A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.
Escala-Garcia M., Abraham J., Andrulis IL., Anton-Culver H., Arndt V., Ashworth A., Auer PL., Auvinen P., Beckmann MW., Beesley J., Behrens S., Benitez J., Bermisheva M., Blomqvist C., Blot W., Bogdanova NV., Bojesen SE., Bolla MK., Børresen-Dale A-L., Brauch H., Brenner H., Brucker SY., Burwinkel B., Caldas C., Canzian F., Chang-Claude J., Chanock SJ., Chin S-F., Clarke CL., Couch FJ., Cox A., Cross SS., Czene K., Daly MB., Dennis J., Devilee P., Dunn JA., Dunning AM., Dwek M., Earl HM., Eccles DM., Eliassen AH., Ellberg C., Evans DG., Fasching PA., Figueroa J., Flyger H., Gago-Dominguez M., Gapstur SM., García-Closas M., García-Sáenz JA., Gaudet MM., George A., Giles GG., Goldgar DE., González-Neira A., Grip M., Guénel P., Guo Q., Haiman CA., Håkansson N., Hamann U., Harrington PA., Hiller L., Hooning MJ., Hopper JL., Howell A., Huang C-S., Huang G., Hunter DJ., Jakubowska A., John EM., Kaaks R., Kapoor PM., Keeman R., Kitahara CM., Koppert LB., Kraft P., Kristensen VN., Lambrechts D., Le Marchand L., Lejbkowicz F., Lindblom A., Lubiński J., Mannermaa A., Manoochehri M., Manoukian S., Margolin S., Martinez ME., Maurer T., Mavroudis D., Meindl A., Milne RL., Mulligan AM., Neuhausen SL., Nevanlinna H., Newman WG., Olshan AF., Olson JE., Olsson H., Orr N., Peterlongo P., Petridis C., Prentice RL., Presneau N., Punie K., Ramachandran D., Rennert G., Romero A., Sachchithananthan M., Saloustros E., Sawyer EJ., Schmutzler RK., Schwentner L., Scott C., Simard J., Sohn C., Southey MC., Swerdlow AJ., Tamimi RM., Tapper WJ., Teixeira MR., Terry MB., Thorne H., Tollenaar RAEM., Tomlinson I., Troester MA., Truong T., Turnbull C., Vachon CM., van der Kolk LE., Wang Q., Winqvist R., Wolk A., Yang XR., Ziogas A., Pharoah PDP., Hall P., Wessels LFA., Chenevix-Trench G., Bader GD., Dörk T., Easton DF., Canisius S., Schmidt MK.
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.