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Li JL, McClellan JC, Zhang H, Gao G, Huo D. Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer. J Natl Cancer Inst 2024; 116:1105-1115. [PMID: 38400758 PMCID: PMC11223833 DOI: 10.1093/jnci/djae041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/25/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown. METHODS We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium. RESULTS Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC. CONCLUSION Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.
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Affiliation(s)
- James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Julian C McClellan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, IL, USA
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2
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McClellan JC, Li JL, Gao G, Huo D. Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status. Breast Cancer Res 2024; 26:51. [PMID: 38515142 PMCID: PMC10958972 DOI: 10.1186/s13058-024-01809-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/14/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Although several transcriptome-wide association studies (TWASs) have been performed to identify genes associated with overall breast cancer (BC) risk, only a few TWAS have explored the differences in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer. Additionally, these studies were based on gene expression prediction models trained primarily in breast tissue, and they did not account for alternative splicing of genes. METHODS In this study, we utilized two approaches to perform multi-tissue TWASs of breast cancer by ER subtype: (1) an expression-based TWAS that combined TWAS signals for each gene across multiple tissues and (2) a splicing-based TWAS that combined TWAS signals of all excised introns for each gene across tissues. To perform this TWAS, we utilized summary statistics for ER + BC from the Breast Cancer Association Consortium (BCAC) and for ER- BC from a meta-analysis of BCAC and the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). RESULTS In total, we identified 230 genes in 86 loci that were associated with ER + BC and 66 genes in 29 loci that were associated with ER- BC at a Bonferroni threshold of significance. Of these genes, 2 genes associated with ER + BC at the 1q21.1 locus were located at least 1 Mb from published GWAS hits. For several well-studied tumor suppressor genes such as TP53 and CHEK2 which have historically been thought to impact BC risk through rare, penetrant mutations, we discovered that common variants, which modulate gene expression, may additionally contribute to ER + or ER- etiology. CONCLUSIONS Our study comprehensively examined how differences in common variation contribute to molecular differences between ER + and ER- BC and introduces a novel, splicing-based framework that can be used in future TWAS studies.
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Affiliation(s)
- Julian C McClellan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA.
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA.
- Section of Hematology & Oncology, Department of Medicine, University of Chicago, Chicago, IL, 60637, USA.
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Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.09.566218. [PMID: 38014246 PMCID: PMC10680675 DOI: 10.1101/2023.11.09.566218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.
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Affiliation(s)
- S. Taylor Head
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Felipe Dezem
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Andrei Todor
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jasmine Plummer
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Simon Gayther
- Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Siddhartha Kar
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Joellen Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Michael P. Epstein
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
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Asgari Y, Sugier PE, Baghfalaki T, Lucotte E, Karimi M, Sedki M, Ngo A, Liquet B, Truong T. GCPBayes pipeline: a tool for exploring pleiotropy at the gene level. NAR Genom Bioinform 2023; 5:lqad065. [PMID: 37416786 PMCID: PMC10320750 DOI: 10.1093/nargab/lqad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/16/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.
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Affiliation(s)
- Yazdan Asgari
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Pierre-Emmanuel Sugier
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
| | | | - Elise Lucotte
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mojgan Karimi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Mohammed Sedki
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Psychiatrie du développement et trajectoires, 94807 Villejuif, France
| | - Amélie Ngo
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
| | - Benoit Liquet
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64000 Pau, France
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team Exposome and Heredity, 94807 Villejuif, France
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Lindström S, Wang L, Feng H, Majumdar A, Huo S, Macdonald J, Harrison T, Turman C, Chen H, Mancuso N, Bammler T, Gallinger S, Gruber SB, Gunter MJ, Le Marchand L, Moreno V, Offit K, De Vivo I, O’Mara TA, Spurdle AB, Tomlinson I, Fitzgerald R, Gharahkhani P, Gockel I, Jankowski J, Macgregor S, Schumacher J, Barnholtz-Sloan J, Bondy ML, Houlston RS, Jenkins RB, Melin B, Wrensch M, Brennan P, Christiani DC, Johansson M, Mckay J, Aldrich MC, Amos CI, Landi MT, Tardon A, Bishop DT, Demenais F, Goldstein AM, Iles MM, Kanetsky PA, Law MH, Amundadottir LT, Stolzenberg-Solomon R, Wolpin BM, Klein A, Petersen G, Risch H, Chanock SJ, Purdue MP, Scelo G, Pharoah P, Kar S, Hung RJ, Pasaniuc B, Kraft P. Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions. J Natl Cancer Inst 2023; 115:712-732. [PMID: 36929942 PMCID: PMC10248849 DOI: 10.1093/jnci/djad043] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/07/2022] [Accepted: 02/14/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. METHODS We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. RESULTS We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. CONCLUSIONS Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.
