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3. Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes

Author

Ping, Jie, Jia, Guochong, Cai, Qiuyin, Guo, Xingyi, Tao, Ran, Ambrosone, Christine, Huo, Dezheng, Ambs, Stefan, Barnard, Mollie E., Chen, Yu, Garcia-Closas, Montserrat, Gu, Jian, Hu, Jennifer J., John, Esther M., Li, Christopher I., Nathanson, Katherine, Nemesure, Barbara, Olopade, Olufunmilayo I., Pal, Tuya, Press, Michael F., Sanderson, Maureen, Sandler, Dale P., Yoshimatsu, Toshio, Adejumo, Prisca O., Ahearn, Thomas, Brewster, Abenaa M., Hennis, Anselm J. M., Makumbi, Timothy, Ndom, Paul, O’Brien, Katie M., Olshan, Andrew F., Oluwasanu, Mojisola M., Reid, Sonya, Yao, Song, Butler, Ebonee N., Huang, Maosheng, Ntekim, Atara, Li, Bingshan, Troester, Melissa A., Palmer, Julie R., Haiman, Christopher A., Long, Jirong, and Zheng, Wei

Published
2024
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5. Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction

Author

Jia, Guochong, Ping, Jie, Guo, Xingyi, Yang, Yaohua, Tao, Ran, Li, Bingshan, Ambs, Stefan, Barnard, Mollie E., Chen, Yu, Garcia-Closas, Montserrat, Gu, Jian, Hu, Jennifer J., Huo, Dezheng, John, Esther M., Li, Christopher I., Li, James L., Nathanson, Katherine L., Nemesure, Barbara, Olopade, Olufunmilayo I., Pal, Tuya, Press, Michael F., Sanderson, Maureen, Sandler, Dale P., Shu, Xiao-Ou, Troester, Melissa A., Yao, Song, Adejumo, Prisca O., Ahearn, Thomas, Brewster, Abenaa M., Hennis, Anselm J. M., Makumbi, Timothy, Ndom, Paul, O’Brien, Katie M., Olshan, Andrew F., Oluwasanu, Mojisola M., Reid, Sonya, Butler, Ebonee N., Huang, Maosheng, Ntekim, Atara, Qian, Huijun, Zhang, Haoyu, Ambrosone, Christine B., Cai, Qiuyin, Long, Jirong, Palmer, Julie R., Haiman, Christopher A., and Zheng, Wei

Published
2024
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8. Gene Expression Profiling Identifies Two Chordoma Subtypes Associated with Distinct Molecular Mechanisms and Clinical Outcomes.

Author

Bai, Jiwei, Shi, Jianxin, Zhang, Yazhuo, Li, Chuzhong, Xiong, Yujia, Koka, Hela, Wang, Difei, Zhang, Tongwu, Song, Lei, Luo, Wen, Zhu, Bin, Hicks, Belynda, Hutchinson, Amy, Kirk, Erin, Troester, Melissa, Li, Mingxuan, Shen, Yutao, Ma, Tianshun, Wang, Junmei, Liu, Xing, Wang, Shuai, Gui, Songbai, McMaster, Mary, Chanock, Stephen, Parry, Dilys, Goldstein, Allen, and Yang, Xiaohong

Subjects
Humans, Chordoma, Hedgehog Proteins, Biomarkers, Tumor, Gene Expression Profiling, Skull Base Neoplasms
Abstract

PURPOSE: Chordoma is a rare bone tumor with a high recurrence rate and limited treatment options. The aim of this study was to identify molecular subtypes of chordoma that may improve clinical management. EXPERIMENTAL DESIGN: We conducted RNA sequencing in 48 tumors from patients with Chinese skull-base chordoma and identified two major molecular subtypes. We then replicated the classification using a NanoString panel in 48 patients with chordoma from North America. RESULTS: Tumors in one subtype were more likely to have somatic mutations and reduced expression in chromatin remodeling genes, such as PBRM1 and SETD2, whereas the other subtype was characterized by the upregulation of genes in epithelial-mesenchymal transition and Sonic Hedgehog pathways. IHC staining of top differentially expressed genes between the two subtypes in 312 patients with Chinese chordoma with long-term follow-up data showed that the expression of some markers such as PTCH1 was significantly associated with survival outcomes. CONCLUSIONS: Our findings may improve the understanding of subtype-specific tumorigenesis of chordoma and inform clinical prognostication and targeted options.

Published
2023

9. A complex systems model of breast cancer etiology: The Paradigm II Model

Author

Hiatt, Robert A, Worden, Lee, Rehkopf, David, Engmann, Natalie, Troester, Melissa, Witte, John S, Balke, Kaya, Jackson, Christian, Barlow, Janice, Fenton, Suzanne E, Gehlert, Sarah, Hammond, Ross A, Kaplan, George, Kornak, John, Nishioka, Krisida, McKone, Thomas, Smith, Martyn T, Trasande, Leonardo, and Porco, Travis C

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Health Sciences, Public Health, Oncology and Carcinogenesis, Cancer, Women's Health, Behavioral and Social Science, Breast Cancer, Prevention, 2.2 Factors relating to the physical environment, Good Health and Well Being, Female, Humans, Breast Neoplasms, Nutrition Surveys, Risk Factors, Alcohol Drinking, Incidence, General Science & Technology
Abstract

BackgroundComplex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers.Methods and findingsWe developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures.ConclusionsThe Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.

