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1. Comparative biological activity of palbociclib and ribociclib in hormone receptor-positive breast cancer

Author

Lorman-Carbó, Natàlia, Martínez-Sáez, Olga, Fernandez-Martinez, Aranzazu, Galván, Patricia, Chic, Nuria, Garcia-Fructuoso, Isabel, Rodríguez, Adela, Gómez-Bravo, Raquel, Schettini, Francesco, Blasco, Paula, Castillo, Oleguer, González-Farré, Blanca, Adamo, Barbara, Vidal, Maria, Muñoz, Montserrat, Perou, Charles M., Malumbres, Marcos, Gavilá, Joaquín, Pascual, Tomás, Prat, Aleix, and Brasó-Maristany, Fara

Published
2024
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3. Cell-cycle inhibition and immune microenvironment in breast cancer treated with ribociclib and letrozole or chemotherapy

Author

Pascual, Tomás, Fernandez-Martinez, Aranzazu, Agrawal, Yash, Pfefferle, Adam D., Chic, Nuria, Brasó-Maristany, Fara, Gonzàlez-Farré, Blanca, Paré, Laia, Villacampa, Guillermo, Saura, Cristina, Hernando, Cristina, Muñoz, Montserrat, Galván, Patricia, Gonzàlez-Farré, Xavier, Oliveira, Mafalda, Gil-Gil, Miguel, Ciruelos, Eva, Villagrasa, Patricia, Gavilá, Joaquín, Prat, Aleix, and Perou, Charles M.

Published
2024
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4. Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Author

Jiang, Yi-Zhou, Ma, Ding, Jin, Xi, Xiao, Yi, Yu, Ying, Shi, Jinxiu, Zhou, Yi-Fan, Fu, Tong, Lin, Cai-Jin, Dai, Lei-Jie, Liu, Cheng-Lin, Zhao, Shen, Su, Guan-Hua, Hou, Wanwan, Liu, Yaqing, Chen, Qingwang, Yang, Jingcheng, Zhang, Naixin, Zhang, Wen-Juan, Liu, Wei, Ge, Weigang, Yang, Wen-Tao, You, Chao, Gu, Yajia, Kaklamani, Virginia, Bertucci, François, Verschraegen, Claire, Daemen, Anneleen, Shah, Nakul M., Wang, Ting, Guo, Tiannan, Shi, Leming, Perou, Charles M., Zheng, Yuanting, Huang, Wei, and Shao, Zhi-Ming

Published
2024
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5. MRE11 liberates cGAS from nucleosome sequestration during tumorigenesis

Author

Cho, Min-Guk, Kumar, Rashmi J., Lin, Chien-Chu, Boyer, Joshua A., Shahir, Jamshaid A., Fagan-Solis, Katerina, Simpson, Dennis A., Fan, Cheng, Foster, Christine E., Goddard, Anna M., Lerner, Lynn M., Ellington, Simon W., Wang, Qinhong, Wang, Ying, Ho, Alice Y., Liu, Pengda, Perou, Charles M., Zhang, Qi, McGinty, Robert K., Purvis, Jeremy E., and Gupta, Gaorav P.

Published
2024
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6. 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

7. Pairwise Nonlinear Dependence Analysis of Genomic Data

Author

Xiang, Siqi, Zhang, Wan, Liu, Siyao, Hoadley, Katherine A., Perou, Charles M., Zhang, Kai, and Marron, J. S.

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel., Comment: 21 pages, 2 supplements

Published
2022

9. Immunological and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 randomized trials

Author

Rediti, Mattia, Fernandez-Martinez, Aranzazu, Venet, David, Rothé, Françoise, Hoadley, Katherine A., Parker, Joel S., Singh, Baljit, Campbell, Jordan D., Ballman, Karla V., Hillman, David W., Winer, Eric P., El-Abed, Sarra, Piccart, Martine, Di Cosimo, Serena, Symmans, William Fraser, Krop, Ian E., Salgado, Roberto, Loi, Sherene, Pusztai, Lajos, Perou, Charles M., Carey, Lisa A., and Sotiriou, Christos

Published
2023
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10. Distinct shared and compartment-enriched oncogenic networks drive primary versus metastatic breast cancer

