A network analysis to identify mediators of germline-driven differences in breast cancer prognosis
Journal
Nature Communications
Journal Volume
11
Journal Issue
1
Pages
312
Date Issued
2020
Author(s)
Escala-Garcia M.
Abraham J.
Andrulis I.L.
Anton-Culver H.
Arndt V.
Ashworth A.
Auer P.L.
Auvinen P.
Beckmann M.W.
Beesley J.
Behrens S.
Benitez J.
Bermisheva M.
Blomqvist C.
Blot W.
Bogdanova N.V.
Bojesen S.E.
Bolla M.K.
B?rresen-Dale A.-L.
Brauch H.
Brenner H.
Brucker S.Y.
Burwinkel B.
Caldas C.
Canzian F.
Chang-Claude J.
Chanock S.J.
Chin S.-F.
Clarke C.L.
Couch F.J.
Cox A.
Cross S.S.
Czene K.
Daly M.B.
Dennis J.
Devilee P.
Dunn J.A.
Dunning A.M.
Dwek M.
Earl H.M.
Eccles D.M.
Eliassen A.H.
Ellberg C.
Evans D.G.
Fasching P.A.
Figueroa J.
Flyger H.
Gago-Dominguez M.
Gapstur S.M.
Garc?a-Closas M.
Garc?a-S?enz J.A.
Gaudet M.M.
George A.
Giles G.G.
Goldgar D.E.
Gonz?lez-Neira A.
Grip M.
Gu?nel P.
Guo Q.
Haiman C.A.
H?kansson N.
Hamann U.
Harrington P.A.
Hiller L.
Hooning M.J.
Hopper J.L.
Howell A.
Huang G.
Hunter D.J.
Jakubowska A.
John E.M.
Kaaks R.
Kapoor P.M.
Keeman R.
Kitahara C.M.
Koppert L.B.
Kraft P.
Kristensen V.N.
Lambrechts D.
Le Marchand L.
Lejbkowicz F.
Lindblom A.
Lubi?ski J.
Mannermaa A.
Manoochehri M.
Manoukian S.
Margolin S.
Martinez M.E.
Maurer T.
Mavroudis D.
Meindl A.
Milne R.L.
Mulligan A.M.
Neuhausen S.L.
Nevanlinna H.
Newman W.G.
Olshan A.F.
Olson J.E.
Olsson H.
Orr N.
Peterlongo P.
Petridis C.
Prentice R.L.
Presneau N.
Punie K.
Ramachandran D.
Rennert G.
Romero A.
Sachchithananthan M.
Saloustros E.
Sawyer E.J.
Schmutzler R.K.
Schwentner L.
Scott C.
Simard J.
Sohn C.
Southey M.C.
Swerdlow A.J.
Tamimi R.M.
Tapper W.J.
Teixeira M.R.
Terry M.B.
Thorne H.
Tollenaar R.A.E.M.
Tomlinson I.
Troester M.A.
Truong T.
Turnbull C.
Vachon C.M.
van der Kolk L.E.
Wang Q.
Winqvist R.
Wolk A.
Yang X.R.
Ziogas A.
Pharoah P.D.P.
Hall P.
Wessels L.F.A.
Chenevix-Trench G.
Bader G.D.
D?rk T.
Easton D.F.
Canisius S.
Schmidt M.K.
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. ? 2020, The Author(s).
SDGs
Other Subjects
cancer; data set; enzyme; enzyme activity; gene; heritability; network analysis; survival; angiogenesis; apoptosis; Article; breast cancer; cancer prognosis; cancer specific survival; cancer survival; cell cycle regulation; circadian rhythm; estrogen receptor negative breast cancer; estrogen receptor positive breast cancer; female; gene frequency; gene linkage disequilibrium; genetic association; genetic variability; genome-wide association study; genotype; germline mutation; germline related prognostic module; human; major clinical study; prognostic assessment; single nucleotide polymorphism; biology; breast tumor; gene regulatory network; genetic variation; genetics; genome-wide association study; germ cell; prognosis; signal transduction; estrogen receptor; G protein alpha 16; GNA11 protein, human; guanine nucleotide binding protein alpha subunit; Apoptosis; Breast Neoplasms; Circadian Clocks; Computational Biology; Female; Gene Regulatory Networks; Genetic Variation; Genome-Wide Association Study; Genotype; Germ Cells; GTP-Binding Protein alpha Subunits; GTP-Binding Protein alpha Subunits, Gq-G11; Humans; Prognosis; Receptors, Estrogen; Signal Transduction
Publisher
Nature Research
Type
journal article