Published
2023

10. Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies

Author

Jung, Audrey Y, Ahearn, Thomas U, Behrens, Sabine, Middha, Pooja, Bolla, Manjeet K, Wang, Qin, Arndt, Volker, Aronson, Kristan J, Augustinsson, Annelie, Freeman, Laura E Beane, Becher, Heiko, Brenner, Hermann, Canzian, Federico, Carey, Lisa A, Consortium, CTS, Czene, Kamila, Eliassen, A Heather, Eriksson, Mikael, Evans, D Gareth, Figueroa, Jonine D, Fritschi, Lin, Gabrielson, Marike, Giles, Graham G, Guénel, Pascal, Hadjisavvas, Andreas, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hoppe, Reiner, Hopper, John L, Howell, Anthony, Hunter, David J, Hüsing, Anika, Kaaks, Rudolf, Kosma, Veli-Matti, Koutros, Stella, Kraft, Peter, Lacey, James V, Le Marchand, Loic, Lissowska, Jolanta, Loizidou, Maria A, Mannermaa, Arto, Maurer, Tabea, Murphy, Rachel A, Olshan, Andrew F, Olsson, Håkan, Patel, Alpa V, Perou, Charles M, Rennert, Gad, Shibli, Rana, Shu, Xiao-Ou, Southey, Melissa C, Stone, Jennifer, Tamimi, Rulla M, Teras, Lauren R, Troester, Melissa A, Truong, Thérèse, Vachon, Celine M, Wang, Sophia S, Wolk, Alicja, Wu, Anna H, Yang, Xiaohong R, Zheng, Wei, Dunning, Alison M, Pharoah, Paul DP, Easton, Douglas F, Milne, Roger L, Chatterjee, Nilanjan, Schmidt, Marjanka K, García-Closas, Montserrat, and Chang-Claude, Jenny

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Breast Cancer, Aging, Reproductive health and childbirth, Female, Humans, Breast Neoplasms, Receptor, ErbB-2, Receptors, Progesterone, Receptors, Estrogen, Triple Negative Breast Neoplasms, Case-Control Studies, Risk Factors, Biomarkers, Tumor, CTS Consortium, Receptor, erbB-2, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundReproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear.MethodsAnalyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided.ResultsCompared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like.ConclusionsThis large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.

Published
2022

14. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Author

Middha, Pooja, Wang, Xiaoliang, Behrens, Sabine, Bolla, Manjeet K., Wang, Qin, Dennis, Joe, Michailidou, Kyriaki, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J., Auer, Paul L., Augustinsson, Annelie, Baert, Thaïs, Freeman, Laura E. Beane, Becher, Heiko, Beckmann, Matthias W., Benitez, Javier, Bojesen, Stig E., Brauch, Hiltrud, Brenner, Hermann, Brooks-Wilson, Angela, Campa, Daniele, Canzian, Federico, Carracedo, Angel, Castelao, Jose E., Chanock, Stephen J., Chenevix-Trench, Georgia, Cordina-Duverger, Emilie, Couch, Fergus J., Cox, Angela, Cross, Simon S., Czene, Kamila, Dossus, Laure, Dugué, Pierre-Antoine, Eliassen, A. Heather, Eriksson, Mikael, Evans, D. Gareth, Fasching, Peter A., Figueroa, Jonine D., Fletcher, Olivia, Flyger, Henrik, Gabrielson, Marike, Gago-Dominguez, Manuela, Giles, Graham G., González-Neira, Anna, Grassmann, Felix, Grundy, Anne, Guénel, Pascal, Haiman, Christopher A., Håkansson, Niclas, Hall, Per, Hamann, Ute, Hankinson, Susan E., Harkness, Elaine F., Holleczek, Bernd, Hoppe, Reiner, Hopper, John L., Houlston, Richard S., Howell, Anthony, Hunter, David J., Ingvar, Christian, Isaksson, Karolin, Jernström, Helena, John, Esther M., Jones, Michael E., Kaaks, Rudolf, Keeman, Renske, Kitahara, Cari M., Ko, Yon-Dschun, Koutros, Stella, Kurian, Allison W., Lacey, James V., Lambrechts, Diether, Larson, Nicole L., Larsson, Susanna, Le Marchand, Loic, Lejbkowicz, Flavio, Li, Shuai, Linet, Martha, Lissowska, Jolanta, Martinez, Maria Elena, Maurer, Tabea, Mulligan, Anna Marie, Mulot, Claire, Murphy, Rachel A., Newman, William G., Nielsen, Sune F., Nordestgaard, Børge G., Norman, Aaron, O’Brien, Katie M., Olson, Janet E., Patel, Alpa V., Prentice, Ross, Rees-Punia, Erika, Rennert, Gad, Rhenius, Valerie, Ruddy, Kathryn J., Sandler, Dale P., Scott, Christopher G., Shah, Mitul, Shu, Xiao-Ou, Smeets, Ann, Southey, Melissa C., Stone, Jennifer, Tamimi, Rulla M., Taylor, Jack A., Teras, Lauren R., Tomczyk, Katarzyna, Troester, Melissa A., Truong, Thérèse, Vachon, Celine M., Wang, Sophia S., Weinberg, Clarice R., Wildiers, Hans, Willett, Walter, Winham, Stacey J., Wolk, Alicja, Yang, Xiaohong R., Zamora, M. Pilar, Zheng, Wei, Ziogas, Argyrios, Dunning, Alison M., Pharoah, Paul D. P., García-Closas, Montserrat, Schmidt, Marjanka K., Kraft, Peter, Milne, Roger L., Lindström, Sara, Easton, Douglas F., and Chang-Claude, Jenny