Author

Jiang, Zhe, Ju, YoungJun, Ali, Amjad, Chung, Philip E. D., Skowron, Patryk, Wang, Dong-Yu, Shrestha, Mariusz, Li, Huiqin, Liu, Jeff C., Vorobieva, Ioulia, Ghanbari-Azarnier, Ronak, Mwewa, Ethel, Koritzinsky, Marianne, Ben-David, Yaacov, Woodgett, James R., Perou, Charles M., Dupuy, Adam, Bader, Gary D., Egan, Sean E., Taylor, Michael D., and Zacksenhaus, Eldad

Published
2023
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13. Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer

Author

Prat, Aleix, Brasó-Maristany, Fara, Martínez-Sáez, Olga, Sanfeliu, Esther, Xia, Youli, Bellet, Meritxell, Galván, Patricia, Martínez, Débora, Pascual, Tomás, Marín-Aguilera, Mercedes, Rodríguez, Anna, Chic, Nuria, Adamo, Barbara, Paré, Laia, Vidal, Maria, Margelí, Mireia, Ballana, Ester, Gómez-Rey, Marina, Oliveira, Mafalda, Felip, Eudald, Matito, Judit, Sánchez-Bayona, Rodrigo, Suñol, Anna, Saura, Cristina, Ciruelos, Eva, Tolosa, Pablo, Muñoz, Montserrat, González-Farré, Blanca, Villagrasa, Patricia, Parker, Joel S., Perou, Charles M., and Vivancos, Ana

Published
2023
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14. 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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15. A 14-gene B-cell immune signature in early-stage triple-negative breast cancer (TNBC): a pooled analysis of seven studies

Author

Conte, Benedetta, Brasó-Maristany, Fara, Hernández, Adela Rodríguez, Pascual, Tomás, Villacampa, Guillermo, Schettini, Francesco, Vidal Losada, Maria J., Seguí, Elia, Angelats, Laura, Garcia-Fructuoso, Isabel, Gómez-Bravo, Raquel, Lorman-Carbó, Natàlia, Paré, Laia, Marín-Aguilera, Mercedes, Martínez-Sáez, Olga, Adamo, Barbara, Sanfeliu, Esther, Fratini, Beatrice, Falato, Claudette, Chic, Núria, Vivancos, Ana, Villagrasa, Patricia, Staaf, Johan, Parker, Joel S., Perou, Charles M., and Prat, Aleix

Published
2024
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16. 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

17. 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

18. 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, and Soucy, Penny

Subjects
Human Genome, Breast Cancer, Genetics, Clinical Research, Cancer, Prevention, 2.1 Biological and endogenous factors, Aetiology, Breast Neoplasms, Case-Control Studies, DNA Copy Number Variations, Female, Genome, Human, Genome-Wide Association Study, Germ Cells, Humans, Risk Factors, NBCS Collaborators, CTS Consortium, ABCTB Investigators, kConFab/AOCS Investigators
Abstract

Germline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E-18). Nine other genes were associated with a p-value

Published
2022

20. 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

21. 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

22. Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Author

Zhao, Na, Kabotyanski, Elena B., Saltzman, Alexander B., Malovannaya, Anna, Yuan, Xueying, Reineke, Lucas C., Lieu, Nadia, Gao, Yang, Pedroza, Diego A., Calderon, Sebastian J., Smith, Alex J., Hamor, Clark, Safari, Kazem, Savage, Sara, Zhang, Bing, Zhou, Jianling, Solis, Luisa M., Hilsenbeck, Susan G., Fan, Cheng, Perou, Charles M., and Rosen, Jeffrey M.

Subjects
Interferon -- Analysis, Immunotherapy -- Patient outcomes, Protein biosynthesis -- Analysis, Chemotherapy -- Patient outcomes, Cancer -- Chemotherapy, Health care industry
Abstract

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A with zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages toward an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon (IFN) response uniformly across models. The induction of an IFN response is partially due to the inhibition of Sox4 translation by zotatifin. A similar induction of IFN-stimulated genes was observed in breast cancer patient biopsies following zotatifin treatment. Surprisingly, zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened IFN response, resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for zotatifin, and provide a rationale for new combination regimens consisting of zotatifin and chemotherapy or immunotherapy as treatments for TNBC., Introduction Triple-negative breast cancer (TNBC) is a heterogeneous group of breast cancers defined by the absence of estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 [...]

