Citation: Mechanic LE, Lindström S, Daily KM, Sieberts SK, Amos CI, Chen H-S, et al. (2017) Up For A Challenge (U4C): Stimulating innovation in breast cancer genetic epidemiology. PLoS Genet 13(9): e1006945. https://doi.org/10.1371/journal.pgen.1006945
Published: September 28, 2017
Copyright: © 2017 Mechanic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The U4C was funded by the National Institutes of Health (NIH) and the National Cancer Institute (NCI). This project was supported by contract HHSN261201200010I from the NCI Division of Cancer Control and Population Sciences to ICF Macro, which supported a task order 14DNBO0071 to Sage Bionetworks and subcontract (123833) to SL for this project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: SMW is a Section Editor of PLOS Genetics. LEM and EMG are employees of NCI. The authors have no other competing interests.
Breast cancer remains a major public health burden, with an estimated 252,710 new cases and 40,610 deaths among women in the United States in 2017 . To identify key genes and biological pathways potentially affecting disease risk, genome-wide association studies (GWAS) have been performed. At present, close to 100 common genetic variants have been associated with breast cancer [2–5]. However, these variants explain only a small proportion of the estimated genetic contribution to the risk of breast cancer . GWAS analyses often report only results from single variant analyses, without exploring the impact of potential combinations or the interplay between variants. Therefore, in 2015, the National Cancer Institute (NCI) launched a challenge to inspire novel cross-disciplinary approaches to more fully decipher the genomic basis of breast cancer, called "Up For A Challenge (U4C)—Stimulating Innovation in Breast Cancer Genetic Epidemiology.” The goal of U4C was to promote the development and/or implementation of innovative approaches to identify novel risk pathways—including new genes or combinations of genes, genetic variants, or sets of genomic features—involved in breast cancer susceptibility in order to generate new biological hypotheses . The challenge involved the formation of teams of scientists with diverse expertise to explore preexisting data sets, in an attempt to extract more useful information than typical GWAS analyses. U4C was also an explicit test of the usefulness of making larger data sets easily accessible to a broad community of researchers (Fig 1).
Existing genome-wide association studies (GWAS), representing thousands of cases and controls. Data were shared and accessed in a manner consistent with informed consent. Some of these data sets were made available for the first time in U4C. Teams competed for a prize to develop innovative analytical methods and make novel discoveries using these data sets.
Fourteen teams, including 88 researchers, submitted 15 U4C entries. U4C participants applied several innovative approaches to the analysis of existing breast cancer GWAS data sets, leading to multiple novel findings (Table 1). After careful considerations from a scientific evaluation panel, the reproduction of primary findings based on in-house reanalyses by using the methods described in the entry, and a review by National Institutes of Health (NIH) judges, 3 entries were selected as U4C prize winners . Team UCSF and UMN-CSBIO tied for the grand prize, Team Transcription was awarded second place, and U4C Maroons was the highest-scoring runner-up. Using their novel approaches, these teams discovered new genes by using a variety of analytical strategies, including imputing gene expression to perform gene-based association tests, network analyses, and the identification of variants that disrupt transcription factor (TF) binding associated with gene expression in breast tissue. The work of these 4 teams is now published as a series in PLOS Genetics to highlight the results of these truly innovative approaches to data reanalysis. Importantly, these papers passed the same rigorous editorial and external peer review evaluation that any submission to PLOS Genetics experiences.
Team UCSF performed a genome-wide association of gene expression . Using the gene-based association method PrediXcan , which integrates germline genotype and gene expression data, they identified novel associations between the following genes and breast cancer: ACAP1and LRRC25 (using whole-blood transcriptome data) and DHODH (using breast- and mammary-tissue transcriptome data).
Team UMN-CSBIO applied a novel computational method, developed initially to analyze yeast data, called BridGE (Bridging Gene Sets with Epistasis) , to explicitly search for pathway-level interactions guided by annotated gene sets from the Molecular Signatures Database (MSigDB) . By examining pathway interactions using 2 of the U4C-designated GWAS data sets, the team identified steroid hormone biosynthesis as a major hub of interactions and found that it was implicated as interacting with many pathways, including a gene set previously associated with acute myeloid leukemia (AML). These interactions would have been missed using traditional approaches.
Team Transcription employed an integrative genomics approach, exploring the hypothesis that many of the noncoding single nucleotide polymorphisms (SNPs) identified by GWAS alter TF binding sites and mediate the effect on disease by modulating TF binding and gene regulation . This team identified a SNP, rs4802200, in perfect linkage disequilibrium (LD) with a GWAS-significant SNP (rs3760982). rs4082200 is predicted to disrupt ZNF143 binding within a breast cancer-relevant regulatory element. This SNP is a strong expression quantitative trait loci (eQTL) of ZNF404 in breast tissue.