Published
2023
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16. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Author

Mueller, Stefanie H., Lai, Alvina G., Valkovskaya, Maria, Michailidou, Kyriaki, Bolla, Manjeet K., Wang, Qin, Dennis, Joe, Lush, Michael, Abu-Ful, Zomoruda, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antonenkova, Natalia N., Arndt, Volker, Aronson, Kristan J., Augustinsson, Annelie, Baert, Thais, Freeman, Laura E. Beane, Beckmann, Matthias W., Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Blomqvist, Carl, Bogdanova, Natalia V., Bojesen, Stig E., Bonanni, Bernardo, Brenner, Hermann, Brucker, Sara Y., Buys, Saundra S., Castelao, Jose E., Chan, Tsun L., Chang-Claude, Jenny, Chanock, Stephen J., Choi, Ji-Yeob, Chung, Wendy K., Colonna, Sarah V., Cornelissen, Sten, Couch, Fergus J., Czene, Kamila, Daly, Mary B., Devilee, Peter, Dörk, Thilo, Dossus, Laure, Dwek, Miriam, Eccles, Diana M., Ekici, Arif B., Eliassen, A. Heather, Engel, Christoph, Evans, D. Gareth, Fasching, Peter A., Fletcher, Olivia, Flyger, Henrik, Gago-Dominguez, Manuela, Gao, Yu-Tang, García-Closas, Montserrat, García-Sáenz, José A., Genkinger, Jeanine, Gentry-Maharaj, Aleksandra, Grassmann, Felix, Guénel, Pascal, Gündert, Melanie, Haeberle, Lothar, Hahnen, Eric, Haiman, Christopher A., Håkansson, Niclas, Hall, Per, Harkness, Elaine F., Harrington, Patricia A., Hartikainen, Jaana M., Hartman, Mikael, Hein, Alexander, Ho, Weang-Kee, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Houlston, Richard S., Howell, Anthony, Hunter, David J., Huo, Dezheng, Ito, Hidemi, Iwasaki, Motoki, Jakubowska, Anna, Janni, Wolfgang, John, Esther M., Jones, Michael E., Jung, Audrey, Kaaks, Rudolf, Kang, Daehee, Khusnutdinova, Elza K., Kim, Sung-Won, Kitahara, Cari M., Koutros, Stella, Kraft, Peter, Kristensen, Vessela N., Kubelka-Sabit, Katerina, Kurian, Allison W., Kwong, Ava, Lacey, James V., Lambrechts, Diether, Le Marchand, Loic, Li, Jingmei, Linet, Martha, Lo, Wing-Yee, Long, Jirong, Lophatananon, Artitaya, Mannermaa, Arto, Manoochehri, Mehdi, Margolin, Sara, Matsuo, Keitaro, Mavroudis, Dimitrios, Menon, Usha, Muir, Kenneth, Murphy, Rachel A., Nevanlinna, Heli, Newman, William G., Niederacher, Dieter, O’Brien, Katie M., Obi, Nadia, Offit, Kenneth, Olopade, Olufunmilayo I., Olshan, Andrew F., Olsson, Håkan, Park, Sue K., Patel, Alpa V., Patel, Achal, Perou, Charles M., Peto, Julian, Pharoah, Paul D. P., Plaseska-Karanfilska, Dijana, Presneau, Nadege, Rack, Brigitte, Radice, Paolo, Ramachandran, Dhanya, Rashid, Muhammad U., Rennert, Gad, Romero, Atocha, Ruddy, Kathryn J., Ruebner, Matthias, Saloustros, Emmanouil, Sandler, Dale P., Sawyer, Elinor J., Schmidt, Marjanka K., Schmutzler, Rita K., Schneider, Michael O., Scott, Christopher, Shah, Mitul, Sharma, Priyanka, Shen, Chen-Yang, Shu, Xiao-Ou, Simard, Jacques, Surowy, Harald, Tamimi, Rulla M., Tapper, William J., Taylor, Jack A., Teo, Soo Hwang, Teras, Lauren R., Toland, Amanda E., Tollenaar, Rob A. E. M., Torres, Diana, Torres-Mejía, Gabriela, Troester, Melissa A., Truong, Thérèse, Vachon, Celine M., Vijai, Joseph, Weinberg, Clarice R., Wendt, Camilla, Winqvist, Robert, Wolk, Alicja, Wu, Anna H., Yamaji, Taiki, Yang, Xiaohong R., Yu, Jyh-Cherng, Zheng, Wei, Ziogas, Argyrios, Ziv, Elad, Dunning, Alison M., Easton, Douglas F., Hemingway, Harry, Hamann, Ute, and Kuchenbaecker, Karoline B.