Published
2023
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24. Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis

Author

Garcia-Recio, Susana, Hinoue, Toshinori, Wheeler, Gregory L., Kelly, Benjamin J., Garrido-Castro, Ana C., Pascual, Tomas, De Cubas, Aguirre A., Xia, Youli, Felsheim, Brooke M., McClure, Marni B., Rajkovic, Andrei, Karaesmen, Ezgi, Smith, Markia A., Fan, Cheng, Ericsson, Paula I. Gonzalez, Sanders, Melinda E., Creighton, Chad J., Bowen, Jay, Leraas, Kristen, Burns, Robyn T., Coppens, Sara, Wheless, Amy, Rezk, Salma, Garrett, Amy L., Parker, Joel S., Foy, Kelly K., Shen, Hui, Park, Ben H., Krop, Ian, Anders, Carey, Gastier-Foster, Julie, Rimawi, Mothaffar F., Nanda, Rita, Lin, Nancy U., Isaacs, Claudine, Marcom, P. Kelly, Storniolo, Anna Maria, Couch, Fergus J., Chandran, Uma, Davis, Michael, Silverstein, Jonathan, Ropelewski, Alexander, Liu, Minetta C., Hilsenbeck, Susan G., Norton, Larry, Richardson, Andrea L., Symmans, W. Fraser, Wolff, Antonio C., Davidson, Nancy E., Carey, Lisa A., Lee, Adrian V., Balko, Justin M., Hoadley, Katherine A., Laird, Peter W., Mardis, Elaine R., King, Tari A., and Perou, Charles M.

Published
2023
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26. 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

27. Combination of polymeric micelle formulation of TGFβ receptor inhibitors and paclitaxel produces consistent response across different mouse models of Triple‐negative breast cancer

Author

Vinod, Natasha, primary, Hwang, Duhyeong, additional, Fussell, Sloane Christian, additional, Owens, Tyler Cannon, additional, Tofade, Olaoluwa Christopher, additional, Benefield, Thad S., additional, Copling, Sage, additional, Ramsey, Jacob D., additional, Rädler, Patrick D., additional, Atkins, Hannah M., additional, Livingston, Eric E., additional, Ezzell, J. Ashley, additional, Sokolsky‐Papkov, Marina, additional, Yuan, Hong, additional, Perou, Charles M., additional, and Kabanov, Alexander V., additional

Published
2024
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29. Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines

Author

Luckett, Daniel J., Laber, Eric B., El-Kamary, Samer S., Fan, Cheng, Jhaveri, Ravi, Perou, Charles M., Shebl, Fatma M., and Kosorok, Michael R.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visual representation of this trade-off. Nonparametric estimators for the ROC curve, such as a weighted support vector machine (SVM), are desirable because they are robust to model misspecification. While weighted SVMs have great potential for estimating ROC curves, their theoretical properties were heretofore underdeveloped. We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter. We demonstrate the proposed confidence band method and the superior sensitivity and specificity of the weighted SVM compared to commonly used methods in diagnostic medicine using simulation studies. We present two illustrative examples: diagnosis of hepatitis C and a predictive model for treatment response in breast cancer.

Published
2018

30. 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

33. Development and Characterization of Syngeneic Orthotopic Transplant Models of Obesity-Responsive Triple-Negative Breast Cancer in C57BL/6J Mice.

Author

Carson, Meredith S., Rädler, Patrick D., Albright, Jody, VerHague, Melissa, Rezeli, Erika T., Roth, Daniel, French, John E., Perou, Charles M., Hursting, Stephen D., and Coleman, Michael F.

