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A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

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.

SUBMITTER: Escala-Garcia M

PROVIDER: S-EPMC6965101 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Escala-Garcia Maria M Abraham Jean J Andrulis Irene L IL Anton-Culver Hoda H Arndt Volker V Ashworth Alan A Auer Paul L PL Auvinen Päivi P Beckmann Matthias W MW Beesley Jonathan J Behrens Sabine S Benitez Javier J Bermisheva Marina M Blomqvist Carl C Blot William W Bogdanova Natalia V NV Bojesen Stig E SE Bolla Manjeet K MK Børresen-Dale Anne-Lise AL Brauch Hiltrud H Brenner Hermann H Brucker Sara Y SY Burwinkel Barbara B Caldas Carlos C Canzian Federico F Chang-Claude Jenny J Chanock Stephen J SJ Chin Suet-Feung SF Clarke Christine L CL Couch Fergus J FJ Cox Angela A Cross Simon S SS Czene Kamila K Daly Mary B MB Dennis Joe J Devilee Peter P Dunn Janet A JA Dunning Alison M AM Dwek Miriam M Earl Helena M HM Eccles Diana M DM Eliassen A Heather AH Ellberg Carolina C Evans D Gareth DG Fasching Peter A PA Figueroa Jonine J Flyger Henrik H Gago-Dominguez Manuela M Gapstur Susan M SM García-Closas Montserrat M García-Sáenz José A JA Gaudet Mia M MM George Angela A Giles Graham G GG Goldgar David E DE González-Neira Anna A Grip Mervi M Guénel Pascal P Guo Qi Q Haiman Christopher A CA Håkansson Niclas N Hamann Ute U Harrington Patricia A PA Hiller Louise L Hooning Maartje J MJ Hopper John L JL Howell Anthony A Huang Chiun-Sheng CS Huang Guanmengqian G Hunter David J DJ Jakubowska Anna A John Esther M EM Kaaks Rudolf R Kapoor Pooja Middha PM Keeman Renske R Kitahara Cari M CM Koppert Linetta B LB Kraft Peter P Kristensen Vessela N VN Lambrechts Diether D Le Marchand Loic L Lejbkowicz Flavio F Lindblom Annika A Lubiński Jan J Mannermaa Arto A Manoochehri Mehdi M Manoukian Siranoush S Margolin Sara S Martinez Maria Elena ME Maurer Tabea T Mavroudis Dimitrios D Meindl Alfons A Milne Roger L RL Mulligan Anna Marie AM Neuhausen Susan L SL Nevanlinna Heli H Newman William G WG Olshan Andrew F AF Olson Janet E JE Olsson Håkan H Orr Nick N Peterlongo Paolo P Petridis Christos C Prentice Ross L RL Presneau Nadege N Punie Kevin K Ramachandran Dhanya D Rennert Gad G Romero Atocha A Sachchithananthan Mythily M Saloustros Emmanouil E Sawyer Elinor J EJ Schmutzler Rita K RK Schwentner Lukas L Scott Christopher C Simard Jacques J Sohn Christof C Southey Melissa C MC Swerdlow Anthony J AJ Tamimi Rulla M RM Tapper William J WJ Teixeira Manuel R MR Terry Mary Beth MB Thorne Heather H Tollenaar Rob A E M RAEM Tomlinson Ian I Troester Melissa A MA Truong Thérèse T Turnbull Clare C Vachon Celine M CM van der Kolk Lizet E LE Wang Qin Q Winqvist Robert R Wolk Alicja A Yang Xiaohong R XR Ziogas Argyrios A Pharoah Paul D P PDP Hall Per P Wessels Lodewyk F A LFA Chenevix-Trench Georgia G Bader Gary D GD Dörk Thilo T Easton Douglas F DF Canisius Sander S Schmidt Marjanka K MK

Nature communications 20200116 1

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 effec ...[more]

PMID: 31949161

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Project description:Objective:This study aims to identify effective gene networks and biomarkers to predict response and prognosis for HER2-negative breast cancer patients who received sequential taxane-anthracycline neoadjuvant chemotherapy. Materials and Methods:Transcriptome data of training dataset including 310 HER2-negative breast cancer who received taxane-anthracycline treatment and an independent validation set with 198 samples were analyzed by weighted gene co-expression network analysis (WGCNA) approach in R language. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed for the selected genes. Module-clinical trait relationships were analyzed to explore the genes and pathways that associated with clinicopathological parameters. Log-rank tests and COX regression were used to identify the prognosis-related genes. Results:We found a significant correlation of an expression module with distant relapse-free survival (HR = 0.213, 95% CI [0.131-0.347], P = 4.80E-9). This blue module contained genes enriched in biological process of hormone levels regulation, reproductive system, response to estradiol, cell growth and mammary gland development as well as pathways including estrogen, apelin, cAMP, the PPAR signaling pathway and fatty acid metabolism. From this module, we further screened and validated six hub genes (CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2), the expression of which were significantly associated with both better chemotherapeutic response and favorable survival for BC patients. Conclusion:We used WGCNA approach to reveal a gene network that regulate HER2-negative breast cancer treatment with taxane-anthracycline neoadjuvant chemotherapy, which enriched in pathways of estrogen signaling, apelin signaling, cAMP signaling, the PPAR signaling pathway and fatty acid metabolism. In addition, genes of CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2 might serve as novel biomarkers predicting chemotherapeutic response and prognosis for HER2-negative breast cancer.

Dataset Information

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Publications

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

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