Published
2023
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17. Racial differences in breast cancer outcomes by hepatocyte growth factor pathway expression

Author

Jones, Gieira S, Hoadley, Katherine A, Benefield, Halei, Olsson, Linnea T, Hamilton, Alina M, Bhattacharya, Arjun, Kirk, Erin L, Tipaldos, Heather J, Fleming, Jodie M, Williams, Kevin P, Love, Michael I, Nichols, Hazel B, Olshan, Andrew F, and Troester, Melissa A

Subjects
Breast Cancer, Cancer, Genetics, Clinical Research, Good Health and Well Being, Black People, Breast Neoplasms, Female, Hepatocyte Growth Factor, Humans, Proportional Hazards Models, Race Factors, White People, Breast cancer, Hepatocyte growth factor, Clinical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

PurposeBlack women have a 40% increased risk of breast cancer-related mortality. These outcome disparities may reflect differences in tumor pathways and a lack of targetable therapies for specific subtypes that are more common in Black women. Hepatocyte growth factor (HGF) is a targetable pathway that promotes breast cancer tumorigenesis, is associated with basal-like breast cancer, and is differentially expressed by race. This study assessed whether a 38-gene HGF expression signature is associated with recurrence and survival in Black and non-Black women.MethodsStudy participants included 1957 invasive breast cancer cases from the Carolina Breast Cancer Study. The HGF signature was evaluated in association with recurrence (n = 1251, 171 recurrences), overall, and breast cancer-specific mortality (n = 706, 190/328 breast cancer/overall deaths) using Cox proportional hazard models.ResultsWomen with HGF-positive tumors had higher recurrence rates [HR 1.88, 95% CI (1.19, 2.98)], breast cancer-specific mortality [HR 1.90, 95% CI (1.26, 2.85)], and overall mortality [HR 1.69; 95% CI (1.17, 2.43)]. Among Black women, HGF positivity was significantly associated with higher 5-year rate of recurrence [HR 1.73; 95% CI (1.01, 2.99)], but this association was not significant in non-Black women [HR 1.68; 95% CI (0.72, 3.90)]. Among Black women, HGF-positive tumors had elevated breast cancer-specific mortality [HR 1.80, 95% CI (1.05, 3.09)], which was not significant in non-Black women [HR 1.52; 95% CI (0.78, 2.99)].ConclusionThis multi-gene HGF signature is a poor-prognosis feature for breast cancer and may identify patients who could benefit from HGF-targeted treatments, an unmet need for Black and triple-negative patients.

Published
2022

19. Lung and extrathoracic cancer incidence among underground uranium miners exposed to radon progeny in the Příbram region of the Czech Republic: a case-cohort study.

Author

Kelly-Reif, Kaitlin, Sandler, Dale, Shore, David, Schubauer-Berigan, Mary, Troester, Melissa, Nylander-French, Leena, and Richardson, David

Subjects
cancer, ionizing, miners, radiation, radon, Adult, Aged, Aged, 80 and over, Cohort Studies, Czech Republic, Humans, Incidence, Lung Neoplasms, Male, Middle Aged, Miners, Neoplasms, Neoplasms, Radiation-Induced, Occupational Exposure, Radon, Radon Daughters, Smoking, Uranium
Abstract

OBJECTIVES: Radon is carcinogenic, but more studies are needed to understand relationships with lung cancer and extrathoracic cancers at low exposures. There are few studies evaluating associations with cancer incidence or assessing the modifying effects of smoking. METHODS: We conducted a case-cohort study with 16 434 underground uranium miners in the Czech Republic with cancer incidence follow-up 1977-1996. Associations between radon exposure and lung cancer, and extrathoracic cancer, were estimated with linear excess relative rate (ERR) models. We examined potential modifying effects of smoking, time since exposure and exposure rate. RESULTS: Under a simple ERR model, assuming a 5-year exposure lag, the estimated ERR of lung cancer per 100 working level months (WLM) was 0.54 (95% CI 0.33 to 0.83) and the estimated ERR of extrathoracic cancer per 100 WLM was 0.07 (95% CI -0.17 to 0.72). Most lung cancer cases were observed among smokers (82%), and the estimated ERR of lung cancer per 100 WLM was larger among smokers (ERR/100 WLM=1.35; 95% CI 0.84 to 2.15) than among never smokers (ERR/100 WLM=0.12; 95% CI -0.05 to 0.49). Among smokers, the estimated ERR of lung cancer per 100 WLM decreased with time since exposure from 3.07 (95% CI -0.04 to 10.32) in the period 5-14 years after exposure to 1.05 (95% CI 0.49 to 1.87) in the period 25+ years after exposure. CONCLUSIONS: We observed positive associations between cumulative radon exposure and lung cancer, consistent with prior studies. We observed a positive association between cumulative radon exposure and extrathoracic cancers, although the estimates were small. There was evidence that the association between radon and lung cancer was modified by smoking in a multiplicative or super-multiplicative fashion.

Published
2022

20. Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Author

Patel, Achal, García-Closas, Montserrat, Olshan, Andrew F, Perou, Charles M, Troester, Melissa A, Love, Michael I, and Bhattacharya, Arjun

Subjects
Genetics, Clinical Research, Prevention, Cancer, Aging, Genetic Testing, Breast Cancer, Good Health and Well Being, Black People, Breast Neoplasms, Female, Gene Expression Profiling, Genes, Neoplasm, Germ Cells, Humans, Neoplasm Recurrence, Local, Risk Factors, White People, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Published
2022

21. Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women

Author

Wang, Xiaoliang, Kapoor, Pooja Middha, Auer, Paul L, Dennis, Joe, Dunning, Alison M, Wang, Qin, Lush, Michael, Michailidou, Kyriaki, Bolla, Manjeet K, Aronson, Kristan J, Murphy, Rachel A, Brooks-Wilson, Angela, Lee, Derrick G, Cordina-Duverger, Emilie, Guénel, Pascal, Truong, Thérèse, Mulot, Claire, Teras, Lauren R, Patel, Alpa V, Dossus, Laure, Kaaks, Rudolf, Hoppe, Reiner, Lo, Wing-Yee, Brüning, Thomas, Hamann, Ute, Czene, Kamila, Gabrielson, Marike, Hall, Per, Eriksson, Mikael, Jung, Audrey, Becher, Heiko, Couch, Fergus J, Larson, Nicole L, Olson, Janet E, Ruddy, Kathryn J, Giles, Graham G, MacInnis, Robert J, Southey, Melissa C, Le Marchand, Loic, Wilkens, Lynne R, Haiman, Christopher A, Olsson, Håkan, Augustinsson, Annelie, Krüger, Ute, Wagner, Philippe, Scott, Christopher, Winham, Stacey J, Vachon, Celine M, Perou, Charles M, Olshan, Andrew F, Troester, Melissa A, Hunter, David J, Eliassen, Heather A, Tamimi, Rulla M, Brantley, Kristen, Andrulis, Irene L, Figueroa, Jonine, Chanock, Stephen J, Ahearn, Thomas U, García-Closas, Montserrat, Evans, Gareth D, Newman, William G, van Veen, Elke M, Howell, Anthony, Wolk, Alicja, Håkansson, Niclas, Anton-Culver, Hoda, Ziogas, Argyrios, Jones, Michael E, Orr, Nick, Schoemaker, Minouk J, Swerdlow, Anthony J, Kitahara, Cari M, Linet, Martha, Prentice, Ross L, Easton, Douglas F, Milne, Roger L, Kraft, Peter, Chang-Claude, Jenny, and Lindström, Sara

Subjects
Genetics, Cancer, Aging, Human Genome, Breast Cancer, Prevention, Estrogen, 2.1 Biological and endogenous factors, Aetiology, Breast, Breast Neoplasms, Estrogen Replacement Therapy, Female, Hormone Replacement Therapy, Humans, Male, Menopause, Risk Factors
Abstract

Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values

Published
2022

22. Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis.

Author

Escala-Garcia, Maria, Canisius, Sander, Keeman, Renske, Beesley, Jonathan, Anton-Culver, Hoda, Arndt, Volker, Augustinsson, Annelie, Becher, Heiko, Beckmann, Matthias W, Behrens, Sabine, Bermisheva, Marina, Bojesen, Stig E, Bolla, Manjeet K, Brenner, Hermann, Canzian, Federico, Castelao, Jose E, Chang-Claude, Jenny, Chanock, Stephen J, Couch, Fergus J, Czene, Kamila, Daly, Mary B, Dennis, Joe, Devilee, Peter, Dörk, Thilo, Dunning, Alison M, Easton, Douglas F, Ekici, Arif B, Eliassen, A Heather, Fasching, Peter A, Flyger, Henrik, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A, Geisler, Jürgen, Giles, Graham G, Grip, Mervi, Gündert, Melanie, Hahnen, Eric, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hartikainen, Jaana M, Heemskerk-Gerritsen, Bernadette AM, Hollestelle, Antoinette, Hoppe, Reiner, Hopper, John L, Hunter, David J, Jacot, William, Jakubowska, Anna, John, Esther M, Jung, Audrey Y, Kaaks, Rudolf, Khusnutdinova, Elza, Koppert, Linetta B, Kraft, Peter, Kristensen, Vessela N, Kurian, Allison W, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Luben, Robert N, Lubiński, Jan, Mannermaa, Arto, Manoochehri, Mehdi, Margolin, Sara, Mavroudis, Dimitrios, Muranen, Taru A, Nevanlinna, Heli, Olshan, Andrew F, Olsson, Håkan, Park-Simon, Tjoung-Won, Patel, Alpa V, Peterlongo, Paolo, Pharoah, Paul DP, Punie, Kevin, Radice, Paolo, Rennert, Gad, Rennert, Hedy S, Romero, Atocha, Roylance, Rebecca, Rüdiger, Thomas, Ruebner, Matthias, Saloustros, Emmanouil, Sawyer, Elinor J, Schmutzler, Rita K, Schoemaker, Minouk J, Scott, Christopher, Southey, Melissa C, Surowy, Harald, Swerdlow, Anthony J, Tamimi, Rulla M, Teras, Lauren R, Thomas, Emilie, Tomlinson, Ian, Troester, Melissa A, Vachon, Celine M, Wang, Qin, Winqvist, Robert, and Wolk, Alicja

Subjects
kConFab/AOCS Investigators, Cancer, Genetics, Breast Cancer, Prevention, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies
Abstract

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10-8 and 4.42 × 10-8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

Published
2021

23. Breast Cancer Disparities Through the Lens of the COVID-19 Pandemic

Author

Newman, Lisa, Fejerman, Laura, Pal, Tuya, Mema, Eralda, McGinty, Geraldine, Cheng, Alex, Levy, Mia, Momoh, Adeyiza, Troester, Melissa, Schneider, Bryan, McNeil, Lorna, Davis, Melissa, Babagbemi, Kemi, and Hunt, Kelly

Subjects
Prevention, Health Services, Cancer, Clinical Trials and Supportive Activities, Breast Cancer, Clinical Research, Good Health and Well Being, Breast cancer, Disparities, COVID-19, Clinical research, African Americans, Hispanic, Latina Americans, Hispanic/Latina Americans, Oncology and Carcinogenesis
Abstract

Purpose of reviewThe emergency medicine and critical care needs of the COVID-19 pandemic forced a sudden and dramatic disruption of cancer screening and treatment programs in the USA during the winter and spring of 2020. This review commentary addresses the impact of the pandemic on racial/ethnic minorities such as African Americans and Hispanic-Latina Americans, with a focus on factors related to breast cancer.Recent findingsAfrican Americans and Hispanic-Latina Americans experienced disproportionately higher morbidity and mortality from COVID-19; many of the same socioeconomic and tumor biology/genetic factors that explain breast cancer disparities are likely to account for COVID-19 outcome disparities.SummaryThe breast cancer clinical and research community should partner with public health experts to ensure participation of diverse patients in COVID-19 treatment trials and vaccine programs and to overcome COVID-19-related breast health management delays that are likely to have been magnified among African Americans and Hispanic-Latina Americans.