Subjects
BIOLOGICAL models, IN vitro studies, TRANSPLANTATION of organs, tissues, etc., DATA analysis, T-test (Statistics), RESEARCH funding, BREAST tumors, INTERNAL thoracic artery, IMMUNE system, CELLULAR signal transduction, MANN Whitney U Test, DESCRIPTIVE statistics, CELL lines, MICE, METABOLITES, RNA, ANIMAL experimentation, GENE expression profiling, ONE-way analysis of variance, STATISTICS, ANALYSIS of variance, SURVIVAL analysis (Biometry), DATA analysis software, OBESITY, DISEASE progression, SEQUENCE analysis
Abstract

Simple Summary: Transplanting cell lines into the mammary fat pad of lean and obese mice is a powerful tool to understand how breast cancer is promoted by obesity. However, for this approach to be effective, well-characterized and appropriate cell lines are needed. Here, we have developed four readily tumorigenic claudin-low triple-negative breast cancer cell lines from tumors arising from C3-TAg transgenic C57BL6 mice (B6TAg). We employ transcriptomic analysis of in vitro and in vivo samples to delineate distinct transcriptomic signatures in each cell line. We demonstrate that tumor progression of the three most distinct cell lines was accelerated by diet-induced obesity. Taken together, our data establish these B6TAg cell lines as potentially potent tools to delineate how obesity promotes triple-negative breast cancer progression. Obesity is an established risk and progression factor for triple-negative breast cancer (TNBC), but preclinical studies to delineate the mechanisms underlying the obesity-TNBC link as well as strategies to break that link are constrained by the lack of tumor models syngeneic to obesity-prone mouse strains. C3(1)/SV40 T-antigen (C3-TAg) transgenic mice on an FVB genetic background develop tumors with molecular and pathologic features that closely resemble human TNBC, but FVB mice are resistant to diet-induced obesity (DIO). Herein, we sought to develop transplantable C3-TAg cell lines syngeneic to C57BL/6 mice, an inbred mouse strain that is sensitive to DIO. We backcrossed FVB-Tg(C3-1-TAg)cJeg/JegJ to C57BL/6 mice for ten generations, and spontaneous tumors from those mice were excised and used to generate four clonal cell lines (B6TAg1.02, B6TAg2.03, B6TAg2.10, and B6TAg2.51). We characterized the growth of the four cell lines in both lean and DIO C57BL/6J female mice and performed transcriptomic profiling. Each cell line was readily tumorigenic and had transcriptional profiles that clustered as claudin-low, yet markedly differed from each other in their rate of tumor progression and transcriptomic signatures for key metabolic, immune, and oncogenic signaling pathways. DIO accelerated tumor growth of orthotopically transplanted B6TAg1.02, B6TAg2.03, and B6TAg2.51 cells. Thus, the B6TAg cell lines described herein offer promising and diverse new models to augment the study of DIO-associated TNBC. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Integrated Molecular Characterization of Testicular Germ Cell Tumors

Author

Shen, Hui, Shih, Juliann, Hollern, Daniel P, Wang, Linghua, Bowlby, Reanne, Tickoo, Satish K, Thorsson, Vésteinn, Mungall, Andrew J, Newton, Yulia, Hegde, Apurva M, Armenia, Joshua, Sánchez-Vega, Francisco, Pluta, John, Pyle, Louise C, Mehra, Rohit, Reuter, Victor E, Godoy, Guilherme, Jones, Jeffrey, Shelley, Carl S, Feldman, Darren R, Vidal, Daniel O, Lessel, Davor, Kulis, Tomislav, Cárcano, Flavio M, Leraas, Kristen M, Lichtenberg, Tara M, Brooks, Denise, Cherniack, Andrew D, Cho, Juok, Heiman, David I, Kasaian, Katayoon, Liu, Minwei, Noble, Michael S, Xi, Liu, Zhang, Hailei, Zhou, Wanding, ZenKlusen, Jean C, Hutter, Carolyn M, Felau, Ina, Zhang, Jiashan, Schultz, Nikolaus, Getz, Gad, Meyerson, Matthew, Stuart, Joshua M, Akbani, Rehan, Wheeler, David, Laird, Peter W, Nathanson, Katherine L, Cortessis, Victoria K, Hoadley, Katherine A, Wheeler, David A, Hughes, Daniel, Covington, Kyle, Jayaseelan, Joy C, Korchina, Viktoriya, Lewis, Lora, Hu, Jianhong, Doddapaneni, HarshaVardhan, Muzny, Donna, Gibbs, Richard, Hollern, Daniel, Vincent, Benjamin G, Chai, Shengjie, Smith, Christof C, Auman, J Todd, Shi, Yan, Meng, Shaowu, Skelly, Tara, Tan, Donghui, Veluvolu, Umadevi, Mieczkowski, Piotr A, Jones, Corbin D, Wilkerson, Matthew D, Balu, Saianand, Bodenheimer, Tom, Hoyle, Alan P, Jefferys, Stuart R, Mose, Lisle E, Simons, Janae V, Soloway, Matthew G, Roach, Jeffrey, Parker, Joel S, Hayes, D Neil, Perou, Charles M, Saksena, Gordon, Cibulskis, Carrie, Schumacher, Steven E, Beroukhim, Rameen, Gabriel, Stacey B, and Ally, Adrian