Team U4C Maroons also utilized a genome-wide gene expression approach, implemented in the MetaXcan , that leveraged GWAS summary statistics. This team identified TP53INP2 (tumor protein p53-inducible nuclear protein 2), associated with estrogen-receptor–negative breast cancer. The association was consistent across 5 of the U4C GWAS data sets and in different populations (European, African, and Asian ancestry) .
U4C demonstrated that making breast cancer genetic epidemiologic data more widely available can accelerate breast cancer genetic epidemiologic research without necessarily generating more data. This was accomplished in a relatively brief period because the competition only ran for 8.5 months. Clearly, the success of the U4C necessitated the enhanced sharing of data and a concerted effort by many investigators from a wide variety of academic disciplines. The formation of new collaborations was encouraged as part of the challenge evaluation criteria, and the success of this multidisciplinary approach is evident in the uniqueness and strength of the results. Several U4C entries embraced the spirit of the competition by critically challenging genetic epidemiology norms. Such reexamination of existing paradigms within a field is important to intellectual growth, but given the inherent conservative nature of most disciplines, this is not always welcomed. We hope that activities such as U4C and the willingness of PLOS Genetics to evaluate and publish these types of studies will encourage more innovation that will generate more novel and important findings.
Another key reason for the success is that 7 breast cancer GWAS data sets were gathered and made available for the challenge via controlled access from the NIH data repository Database of Genotypes and Phenotypes (dbGaP) . Such streamlined access to data promoted the success of U4C and is completely in agreement with the PLOS Genetics editorial policy . In the future, an improved informed consent mechanism that explicitly enables analysis and reanalysis of data sets by multiple research teams could enhance the ability to pursue multidisciplinary approaches. This broad access also promoted the exploration of data across several continental ancestries. This is in contrast to the history of the genetic epidemiology of breast cancer, in which most GWAS have focused on populations of European descent, even though a few recent studies have highlighted the need to further explore initial findings in non-European populations [16–21]. With this in mind, U4C provided access to new non-European data sets to promote cross-ethnic analyses, and 9 U4C entries performed comparisons using populations of different ethnic groups, with several entries exploring approaches using non-European populations. Although the transethnic analyses were more complete than most studies in the past, not all groups leveraged all the available data, perhaps due in part to smaller numbers of understudied populations in available data sets. This will require improvement.
Overall, U4C successfully encouraged diverse research teams to expand analytical strategies in the genetic epidemiology of breast cancer and identify novel biological hypotheses for breast cancer risk. The approach leveraged a wide distribution of existing data sets that was a key and cost-effective means to furthering our understanding of breast cancer risk. Lastly, the results from U4C provide proof of principle that open competition can free investigators to push traditional boundaries and unleash their intellectual creativity to generate new and important insights into the biology of breast cancer and beyond.
We would like to thank Mike Feolo of dbGaP, Charlisse Caga-Anan, Stefanie Nelson, Tiffany Bowen, and Sharna Tingle from the NCI extramural data access committee and Geoff Tobias from the NCI intramural data access committee for processing data access requests for the U4C. Moreover, we thank the U4C Evaluation Panel and the NIH Judges for this competition. In addition, we would like to thank Gonçalo Abecasis and Christian Fuchsberger for assistance with the University of Michigan Imputation Server. We acknowledge support from NCI Office of Communications and NCI DCCPS EGRP Communications Team.
