A network analysis to identify mediators of germline-driven differences in breast cancer prognosis
Maria Escala-Garcia, Jean Abraham, Irene L. Andrulis, Hoda Anton‐Culver, Volker Arndt, Alan Ashworth, Paul L. Auer, Päivi Auvinen, Matthias W. Beckmann, Jonathan Beesley, Sabine Behrens, Javier Benı́tez, Marina Bermisheva, Carl Blomqvist, William J. Blot, Natalia Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Anne‐Lise Børresen‐Dale, Hiltrud Brauch, Hermann Brenner, Sara Y. Brucker, Barbara Burwinkel, Carlos Caldas, Federico Canzian, Jenny Chang‐Claude, Stephen J. Chanock, Suet‐Feung Chin, Christine L. Clarke, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Mary B. Daly, Joe Dennis, Peter Devilee, Janet Dunn, Alison M. Dunning, Miriam Dwek, Helena Earl, Diana M. Eccles, A. Heather Eliassen, Carolina Ellberg, D. Gareth Evans, Peter A. Fasching, Jonine D. Figueroa, Henrik Flyger, Manuela Gago-Domínguez, Susan M. Gapstur, Montserrat García‐Closas, José Á. García-Sáenz, Mia M. Gaudet, Angela George, Graham G. Giles, David E. Goldgar, Anna González‐Neira, Mervi Grip, Pascal Guénel, Qi Guo, Christopher A. Haiman, Niclas Håkansson, Ute Hamann, Patricia Harrington, Louise Hiller, Maartje J. Hooning, John L. Hopper, Anthony Howell, Chiun‐Sheng Huang, Guanmengqian Huang, David J. Hunter, Anna Jakubowska, Esther M. John, Rudolf Kaaks, Pooja Middha, Renske Keeman, Cari M. Kitahara, Linetta B. Koppert, Peter Kraft, Vessela N. Kristensen, Diether Lambrechts, Loı̈c Le Marchand, Flavio Lejbkowicz, Annika Lindblom, Jan Lubiński, Arto Mannermaa, Mehdi Manoochehri, Siranoush Manoukian, Sara Margolin, Marı́a Elena Martı́nez, Tabea Maurer, Dimitrios Mavroudis, Alfons Meindl, Roger L. Milne, Anna Marie Mulligan, Susan L. Neuhausen, Heli Nevanlinna, William G. Newman, Andrew F. Olshan, Janet E. Olson, Håkan Olsson
Abstract
Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.