Subjects
Urologic Diseases, Rare Diseases, Human Genome, Cancer, Biotechnology, Genetics, DNA Copy Number Variations, DNA Methylation, Gene Expression Regulation, Neoplastic, Humans, Male, MicroRNAs, Neoplasms, Germ Cell and Embryonal, Proto-Oncogene Proteins c-kit, Seminoma, Testicular Neoplasms, ras Proteins, Cancer Genome Atlas Research Network, DNA methylation, KIT, The Cancer Genome Atlas, copy number, exome sequencing, miR-375, nonseminoma, seminoma, testicular germ cell tumors, Biochemistry and Cell Biology, Medical Physiology
Abstract

We studied 137 primary testicular germ cell tumors (TGCTs) using high-dimensional assays of genomic, epigenomic, transcriptomic, and proteomic features. These tumors exhibited high aneuploidy and a paucity of somatic mutations. Somatic mutation of only three genes achieved significance-KIT, KRAS, and NRAS-exclusively in samples with seminoma components. Integrated analyses identified distinct molecular patterns that characterized the major recognized histologic subtypes of TGCT: seminoma, embryonal carcinoma, yolk sac tumor, and teratoma. Striking differences in global DNA methylation and microRNA expression between histology subtypes highlight a likely role of epigenomic processes in determining histologic fates in TGCTs. We also identified a subset of pure seminomas defined by KIT mutations, increased immune infiltration, globally demethylated DNA, and decreased KRAS copy number. We report potential biomarkers for risk stratification, such as miRNA specifically expressed in teratoma, and others with molecular diagnostic potential, such as CpH (CpA/CpC/CpT) methylation identifying embryonal carcinomas.

Published
2018

35. Asparagine bioavailability governs metastasis in a model of breast cancer

Author

Knott, Simon RV, Wagenblast, Elvin, Khan, Showkhin, Kim, Sun Y, Soto, Mar, Wagner, Michel, Turgeon, Marc-Olivier, Fish, Lisa, Erard, Nicolas, Gable, Annika L, Maceli, Ashley R, Dickopf, Steffen, Papachristou, Evangelia K, D’Santos, Clive S, Carey, Lisa A, Wilkinson, John E, Harrell, J Chuck, Perou, Charles M, Goodarzi, Hani, Poulogiannis, George, and Hannon, Gregory J

Subjects
Ecological Applications, Biomedical and Clinical Sciences, Environmental Sciences, Oncology and Carcinogenesis, Breast Cancer, Cancer, Animals, Asparaginase, Asparagine, Aspartate-Ammonia Ligase, Biological Availability, Breast Neoplasms, Cell Line, Tumor, Disease Models, Animal, Disease Progression, Epithelial-Mesenchymal Transition, Female, Humans, Lung Neoplasms, Male, Mice, Neoplasm Invasiveness, Neoplasm Metastasis, Prognosis, Prostatic Neoplasms, RNA Interference, Reproducibility of Results, General Science & Technology
Abstract