Published
2021

25. Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women.

Author

Adedokun, Babatunde, Du, Zhaohui, Gao, Guimin, Ahearn, Thomas U, Lunetta, Kathryn L, Zirpoli, Gary, Figueroa, Jonine, John, Esther M, Bernstein, Leslie, Zheng, Wei, Hu, Jennifer J, Ziegler, Regina G, Nyante, Sarah, Bandera, Elisa V, Ingles, Sue A, Press, Michael F, Deming-Halverson, Sandra L, Rodriguez-Gil, Jorge L, Yao, Song, Ogundiran, Temidayo O, Ojengbede, Oladosu, Blot, William, Troester, Melissa A, Nathanson, Katherine L, Hennis, Anselm, Nemesure, Barbara, Ambs, Stefan, Fiorica, Peter N, Sucheston-Campbell, Lara E, Bensen, Jeannette T, Kushi, Lawrence H, Torres-Mejia, Gabriela, Hu, Donglei, Fejerman, Laura, Bolla, Manjeet K, Dennis, Joe, Dunning, Alison M, Easton, Douglas F, Michailidou, Kyriaki, Pharoah, Paul DP, Wang, Qin, Sandler, Dale P, Taylor, Jack A, O'Brien, Katie M, Kitahara, Cari M, Falusi, Adeyinka G, Babalola, Chinedum, Yarney, Joel, Awuah, Baffour, Addai-Wiafe, Beatrice, GBHS Study Team, Chanock, Stephen J, Olshan, Andrew F, Ambrosone, Christine B, Conti, David V, Ziv, Elad, Olopade, Olufunmilayo I, Garcia-Closas, Montserrat, Palmer, Julie R, Haiman, Christopher A, and Huo, Dezheng

Subjects
GBHS Study Team, Humans, Breast Neoplasms, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Introns, African Continental Ancestry Group, European Continental Ancestry Group, Female, Genome-Wide Association Study
Abstract

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P

Published
2021

26. Joint and individual analysis of breast cancer histologic images and genomic covariates

Author

Carmichael, Iain, Calhoun, Benjamin C., Hoadley, Katherine A., Troester, Melissa A., Geradts, Joseph, Couture, Heather D., Olsson, Linnea, Perou, Charles M., Niethammer, Marc, Hannig, Jan, and Marron, J. S.

Subjects
Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications
Abstract

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics.

Published
2019

27. Genome-Wide Interaction Analysis of Menopausal Hormone Therapy Use and Breast Cancer Risk Among 62,370 Women

Author

Wang, Xiaoliang, Kapoor, Pooja Middha, Auer, Paul L, Dennis, Joe, Dunning, Alison M, Wang, Qin, Lush, Michael, Michailidou, Kyriaki, Bolla, Manjeet K, Aronson, Kristan J, Murphy, Rachel A, Brooks-Wilson, Angela, Lee, Derrick G, Cordina-Duverger, Emilie, Guénel, Pascal, Truong, Thérèse, Mulot, Claire, Teras, Lauren R, Patel, Alpa V, Dossus, Laure, Kaaks, Rudolf, Hoppe, Reiner, Lo, Wing-Yee, Brüning, Thomas, Hamann, Ute, Czene, Kamila, Gabrielson, Marike, Hall, Per, Eriksson, Mikael, Jung, Audrey, Becher, Heiko, Couch, Fergus J, Larson, Nicole L, Olson, Janet E, Ruddy, Kathryn J, Giles, Graham G, MacInnis, Robert J, Southey, Melissa C, Marchand, Loic Le, Wilkens, Lynne R, Haiman, Christopher A, Olsson, Håkan, Augustinsson, Annelie, Krüger, Ute, Wagner, Philippe, Scott, Christopher, Winham, Stacey J, Vachon, Celine M, Perou, Charles M, Olshan, Andrew F, Troester, Melissa A, Hunter, David J, Eliassen, A Heather, Tamimi, Rulla M, Brantley, Kristen, Andrulis, Irene L, Figueroa, Jonine, Chanock, Stephen J, Ahearn, Thomas U, García-Closas, Montserrat, Evans, Gareth D, Newman, William G, Veen, Elke M van, Howell, Anthony, Wolk, Alicja, Håkansson, Niclas, Anton-Culver, Hoda, Ziogas, Argyrios, Jones, Michael E, Orr, Nick, Schoemaker, Minouk J, Swerdlow, Anthony J, Kitahara, Cari M, Linet, Martha, Prentice, Ross L, Easton, Douglas F, Milne, Roger L, Kraft, Peter, Chang-Claude, Jenny, and Lindström, Sara

Subjects
Cancer, Prevention, Breast Cancer, Genetics, Human Genome, Aging, Estrogen, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being
Abstract

