Body mass index and breast cancer survival: a Mendelian randomization analysis

Guo, Qi; Burgess, Stephen; Turman, Constance; Bolla, Manjeet K.; Wang, Qin; Lush, Michael; Abraham, Jean; Aittomäki, Kristiina; Andrulis, Irene L.; Apicella, Carmel; Arndt, Volker; Barrdahl, Myrto; Benı́tez, Javier; Berg, Christine D.; Blomqvist, Carl; Bojesen, Stig E.; Bonanni, Bernardo; Brand, Judith S.; Brenner, Hermann; Broeks, Annegien; Burwinkel, Barbara; Caldas, Carlos; Campa, Daniele; Canzian, Federico; Chang‐Claude, Jenny; Chanock, Stephen J.; Chin, Suet-Feung; Couch, Fergus J.; Cox, Angela; Cross, Simon S.; Cybulski, Cezary; Czene, Kamila; Darabi, Hatef; Devilee, Peter; Diver, W. Ryan; Dunning, Alison M.; Earl, Helena; Eccles, Diana; Ekici, Arif B.; Eriksson, Mikael; Evans, D. Gareth; Fasching, Peter A.; Figueroa, Jonine D.; Flesch-Janys, Dieter; Flyger, Henrik; Gapstur, Susan M.; Gaudet, Mia M.; Giles, Graham G.; Glendon, Gord; Grip, Mervi; Gronwald, Jacek; Haeberle, Lothar; Haiman, Christopher A.; Hall, Per; Hamann, Ute; Hankinson, Susan E.; Hartikainen, Jaana M.; Hein, Alexander; Hiller, Louise; Hogervorst, Frans B.L.; Holleczek, Bernd; Hooning, Maartje J.; Hoover, Robert N.; Humphreys, Keith; Hunter, David J.; Hüsing, Anika; Jakubowska, Anna; Jukkola-Vuorinen, Arja; Kaaks, Rudolf; Kabisch, Maria; Kataja, Vesa; Knight, Julia A.; Koppert, Linetta B.; Kosma, Veli‐Matti; Kristensen, Vessela N.; Lambrechts, Diether; Marchand, Loı̈c Le; Li, Jingmei; Lindblom, Annika; Lindström, Sara; Lissowska, Jolanta; Lubiński, Jan; Machiela, Mitchell J.; Mannermaa, Arto; Manoukian, Siranoush; Margolin, Sara; Marmé, Federik; Martens, John W.M.; McLean, Catriona; Menéndez, Primitiva; Milne, Roger L.; Mulligan, Anna Marie; Muranen, Taru; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F.; Nordestgaard, Børge G.; Olson, Janet E.; Peŕez, José Ignacio Arias; Peterlongo, Paolo; Phillips, Kelly‐Anne; Poole, Christopher; Pylkäs, Katri; Radice, Paolo; Rahman, Nazneen; Rüdiger, Thomas; Rudolph, Anja; Sawyer, Elinor J.; Schumacher, Fredrick; Seibold, Petra; Seynaeve, Caroline; Shah, Mitul; Smeets, Ann; Southey, Melissa C.; Tollenaar, Rob A.E.M.; Tomlinson, Ian; Tsimiklis, Helen; Ulmer, Hans-Ulrich; Vachon, Celine M.; Ouweland, Ans M.W. van den; Veer, Laura J. van’t; Wildiers, Hans; Willett, Walter C.; Winqvist, Robert; Zamora, M. Pilar; Chenevix-Trench, Georgia; Dörk, Thilo; Easton, Douglas F.; García‐Closas, Montserrat; Kraft, Peter; Hopper, John L.; Wang, Zheng; Schmidt, Marjanka K.; Pharoah, Paul D.P.

doi:10.1093/ije/dyx131

Cited by 54 publications

(41 citation statements)

References 23 publications

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“…Since the CBCS analysis was a case-only study, we were wary of potential collider bias by unmeasured confounders associated with both breast cancer risk and progression [36,[55][56][57], which may affect the effect sizes of association between survival and GReX of genes. None of the GReX of these four genes showed significant transcriptome-wide associations with breast cancer risk in iCOGs data [37][38][39], suggesting that our estimates of association may be free of the collider bias.…”

Section: Discussionmentioning

confidence: 99%

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

et al. 2020

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Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry-or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

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Section: Discussionmentioning

confidence: 99%

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

et al. 2020

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“…Since the CBCS analysis was a case-only study, we were wary of potential collider bias by unmeasured confounders associated with both breast cancer risk and progression [34,48–50]. These colliders may affect the magnitude and direction of effect sizes on association between survival and GReX of genes (Supplemental Figure 14).…”