Using a functional model of breast cancer heterogeneity, we previously showed that clonal sub-populations proficient at generating circulating tumour cells were not all equally capable of forming metastases at secondary sites. A combination of differential expression and focused in vitro and in vivo RNA interference screens revealed candidate drivers of metastasis that discriminated metastatic clones. Among these, asparagine synthetase expression in a patient's primary tumour was most strongly correlated with later metastatic relapse. Here we show that asparagine bioavailability strongly influences metastatic potential. Limiting asparagine by knockdown of asparagine synthetase, treatment with l-asparaginase, or dietary asparagine restriction reduces metastasis without affecting growth of the primary tumour, whereas increased dietary asparagine or enforced asparagine synthetase expression promotes metastatic progression. Altering asparagine availability in vitro strongly influences invasive potential, which is correlated with an effect on proteins that promote the epithelial-to-mesenchymal transition. This provides at least one potential mechanism for how the bioavailability of a single amino acid could regulate metastatic progression.

Published
2018

37. The VEGF-Hypoxia Signature is Upregulated in Basal like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

Author

Han, Yoo Jane, primary, Liu, Siyao, additional, Hardeman, Ashley, additional, Rajagopal, Padma Sheila., additional, Mueller, Jeffrey, additional, Khramtsova, Galina, additional, Sanni, Ayodele, additional, Ajani, Mustapha A., additional, Clayton, Wendy, additional, Hurley, Ian W., additional, Yoshimatsu, Toshio F., additional, Zheng, Yonglan, additional, Parker, Joel, additional, Perou, Charles M., additional, and Olopade, Olufunmilayo I., additional

Published
2024
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38. Research autopsy programmes in oncology: shared experience from 14 centres across the world

Author

Geukens, Tatjana, primary, Maetens, Marion, additional, Hooper, Jody E, additional, Oesterreich, Steffi, additional, Lee, Adrian V, additional, Miller, Lori, additional, Atkinson, Jenny M, additional, Rosenzweig, Margaret, additional, Puhalla, Shannon, additional, Thorne, Heather, additional, Devereux, Lisa, additional, Bowtell, David, additional, Loi, Sherene, additional, Bacon, Eliza R, additional, Ihle, Kena, additional, Song, Mihae, additional, Rodriguez‐Rodriguez, Lorna, additional, Welm, Alana L, additional, Gauchay, Lisa, additional, Murali, Rajmohan, additional, Chanda, Pharto, additional, Karacay, Ali, additional, Naceur‐Lombardelli, Cristina, additional, Bridger, Hayley, additional, Swanton, Charles, additional, Jamal‐Hanjani, Mariam, additional, Kollath, Lori, additional, True, Lawrence, additional, Morrissey, Colm, additional, Chambers, Meagan, additional, Chinnaiyan, Arul M, additional, Wilson, Allecia, additional, Mehra, Rohit, additional, Reichert, Zachery, additional, Carey, Lisa A, additional, Perou, Charles M, additional, Kelly, Erin, additional, Maeda, Daichi, additional, Goto, Akiteru, additional, Kulka, Janina, additional, Székely, Borbála, additional, Szasz, A Marcell, additional, Tőkés, Anna‐Mária, additional, Van Den Bogaert, Wouter, additional, Floris, Giuseppe, additional, and Desmedt, Christine, additional

Published
2024
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39. Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer

Author

Fernandez-Martinez, Aranzazu, primary, Rediti, Mattia, additional, Tang, Gong, additional, Pascual, Tomás, additional, Hoadley, Katherine A., additional, Venet, David, additional, Rashid, Naim U., additional, Spears, Patricia A., additional, Islam, Md N., additional, El-Abed, Sarra, additional, Bliss, Judith, additional, Lambertini, Matteo, additional, Di Cosimo, Serena, additional, Huobe, Jens, additional, Goerlitz, David, additional, Hu, Rong, additional, Lucas, Peter C., additional, Swain, Sandra M., additional, Sotiriou, Christos, additional, Perou, Charles M., additional, and Carey, Lisa A., additional

Published
2024
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40. Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Author

Howard, Frederick M., primary, Hieromnimon, Hanna M., additional, Ramesh, Siddhi, additional, Dolezal, James, additional, Kochanny, Sara, additional, Zhang, Qianchen, additional, Feiger, Brad, additional, Peterson, Joseph, additional, Fan, Cheng, additional, Perou, Charles M., additional, Vickery, Jasmine, additional, Sullivan, Megan, additional, Cole, Kimberly, additional, Khramtsova, Galina, additional, and Pearson, Alexander T., additional