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values

Published
2021

28. DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Author

Bhattacharya, Arjun, Hamilton, Alina M, Troester, Melissa A, and Love, Michael I

Subjects
Clinical Research, Breast Cancer, Genetics, Cancer, Algorithms, Animals, Benchmarking, Breast Neoplasms, Computational Biology, Databases, Genetic, Female, Gene Expression Profiling, Genomics, Humans, Lung Neoplasms, Male, Neoplasms, Prostatic Neoplasms, Quantitative Trait Loci, RNA, Messenger, RNA-Seq, Receptors, CCR3, Single-Cell Analysis, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Developmental Biology
Abstract

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

Published
2021

29. Deep Multi-View Learning via Task-Optimal CCA

Author

Couture, Heather D., Kwitt, Roland, Marron, J. S., Troester, Melissa, Perou, Charles M., and Niethammer, Marc

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CCA projection for discrimination and the CCA projection itself, or they are linear only. We address these deficiencies by simultaneously optimizing a CCA-based and a task objective in an end-to-end manner. Together, these two objectives learn a non-linear CCA projection to a shared latent space that is highly correlated and discriminative. Our method shows a significant improvement over previous state-of-the-art (including deep supervised approaches) for cross-view classification, regularization with a second view, and semi-supervised learning on real data.

Published
2019

31. Case-Case Genome-Wide Analyses Identify Subtype-Informative Variants that Confer Risk for Breast Cancer

Author

Sun, Xiaohui, primary, Verma, Shiv Prakash, additional, Jia, Guochong, additional, Wang, Xinjun, additional, Ping, Jie, additional, Guo, Xingyi, additional, Shu, Xiao-Ou, additional, Chen, Jianhong, additional, Derkach, Andriy, additional, Cai, Qiuyin, additional, Liang, Xiaolin, additional, Long, Jirong, additional, Offit, Kenneth, additional, Oh, Jung Hun, additional, Reiner, Anne S., additional, Watt, Gordon P., additional, Woods, Meghan, additional, Yang, Yaohua, additional, Ambrosone, Christine B., additional, Ambs, Stefan, additional, Chen, Yu, additional, Concannon, Patrick, additional, Garcia-Closas, Montserrat, additional, Gu, Jian, additional, Haiman, Christopher A., additional, Hu, Jennifer J., additional, Huo, Dezheng, additional, John, Esther M., additional, Knight, Julia A., additional, Li, Christopher I., additional, Lynch, Charles F., additional, Mellemkjaer, Lene, additional, Nathanson, Katherine L., additional, Nemesure, Barbara, additional, Olopade, Olufunmilayo I., additional, Olshan, Andrew F., additional, Pal, Tuya, additional, Palmer, Julie R., additional, Press, Michael F., additional, Sanderson, Maureen, additional, Sandler, Dale P., additional, Troester, Melissa A., additional, Zheng, Wei, additional, Bernstein, Jonine L., additional, Buas, Matthew F., additional, and Shu, Xiang, additional

Published
2024
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33. Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Author

Couture, Heather D., Marron, J. S., Perou, Charles M., Troester, Melissa A., and Niethammer, Marc

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity.

Published
2018

38. Rare germline copy number variants (CNVs) and breast cancer risk

Author

Dennis, Joe, Tyrer, Jonathan P., Walker, Logan C., Michailidou, Kyriaki, Dorling, Leila, Bolla, Manjeet K., Wang, Qin, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antonenkova, Natalia N., Arndt, Volker, Aronson, Kristan J., Freeman, Laura E. Beane, Beckmann, Matthias W., Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Castelao, Jose E., Chang-Claude, Jenny, Chenevix-Trench, Georgia, Clarke, Christine L., Collée, J. Margriet, Couch, Fergus J., Cox, Angela, Cross, Simon S., Czene, Kamila, Devilee, Peter, Dörk, Thilo, Dossus, Laure, Eliassen, A. Heather, Eriksson, Mikael, Evans, D. Gareth, Fasching, Peter A., Figueroa, Jonine, Fletcher, Olivia, Flyger, Henrik, Fritschi, Lin, Gabrielson, Marike, Gago-Dominguez, Manuela, García-Closas, Montserrat, Giles, Graham G., González-Neira, Anna, Guénel, Pascal, Hahnen, Eric, Haiman, Christopher A., Hall, Per, Hollestelle, Antoinette, Hoppe, Reiner, Hopper, John L., Howell, Anthony, Jager, Agnes, Jakubowska, Anna, John, Esther M., Johnson, Nichola, Jones, Michael E., Jung, Audrey, Kaaks, Rudolf, Keeman, Renske, Khusnutdinova, Elza, Kitahara, Cari M., Ko, Yon-Dschun, Kosma, Veli-Matti, Koutros, Stella, Kraft, Peter, Kristensen, Vessela N., Kubelka-Sabit, Katerina, Kurian, Allison W., Lacey, James V., Lambrechts, Diether, Larson, Nicole L., Linet, Martha, Ogrodniczak, Alicja, Mannermaa, Arto, Manoukian, Siranoush, Margolin, Sara, Mavroudis, Dimitrios, Milne, Roger L., Muranen, Taru A., Murphy, Rachel A., Nevanlinna, Heli, Olson, Janet E., Olsson, Håkan, Park-Simon, Tjoung-Won, Perou, Charles M., Peterlongo, Paolo, Plaseska-Karanfilska, Dijana, Pylkäs, Katri, Rennert, Gad, Saloustros, Emmanouil, Sandler, Dale P., Sawyer, Elinor J., Schmidt, Marjanka K., Schmutzler, Rita K., Shibli, Rana, Smeets, Ann, Soucy, Penny, Southey, Melissa C., Swerdlow, Anthony J., Tamimi, Rulla M., Taylor, Jack A., Teras, Lauren R., Terry, Mary Beth, Tomlinson, Ian, Troester, Melissa A., Truong, Thérèse, Vachon, Celine M., Wendt, Camilla, Winqvist, Robert, Wolk, Alicja, Yang, Xiaohong R., Zheng, Wei, Ziogas, Argyrios, Simard, Jacques, Dunning, Alison M., Pharoah, Paul D. P., and Easton, Douglas F.

Published
2022
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40. Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Author

Ahearn, Thomas U., Zhang, Haoyu, Michailidou, Kyriaki, Milne, Roger L., Bolla, Manjeet K., Dennis, Joe, Dunning, Alison M., Lush, Michael, Wang, Qin, Andrulis, Irene L., Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J., Auer, Paul L., Augustinsson, Annelie, Baten, Adinda, Becher, Heiko, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Blomqvist, Carl, Bojesen, Stig E., Bonanni, Bernardo, Børresen-Dale, Anne-Lise, Brauch, Hiltrud, Brenner, Hermann, Brooks-Wilson, Angela, Brüning, Thomas, Burwinkel, Barbara, Buys, Saundra S., Canzian, Federico, Castelao, Jose E., Chang-Claude, Jenny, Chanock, Stephen J., Chenevix-Trench, Georgia, Clarke, Christine L., Collée, J. Margriet, Cox, Angela, Cross, Simon S., Czene, Kamila, Daly, Mary B., Devilee, Peter, Dörk, Thilo, Dwek, Miriam, Eccles, Diana M., Evans, D. Gareth, Fasching, Peter A., Figueroa, Jonine, Floris, Giuseppe, Gago-Dominguez, Manuela, Gapstur, Susan M., García-Sáenz, José A., Gaudet, Mia M., Giles, Graham G., Goldberg, Mark S., González-Neira, Anna, Alnæs, Grethe I. Grenaker, Grip, Mervi, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Harkness, Elaine F., Heemskerk-Gerritsen, Bernadette A. M., Holleczek, Bernd, Hollestelle, Antoinette, Hooning, Maartje J., Hoover, Robert N., Hopper, John L., Howell, Anthony, Jakimovska, Milena, Jakubowska, Anna, John, Esther M., Jones, Michael E., Jung, Audrey, Kaaks, Rudolf, Kauppila, Saila, Keeman, Renske, Khusnutdinova, Elza, Kitahara, Cari M., Ko, Yon-Dschun, Koutros, Stella, Kristensen, Vessela N., Krüger, Ute, Kubelka-Sabit, Katerina, Kurian, Allison W., Kyriacou, Kyriacos, Lambrechts, Diether, Lee, Derrick G., Lindblom, Annika, Linet, Martha, Lissowska, Jolanta, Llaneza, Ana, Lo, Wing-Yee, MacInnis, Robert J., Mannermaa, Arto, Manoochehri, Mehdi, Margolin, Sara, Martinez, Maria Elena, McLean, Catriona, Meindl, Alfons, Menon, Usha, Nevanlinna, Heli, Newman, William G., Nodora, Jesse, Offit, Kenneth, Olsson, Håkan, Orr, Nick, Park-Simon, Tjoung-Won, Patel, Alpa V., Peto, Julian, Pita, Guillermo, Plaseska-Karanfilska, Dijana, Prentice, Ross, Punie, Kevin, Pylkäs, Katri, Radice, Paolo, Rennert, Gad, Romero, Atocha, Rüdiger, Thomas, Saloustros, Emmanouil, Sampson, Sarah, Sandler, Dale P., Sawyer, Elinor J., Schmutzler, Rita K., Schoemaker, Minouk J., Schöttker, Ben, Sherman, Mark E., Shu, Xiao-Ou, Smichkoska, Snezhana, Southey, Melissa C., Spinelli, John J., Swerdlow, Anthony J., Tamimi, Rulla M., Tapper, William J., Taylor, Jack A., Teras, Lauren R., Terry, Mary Beth, Torres, Diana, Troester, Melissa A., Vachon, Celine M., van Deurzen, Carolien H. M., van Veen, Elke M., Wagner, Philippe, Weinberg, Clarice R., Wendt, Camilla, Wesseling, Jelle, Winqvist, Robert, Wolk, Alicja, Yang, Xiaohong R., Zheng, Wei, Couch, Fergus J., Simard, Jacques, Kraft, Peter, Easton, Douglas F., Pharoah, Paul D. P., Schmidt, Marjanka K., García-Closas, Montserrat, and Chatterjee, Nilanjan

Published
2022
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42. Supplementary Table S1 from Disparities in OncotypeDx Testing and Subsequent Chemotherapy Receipt by Geography and Socioeconomic Status

Author

Van Alsten, Sarah C., primary, Dunn, Matthew R., primary, Hamilton, Alina M., primary, Ivory, Joannie M., primary, Gao, Xiaohua, primary, Kirk, Erin L., primary, Nsonwu-Farley, Joseph S., primary, Carey, Lisa A., primary, Abdou, Yara, primary, Reeder-Hayes, Katherine E., primary, Roberson, Mya L., primary, Wheeler, Stephanie B., primary, Emerson, Marc A., primary, Hyslop, Terry, primary, and Troester, Melissa A., primary

Published
2024
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