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Affiliation(s)
- Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Helian Feng
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, Telangana, India
| | - Sijia Huo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - James Macdonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Tabitha Harrison
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hongjie Chen
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | | | - Steve Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Marc J Gunter
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | | | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Radcliffe Institute, Cambridge, MA, USA
| | - Tracy A O’Mara
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian Tomlinson
- Cancer Research Centre, The University of Edinburgh, Edinburgh, UK
| | | | - Rebecca Fitzgerald
- MRC Cancer Unit, Hutchison-MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Janusz Jankowski
- Institute for Clinical Trials, University College London, Holborn, UK
- University of the South Pacific, Suva, Fiji
| | - Stuart Macgregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Jill Barnholtz-Sloan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Trans-Divisional Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melissa L Bondy
- Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Paul Brennan
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - David C Christiani
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - James Mckay
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adonina Tardon
- University Institute of Oncology of the Principality of Asturias (IUOPA), University of Oviedo and Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Oviedo, Spain
| | | | - D Timothy Bishop
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Florence Demenais
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Paris, France
| | - Alisa M Goldstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark M Iles
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia
| | | | - Laufey T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rachael Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | | | - Alison Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gloria Petersen
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN, USA
| | - Harvey Risch
- Yale School of Public Health, Chronic Disease Epidemiology, New Haven, CT, USA
| | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark P Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ghislaine Scelo
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbuaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Punzón-Jiménez P, Lago V, Domingo S, Simón C, Mas A. Molecular Management of High-Grade Serous Ovarian Carcinoma. Int J Mol Sci 2022; 23:13777. [PMID: 36430255 PMCID: PMC9692799 DOI: 10.3390/ijms232213777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.
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Affiliation(s)
- Paula Punzón-Jiménez
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
| | - Victor Lago
- Department of Gynecologic Oncology, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain
- Department of Obstetrics and Gynecology, CEU Cardenal Herrera University, 46115 Valencia, Spain
| | - Santiago Domingo
- Department of Gynecologic Oncology, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Universidad de Valencia, 46010 Valencia, Spain
| | - Carlos Simón
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Universidad de Valencia, 46010 Valencia, Spain
- Department of Pediatrics, Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard University, Boston, MA 02215, USA
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Aymara Mas
- Carlos Simon Foundation, INCLIVA Health Research Institute, 46010 Valencia, Spain
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7
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Nameki R, Shetty A, Dareng E, Tyrer J, Lin X, Pharoah P, Corona RI, Kar S, Lawrenson K. chromMAGMA: regulatory element-centric interrogation of risk variants. Life Sci Alliance 2022; 5:e202201446. [PMID: 35777959 PMCID: PMC9251535 DOI: 10.26508/lsa.202201446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/24/2022] Open
Abstract
Candidate causal risk variants from genome-wide association studies reside almost exclusively in noncoding regions of the genome and innovative approaches are necessary to understand their biological function. Multi-marker analysis of genomic annotation (MAGMA) is a widely used program that nominates candidate risk genes by mapping single-nucleotide polymorphism summary statistics from genome-wide association studies to gene bodies. We augmented MAGMA to create chromatin-MAGMA (chromMAGMA), a method to nominate candidate risk genes based on the presence of risk variants within noncoding regulatory elements (REs). We applied chromMAGMA to a genetic susceptibility dataset for epithelial ovarian cancer (EOC), a rare gynecologic malignancy characterized by high mortality. This identified 155 unique candidate EOC risk genes across five EOC histotypes; 83% (105/127) of high-grade serous ovarian cancer risk genes had not previously been implicated in this EOC histotype. Risk genes nominated by chromMAGMA converged on mRNA splicing and transcriptional dysregulation pathways. chromMAGMA is a pipeline that nominates candidate risk genes through a gene regulation-focused approach and helps interpret the biological mechanism of noncoding risk variants for complex diseases.
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Affiliation(s)
- Robbin Nameki
- Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anamay Shetty
- Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Eileen Dareng
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Xianzhi Lin
- Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Rosario I Corona
- Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Siddhartha Kar
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Lawrenson
- Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Cancer Prevention and Control Program, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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8
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Buttarelli M, Ciucci A, Palluzzi F, Raspaglio G, Marchetti C, Perrone E, Minucci A, Giacò L, Fagotti A, Scambia G, Gallo D. Identification of a novel gene signature predicting response to first-line chemotherapy in BRCA wild-type high-grade serous ovarian cancer patients. J Exp Clin Cancer Res 2022; 41:50. [PMID: 35120576 PMCID: PMC8815250 DOI: 10.1186/s13046-022-02265-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 01/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management. METHODS To identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance. RESULTS RNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance. CONCLUSIONS Using machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.
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Affiliation(s)
- Marianna Buttarelli
- Unità di Medicina Traslazionale per la Salute della Donna e del Bambino, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
| | - Alessandra Ciucci
- Unità di Medicina Traslazionale per la Salute della Donna e del Bambino, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
| | - Fernando Palluzzi
- Bioinformatics Facility Core Research, Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Giuseppina Raspaglio
- Unità di Medicina Traslazionale per la Salute della Donna e del Bambino, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
| | - Claudia Marchetti
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Emanuele Perrone
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Angelo Minucci
- Molecular and Genomic Diagnostics Unit (MGDUnit), Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Luciano Giacò
- Bioinformatics Facility Core Research, Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Anna Fagotti
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Giovanni Scambia
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Daniela Gallo
- Unità di Medicina Traslazionale per la Salute della Donna e del Bambino, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy.
- Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy.
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Parrish RL, Gibson GC, Epstein MP, Yang J. TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8. HGG ADVANCES 2022; 3:100068. [PMID: 35047855 PMCID: PMC8756507 DOI: 10.1016/j.xhgg.2021.100068] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/01/2021] [Indexed: 01/12/2023] Open
Abstract
Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.
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Affiliation(s)
- Randy L. Parrish
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Greg C. Gibson
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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