Published
2024
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42. Endocrine-Sensitive Disease Rate in Postmenopausal Patients With Estrogen Receptor–Rich/ERBB2-Negative Breast Cancer Receiving Neoadjuvant Anastrozole, Fulvestrant, or Their Combination

Author

Ma, Cynthia X., primary, Suman, Vera J., additional, Sanati, Souzan, additional, Vij, Kiran, additional, Anurag, Meenakshi, additional, Leitch, A. Marilyn, additional, Unzeitig, Gary W., additional, Hoog, Jeremy, additional, Fernandez-Martinez, Aranzazu, additional, Fan, Cheng, additional, Gibbs, Richard A., additional, Watson, Mark A., additional, Dockter, Travis J., additional, Hahn, Olwen, additional, Guenther, Joseph M., additional, Caudle, Abigail, additional, Crouch, Erika, additional, Tiersten, Amy, additional, Mita, Monica, additional, Razaq, Wajeeha, additional, Hieken, Tina J., additional, Wang, Yang, additional, Rimawi, Mothaffar F., additional, Weiss, Anna, additional, Winer, Eric P., additional, Hunt, Kelly K., additional, Perou, Charles M., additional, Ellis, Matthew J., additional, Partridge, Ann H., additional, and Carey, Lisa A., additional

Published
2024
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45. A single-cell and spatially resolved atlas of human breast cancers

Author

Wu, Sunny Z., Al-Eryani, Ghamdan, Roden, Daniel Lee, Junankar, Simon, Harvey, Kate, Andersson, Alma, Thennavan, Aatish, Wang, Chenfei, Torpy, James R., Bartonicek, Nenad, Wang, Taopeng, Larsson, Ludvig, Kaczorowski, Dominik, Weisenfeld, Neil I., Uytingco, Cedric R., Chew, Jennifer G., Bent, Zachary W., Chan, Chia-Ling, Gnanasambandapillai, Vikkitharan, Dutertre, Charles-Antoine, Gluch, Laurence, Hui, Mun N., Beith, Jane, Parker, Andrew, Robbins, Elizabeth, Segara, Davendra, Cooper, Caroline, Mak, Cindy, Chan, Belinda, Warrier, Sanjay, Ginhoux, Florent, Millar, Ewan, Powell, Joseph E., Williams, Stephen R., Liu, X. Shirley, O’Toole, Sandra, Lim, Elgene, Lundeberg, Joakim, Perou, Charles M., and Swarbrick, Alexander

Published
2021
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46. The molecular basis of breast cancer pathological phenotypes

Author

Heng, Yujing J, Lester, Susan C, Tse, Gary MK, Factor, Rachel E, Allison, Kimberly H, Collins, Laura C, Chen, Yunn‐Yi, Jensen, Kristin C, Johnson, Nicole B, Jeong, Jong Cheol, Punjabi, Rahi, Shin, Sandra J, Singh, Kamaljeet, Krings, Gregor, Eberhard, David A, Tan, Puay Hoon, Korski, Konstanty, Waldman, Frederic M, Gutman, David A, Sanders, Melinda, Reis‐Filho, Jorge S, Flanagan, Sydney R, Gendoo, Deena MA, Chen, Gregory M, Haibe‐Kains, Benjamin, Ciriello, Giovanni, Hoadley, Katherine A, Perou, Charles M, and Beck, Andrew H

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Genetics, Biotechnology, Human Genome, Breast Cancer, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Biomarkers, Tumor, Breast Neoplasms, Databases, Genetic, Female, Gene Expression Profiling, Genomics, Humans, Neoplasm Invasiveness, Phenotype, Receptors, Estrogen, PAM50, TCGA, bioinformatics, genomics, mRNA, epithelial tubule formation, histological grade, Clinical Sciences, Pathology, Clinical sciences, Oncology and carcinogenesis
Abstract