The U4C Challenge Participants included: Jonathan Auerbach, Columbia University; Michael Bilow, University of California, Los Angeles; Grace Chan, UConn Health; Yu-Ting Chen, University of California, Los Angeles; Yuwei Cheng, Yale School of Medicine; Mete Civelek, University of Virginia; Kevin Claffey, UConn Health; Jason Cong, University of California, Los Angeles; Xiaojiang Cui, Cedars-Sinai Medical Center; Christina Dadurian, The Rockefeller University; Nan Deng, Cedars-Sinai Medical Center; Mikhail Dozmorov, Virginia Commonwealth University; Yinghui Duan, UConn Health; Nima Emami, University of California, San Francisco; Eleazar Eskin, University of California, Los Angeles; Zhenman Fang, University of California, Los Angeles; Lisa Gai, University of California, Los Angeles; Guimin Gao, University of Chicago; James Grady, UConn Health; Rebecca Graff, University of California, San Francisco; Yongtao Guan, Baylor College of Medicine; Michael J. Guertin, University of Virginia; Dexter Hadley, University of California, San Francisco; David Han, UConn Health; Christos Hatzis, Yale School of Medicine; Jessica Hoag, UConn Health; Joshua Hoffman, University of California, San Francisco; Farhad Hormozdiari, University of California, Los Angeles; Yayun Hsu, Columbia University; Yiming Hu, Yale School of Medicine; Donglei Hu, University of California, San Francisco; Jianhua Hu, MD Anderson Cancer Center; Scott Huntsman, University of California, San Francisco; Dezheng Huo, University of Chicago; Joseph G. Ibrahim, University of North Carolina at Chapel Hill; Hae Kyung Im, University of Chicago; Tingting Jiang, Yale School of Medicine; Chia-Ling Kuo, UConn Health; Carol Lange, University of Minnesota; Lancelote Leong, University of California, San Francisco; Xiaotong Li, Yale School of Medicine; Zhenqiu Liu, Cedars-Sinai Medical Center; Yunxian Liu, University of Virginia; Shaw-hwa Lo, Columbia University; Qiongshi Lu, Yale School of Medicine; Arunabha Majumdar, University of California, San Francisco; Serghei Mangul, University of California, Los Angeles; Chad Myers, University of Minnesota; Olufunmilayo Olopade, University of Chicago; Michael Passarelli, University of California, San Francisco; Steven Piantadosi, Cedars-Sinai Medical Center; Brandon Pierce, University of Chicago; Ryan Powles, Yale School of Medicine; Lajos Pusztai, Yale School of Medicine; Zhihua Qi, Baylor College of Medicine; Philip Ramsey, University of New Hampshire; Stephen S. Rich, University of Virginia; Sanjay Shete, MD Anderson Cancer Center; Lauren Staples, University of New Hampshire; Helen Swede, UConn Health; Caroline Tai, University of California, San Francisco; Rajesh Talluri, MD Anderson Cancer Center; Naoto Tada Ueno, MD Anderson Cancer Center; Qian Wang, Yale School of Medicine; Jian Wang, MD Anderson Cancer Center; Pei Wang, Mount Sinai; Wen Wang, University of Minnesota; Yue Wang, University of North Carolina at Chapel Hill; Jill Wegrzyn, UConn Health; John Witte, University of California, San Francisco; Knut M. Wittkowski, The Rockefeller University; Zhiyuan Xu, University of Minnesota; Taegyun Yang, University of California, Los Angeles; Noah Zaitlen, University of California, San Francisco; Heping Zhang, Yale University; Jingwen Zhang, University of North Carolina at Chapel Hill; Hongyu Zhao, Yale School of Medicine; Tian Zheng, Columbia University; Quan Zhou, Baylor College of Medicine; Peipei Zhou, University of California, Los Angeles; Fan Zhou, University of North Carolina at Chapel Hill; Hongtu Zhu, University of North Carolina at Chapel Hill; Ziliang Zhu, University of North Carolina at Chapel Hill; Elad Ziv; University of California, San Francisco.
The U4C Challenge Data Contributors included: Clement Adebamowo, University of Maryland Baltimore, Christine Ambrosone, Roswell Park Cancer Institute; Stefan Ambs, National Cancer Institute; Leslie Bernstein, Beckman Research Institute/City of Hope; Federico Canzian, German Cancer Research Center; Stephen Chanock, National Cancer Institute; Susan M.Domchek, University of Pennsylvania; Adeyinka G.Falusi, University of Ibadan; Yu-Tang Gao, Shanghai Cancer Institute; Susan Gapstur, American Cancer Society; Montserrat Garcia-Closas, National Cancer Institute; Christopher Haiman, University Of Southern California; Anselm J.M.Hennis, State University of New York at Stony Brook; Jennifer Hu, University Of Miami School of Medicine; Dezheng Huo, University of Chicago; David Hunter, Harvard School of Public Health; Peter Kraft, Harvard School of Public Health; Sara Lindstroem, University Of Washington; Esther John, Cancer Prevention Institute of California; Maria Cristina Leske, State University of New York at Stony Brook; Katherine L.Nathanson, University of Pennsylvania; Barbara Nemesure, State University New York Stony Brook; Temidayo O.Ogundiran, University of Ibadan; Olufunmilayo I. Olopade, University of Chicago; Andrew Olshan, University of North Carolina Chapel Hill; Timothy R.Rebbeck, Dana-Farber Cancer Institute; Elio Riboli, The Imperial College of Science, Technology and Medicine; Suh-Yuh Wu, State University of New York at Stony Brook; Wei Zheng, Vanderbilt University Medical Center; Yonglan Zheng, University of Chicago; Regina Ziegler, National Cancer Institute; Elad Ziv, University of California, San Francisco.
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