Section: Discussionmentioning

confidence: 99%

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Bhattacharya

García‐Closas

Olshan

et al. 2019

Preprint

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1 Background: The relationship between germline genetic variation and breast cancer 2 survival is largely unknown, especially in understudied minority populations who often 3 have poorer survival. Genome-wide association studies (GWAS) have interrogated 4 breast cancer survival but often are underpowered due to subtype heterogeneity and 5 many clinical covariates and detect loci in non-coding regions that are difficult to interpret. 6 Transcriptome-wide association studies (TWAS) show increased power in detecting 7 functionally-relevant loci by leveraging expression quantitative trait loci (eQTLs) from 8 external reference panels in relevant tissues. However, ancestry-or race-specific 9 reference panels may be needed to draw correct inference in ancestrally-diverse cohorts. 10 Such panels for breast cancer are lacking. 12Results: We provide a framework for TWAS for breast cancer in diverse populations, 13 using data from the Carolina Breast Cancer Study (CBCS), a North Carolina population-14 based cohort that oversampled black women. We perform eQTL analysis for 406 breast 15 cancer-related genes to train race-stratified predictive models of tumor expression from 16 germline genotypes. Using these models, we impute expression in independent data from 17 CBCS and TCGA, accounting for sampling variability in assessing performance. These 18 models are not applicable across race, and their predictive performance varies across 19 tumor subtype. Within CBCS (ܰ = 3,828), at a false discovery-adjusted significance of 20 0.10 and stratifying for race, we identify associations in black women near AURKA, 21 CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. 22Conclusions: We show that carefully implemented and thoroughly validated TWAS is an 24 efficient approach for understanding the genetics underpinning breast cancer outcomes 25 in diverse populations. 26 27 Keywords: transcriptome-wide analysis (TWAS); breast cancer; expression quantitative 28 trait loci (eQTL); survival; polygenic traits 29 4 Background 30 Breast cancer remains the most common cancer among women in the world [1]. Breast 31 cancer tends to be more aggressive in young women and African American women, 32 though underlying germline determinants of poor outcomes are not well-studied. Cohorts 33 that represent understudied minority populations, like the Carolina Breast Cancer Study 34 (CBCS), have identified differences in healthcare access, socioeconomics, and 35 environmental exposures associated with disparities in outcome [2-4], but more targeted 36 genomic studies are necessary to interrogate these disparities from a biologic and genetic 37 perspective. 38 39 Few genome-wide association studies (GWAS) have studied the relationship between 40 germline variation and survival outcomes in breast cancer, with most focusing instead on 41 genetic predictors of risk [5,6]. Recently, GWAS have shown evidence of association 42 between candidate common germline variants and breast cancer survival, but these 43 studies are often underpow...

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“…The data for the association between BMI and survival are fairly consistent for HR‐positive breast cancer, with less consistent results reported for HR‐negative tumors . A recent study using genetic variants associated with BMI as a proxy for body fatness supported a possible causal association between increased BMI and reduced breast cancer survival in women with estrogen receptor–positive cancers, but not in those with estrogen receptor–negative cancers . Postdiagnosis weight gain predicted worse survival in most but not all studies.…”

Section: Global Burden Of Cancers Attributable To Excess Body Weightmentioning

confidence: 93%

“…165 A recent study using genetic variants associated with BMI as a proxy for body fatness supported a possible causal association between increased BMI and reduced breast cancer survival in women with estrogen receptor-positive cancers, but not in those with estrogen receptor-negative cancers. 166 Postdiagnosis weight gain predicted worse survival in most [167][168][169] but not all 170 studies. A recent study using clinically acquired computed tomography scans from patients with stage II or III breast cancer demonstrated that those who had sarcopenia and high total adipose tissue had the highest mortality and that BMI alone did not optimally identify patients who were at higher risk of death because of variation in body composition.…”

Section: Excess Body Weight and Cancer Survivalmentioning

confidence: 98%

Global patterns in excess body weight and the associated cancer burden

Sung¹,

Siegel

Torre

et al. 2018

CA A Cancer J Clinicians

405

292

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The prevalence of excess body weight and the associated cancer burden have been rising over the past several decades globally. Between 1975 and 2016, the prevalence of excess body weight in adults—defined as a body mass index (BMI) ≥ 25 kg/m2—increased from nearly 21% in men and 24% in women to approximately 40% in both sexes. Notably, the prevalence of obesity (BMI ≥ 30 kg/m2) quadrupled in men, from 3% to 12%, and more than doubled in women, from 7% to 16%. This change, combined with population growth, resulted in a more than 6‐fold increase in the number of obese adults, from 100 to 671 million. The largest absolute increase in obesity occurred among men and boys in high‐income Western countries and among women and girls in Central Asia, the Middle East, and North Africa. The simultaneous rise in excess body weight in almost all countries is thought to be driven largely by changes in the global food system, which promotes energy‐dense, nutrient‐poor foods, alongside reduced opportunities for physical activity. In 2012, excess body weight accounted for approximately 3.9% of all cancers (544,300 cases) with proportion varying from less than 1% in low‐income countries to 7% or 8% in some high‐income Western countries and in Middle Eastern and Northern African countries. The attributable burden by sex was higher for women (368,500 cases) than for men (175,800 cases). Given the pandemic proportion of excess body weight in high‐income countries and the increasing prevalence in low‐ and middle‐income countries, the global cancer burden attributable to this condition is likely to increase in the future. There is emerging consensus on opportunities for obesity control through the multisectoral coordinated implementation of core policy actions to promote an environment conducive to a healthy diet and active living. The rapid increase in both the prevalence of excess body weight and the associated cancer burden highlights the need for a rejuvenated focus on identifying, implementing, and evaluating interventions to prevent and control excess body weight.

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Body mass index and breast cancer survival: a Mendelian randomization analysis

Cited by 54 publications

References 23 publications

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Global patterns in excess body weight and the associated cancer burden

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