity is more strongly associated with estrogen receptor–positive (ERþ) breast cancers ( 1 ). In addition to estrogen ( 3 ), other mechanisms that may contribute to the body mass index (BMI)–breast cancer association include inflammation and insulin resistance. Perturbations in metabolic systems are hallmarks of obesity, and a logical place to investigate additional mechanisms. The study of metabolomics, relatively recently applied in epidemiologic settings, offers great potential to explore differences in metabolomic profiles and subsequent breast cancer risk, such as the Moore et al. report in this issue of the Journal ( 4 ). However, to date, the only other study to prospectively assess the association between small molecule metabolites and breast cancer risk is from the same group of investigators, who reported several diet-related metabolites associated with risk ( 5 ). In this issue of the Journal, Moore et al. ( 4 ) report on the correlation between BMI and circulating metabolites, and then on the association between BMI-related metabolites and breast cancer risk. The nested case–control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) included 621 invasive breast cancer case subjects and 621 matched controls. A total of 617 known metabolites, measured in archived serum samples, were included in the analysis. Correlation analyses resulted in 67 metabolites that were modestly associated with BMI (partial Pearson correlation range ¼ –0.26 to 0.35). Of these, seven metabolites were statistically significantly associated with overall breast cancer risk after correcting for multiple comparisons (false discovery rate < 0.2), including two androgen-related metabolites and three branchedchain amino acid (BCAA)–related metabolites. Twenty-three metabolites were statistically significantly associated with ERþ breast cancer (n ¼ 418 case subjects), including the two androgen-related metabolites and a total of six BCAA-related metabolites. No metabolites were statistically significantly associated with the smaller group of 144 ER- breast cancers. With forward stepwise selection, one androgen-related metabolite (16alpha-hydroxy-DHEA-3-sulfate) was selected for overall and ERþ breast cancer risk, and one (overall) or three (ERþ) BCAArelated metabolites were selected. The methodological approach of Moore et al. ( 4 ), focusing on the subset of metabolites correlated with BMI, has an implicit advantage in its reductionist nature. While this approach is effective for identifying metabolites specific to the BMI–breast cancer relationship, there are consequences of this strategy that deserve consideration. From the outset, such an approach leaves the lingering question of which remaining metabolites from the full set of 617 may be important in breast cancer etiology. Within a reductionist approach, the choices of analytic techniques also may result in missed metabolites. For instance, a Bonferroni approach in the screening step does not account for the high correlations among the metabolites and is likely overly conservative. A less conservative approach that accounts for correlations among metabolites (eg, “number of effective tests” [6]) would decrease the possibility of missing metabolites in the initial BMI screening stage. Second, the agnostic datadriven approach of Pearson correlations does not accommodate metabolites or pathways previously reported to be associated with BMI. For example, while all three BCAAs (leucine, isoleucine, and valine) have been associated with BMI in prior publications ( 7–12 ), Moore at al. identified only valine as associated with BMI. Although a systematic comparison with published E D I T O R I A L BMI-associated metabolites would enhance this approach, we recognize that the task of comparing metabolomics results across studies is daunting given the lack of consistent reporting across studies. Without standard identifiers, such as those from the human metabolome ( 13 ), reported along with the metabolite names, it is unclear if there is overlap between metabolites with different names. Complicating this issue is the rapidly growing nature of the field of metabolomics, where many platforms identify metabolites that are not yet catalogued in the Human Metabolome Database. The final step in the analysis by Moore et al. ( 4 ) was to assess the change in the BMI–breast cancer association when accounting for BMI-related metabolites associated with breast cancer. In the overall analysis, the association was attenuated with the addition of the two selected metabolites. The authors estimate that 57.6% of the BMI–breast cancer association is due to the indirect effect through these metabolites. While impressive, it seems likely that this is an overestimate given the wellestablished associations between BMI and estradiol ( 2 ), and estradiol and breast cancer risk ( 14 ). Indeed, in a collaborative analysis of eight prospective studies ( 3 ), adjusting for circulating estradiol resulted in even more attenuation of the BMI–breast cancer association than reported by Moore et al., although they reported no correlation between the selected metabolite 16alpha-hydroxy-DHEA-3-sulfate and estradiol. Although the BMI– breast cancer association is stronger among ERþ tumors ( 1 ), this was not the case in the analysis by Moore et al. This complicates the mediation interpretation when metabolites are included in a model of BMI and ERþ breast cancer risk. Moore et al. ( 4 ) make important strides through their exploration of the BMI–metabolomics–breast cancer association. Further steps could include examination of the metabolic pathways where the selected metabolites operate, to strengthen the understanding of the underlying biology. Several analytic approaches could be utilized for this. One approach would evaluate the association between all metabolites in the metabolic pathway and breast cancer risk with a more liberal statistical significance threshold given the correlation among metabolites within a pathway. A more sophisticated approach would assess the pathway metabolites simultaneously through multivariate techniques, such as principal components analysis, partial least squares discriminant analysis, network approaches (eg, weighted gene co-expression network analysis), or metabolite set enrichment analysis. Evaluation of metabolic pathways may yield important additional information on the functional nature of the relationship between the selected metabolite and breast cancer. The two potential mechanisms identified, steroid hormone and BCAA, are of particular interest in breast cancer etiology. While the steroid hormone mechanism has been investigated in detail, the hypothesis that BCAA metabolism may contribute to breast carcinogenesis is intriguing. BCAA levels are predictors of insulin resistance and type 2 diabetes risk, independent of BMI ( 15 ), and have been associated with pancreatic cancer ( 16 ). The reported association with breast cancer warrants additional investigation and replication in future studies. In conclusion, Moore et al. provide an exciting look into the possible metabolic underpinnings of breast cancer etiology related to adiposity, and we anticipate that future investigations of the role of metabolomics in breast cancer will work to confirm these findings and also expand to a broader snapshot of the metabolome to better understand the biologic networks of importance. Notes Dr. Lasky-Su is a consultant to Metabolon. Drs. Zeleznik and Eliassen have no disclosures. 1. Lauby-Secretan B , Scoccianti C , Loomis D , et al. Body fatness and cancerviewpoint of the IARC Working Group . N Engl J Med . 2016 ; 375 ( 8 ): 794 - 798 . 2. Endogenous Hormones Breast Cancer Collaborative Group . Circulating sex hormones and breast cancer risk factors in postmenopausal women: Reanalysis of 13 studies . Br J Cancer . 2011 ; 105 ( 5 ): 709 - 722 . 3. Key TJ , Appleby PN , Reeves GK , et al. Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women . J Natl Cancer Inst . 2003 ; 95 ( 16 ): 1218 - 1226 . 4. Moore SC , Playdon MC , Sampson JN , et al. A metabolomics analysis of body mass index and postmenopausal breast cancer risk . J Natl Cancer Inst . 2018 ; 110 ( 6 ): 588 - 597 . 5. Playdon MC , Ziegler RG , Sampson JN , et al. Nutritional metabolomics and breast cancer risk in a prospective study . Am J Clin Nutr . 2017 ; 106 ( 2 ): 637 - 649 . 6. Gao X , Starmer J , Martin ER . A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms . Genet Epidemiol . 2008 ; 32 ( 4 ): 361 - 369 . 7. Moore SC , Matthews CE , Sampson JN , et al. Human metabolic correlates of body mass index . Metabolomics . 2014 ; 10 ( 2 ): 259 - 269 . 8. Newgard CB , An J , Bain JR , et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance . Cell Metab . 2009 ; 9 ( 4 ): 311 - 326 . 9. Ho JE , Larson MG , Ghorbani A , et al. Metabolomic profiles of body mass index in the Framingham Heart Study reveal distinct cardiometabolic phenotypes . PLoS One . 2016 ; 11 ( 2 ): e0148361 . 10. Wurtz P , Wang Q , Kangas AJ , et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change . PLoS Med . 2014 ; 11 ( 12 ): e1001765 . 11. Park S , Sadanala KC , Kim EK . A metabolomic approach to understanding the metabolic link between obesity and diabetes . Mol Cells . 2015 ; 38 ( 7 ): 587 - 596 . 12. Shin AC , Fasshauer M , Filatova N , et al. Brain insulin lowers circulating BCAA levels by inducing hepatic BCAA catabolism . Cell Metab . 2014 ; 20 ( 5 ): 898 - 909 . 13. Wishart DS , Feunang YD , Marcu A , et al. HMDB 4 . 0: The human metabolome database for 2018 [Epub ahead of print November 11, 2017 ]. Nucleic Acids Res . 2017 . doi: 10 .1093/nar/gkx1089. 14. Key T , Appleby P , Barnes I , Reeves G , Endogenous H , Breast Cancer Collaborative Group. Endogenous sex hormones and breast cancer in postmenopausal women: Reanalysis of nine prospective studies . J Natl Cancer Inst . 2002 ; 94 ( 8 ): 606 - 616 . 15. Wang TJ , Larson MG , Vasan RS , et al. Metabolite profiles and the risk of developing diabetes . Nat Med . 2011 ; 17 ( 4 ): 448 - 453 . 16. Mayers JR , Wu C , Clish CB , et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development . Nat Med . 2014 ; 20 ( 10 ): 1193 - 1198 .

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Lasky-Su, Jessica A, Zeleznik, Oana A, Eliassen, A Heather. Using Metabolomics to Explore the Role of Postmenopausal Adiposity in Breast Cancer Risk, JNCI: Journal of the National Cancer Institute, 2018, 547-548, DOI: 10.1093/jnci/djx283