The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or reverse-phase protein assay subtype. Marked nuclear pleomorphism, necrosis, inflammation and a high mitotic count were associated with the basal-like subtype, and had a similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed by use of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of poorly differentiated epithelial tubules was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative breast cancer. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has the potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology, and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Published
2017

48. ULK1 inhibition overcomes compromised antigen presentation and restores antitumor immunity in LKB1-mutant lung cancer

Author

Deng, Jiehui, Thennavan, Aatish, Dolgalev, Igor, Chen, Ting, Li, Jie, Marzio, Antonio, Poirier, John T., Peng, David H., Bulatovic, Mirna, Mukhopadhyay, Subhadip, Silver, Heather, Papadopoulos, Eleni, Pyon, Val, Thakurdin, Cassandra, Han, Han, Li, Fei, Li, Shuai, Ding, Hailin, Hu, Hai, Pan, Yuanwang, Weerasekara, Vajira, Jiang, Baishan, Wang, Eric S., Ahearn, Ian, Philips, Mark, Papagiannakopoulos, Thales, Tsirigos, Aristotelis, Rothenberg, Eli, Gainor, Justin, Freeman, Gordon J., Rudin, Charles M., Gray, Nathanael S., Hammerman, Peter S., Pagano, Michele, Heymach, John V., Perou, Charles M., Bardeesy, Nabeel, and Wong, Kwok-Kin

Published
2021
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49. A multivariable prognostic score to guide systemic therapy in early-stage HER2-positive breast cancer: a retrospective study with an external evaluation

Author

Prat, Aleix, Guarneri, Valentina, Paré, Laia, Griguolo, Gaia, Pascual, Tomás, Dieci, Maria V, Chic, Núria, González-Farré, Blanca, Frassoldati, Antonio, Sanfeliu, Esther, Cejalvo, Juan M, Muñoz, Montserrat, Bisagni, Giancarlo, Brasó-Maristany, Fara, Urso, Loredana, Vidal, Maria, Brandes, Alba A, Adamo, Barbara, Musolino, Antonino, Miglietta, Federica, Conte, Benedetta, Oliveira, Mafalda, Saura, Cristina, Pernas, Sònia, Alarcón, Jesús, Llombart-Cussac, Antonio, Cortés, Javier, Manso, Luis, López, Rafael, Ciruelos, Eva, Schettini, Francesco, Villagrasa, Patricia, Carey, Lisa A, Perou, Charles M, Piacentini, Federico, D'Amico, Roberto, Tagliafico, Enrico, Parker, Joel S, and Conte, Pierfranco

Published
2020
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50. Visual Intratumor Heterogeneity and Breast Tumor Progression.

Author

Li, Yao, Van Alsten, Sarah C., Lee, Dong Neuck, Kim, Taebin, Calhoun, Benjamin C., Perou, Charles M., Wobker, Sara E., Marron, J. S., Hoadley, Katherine A., and Troester, Melissa A.

Subjects
BREAST cancer prognosis, BIOLOGICAL models, RISK assessment, HUMAN services programs, CANCER relapse, RESEARCH funding, BREAST tumors, TUMOR markers, DESCRIPTIVE statistics, BLACK people, EPITHELIUM, STROMAL cells, CONFIDENCE intervals, GENETIC mutation, MACHINE learning, TREATMENT effect heterogeneity, DISEASE progression, REGRESSION analysis, PROPORTIONAL hazards models, MOLECULAR pathology, DISEASE risk factors
Abstract

Simple Summary: This study investigates the role of visual intratumor heterogeneity (ITH) in breast cancer progression. By analyzing histologic images from the Carolina Breast Cancer Study (CBCS) and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data using advanced image processing and machine learning techniques, we developed a measure of tumor heterogeneity based on visual features. Our findings indicate that tumors with low visual heterogeneity exhibited a higher risk of recurrence and were more likely to come from patients whose tumors comprised of only one subclone or had a TP53 mutation. Conversely, high visual heterogeneity was correlated with a more favorable prognosis. These results suggest that visual heterogeneity provides complementary information to molecular markers. A comprehensive understanding of both the visual and molecular aspects of heterogeneity has the potential to offer novel insights for treatment strategies. High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22–2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression. [ABSTRACT FROM AUTHOR]

Published
2024
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