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. 2017 Sep 28;13(9):e1006945. doi: 10.1371/journal.pgen.1006945

Up For A Challenge (U4C): Stimulating innovation in breast cancer genetic epidemiology

Leah E Mechanic 1,*,#, Sara Lindström 2,#, Kenneth M Daily 3, Solveig K Sieberts 3, Christopher I Amos 4, Huann-Sheng Chen 5, Nancy J Cox 6, Marina Dathe 1, Eric J Feuer 5, Michael J Guertin 7, Joshua Hoffman 8, Yunxian Liu 7, Jason H Moore 9, Chad L Myers 10, Marylyn D Ritchie 11,12, Joellen Schildkraut 13, Fredrick Schumacher 14, John S Witte 8, Wen Wang 10, Scott M Williams 14; U4C Challenge Participants; U4C Challenge Data Contributors, Elizabeth M Gillanders 1
PMCID: PMC5619686  PMID: 28957327

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 [1]. 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 [4]. 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 [6]. 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).

Fig 1. Stimulation of innovation in U4C.

Fig 1

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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 [6]. 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.

Table 1. Overview of U4C entries.

Team Name Entry Title dbGaP Accession Number for U4C-Designated Data Sets Used Other Data Sets Strategy Replication Strategy
Battalion Y Integrative Analysis of Diverse Genomic Data Identifies Novel Link Between Immunity Pathways and Inherited Breast Cancer Risk phs000147, phs000383, phs000517, phs000799, phs000812, phs000851, phs000912 GTEx, seeQTL, GenoSkyline, TCGA GWAS, meta-analysis, functional annotation, protein–protein interaction, tissue specific enrichment, somatic mutations, eQTL analysis Consistency across dbGaP data sets
CSMC_TEAM Identifying Novel Genes and Pathways for Breast Cancer with Semiparametric Modeling phs000147 Expression data from European Genome-Phenome Archive (EGAS00000000083), SNP gene annotation databases (KEGG, panther, cell map, BioCarta, etc.) Linked SNPs to genes and pathways and looked for enrichment of genes and pathways in breast cancer Compared dbGaP association results with gene expression and annotation databases
Gene Fishing Novel Genetic Variants of Breast Cancer—SNPs, Genes, and Gene-Gene Interactions phs000799 None Linked SNPs to genes and performed gene-based and gene–gene interaction tests Split data into testing and training
hapQTL Haplotype Associations in Shanghai Breast Cancer Study (Up For A Challenge) phs000799 None Examined haplotype associations and identified nearby genes Down sampling 100 times
MDACC Association of X-Chromosome Genetic Variants and Breast Cancer Risk phs000147, phs000383, phs000812, phs000851 snp-nexus.org; IPA Pathway Database Single SNPs and gene-based tests of association on X chromosome and pathway analysis (IPA) Consistency across studies and previous gene-expression publication
MDACC Prediction of Breast Cancer Status Using X- Chromosome Genetic Variants phs000147, phs000812, phs000851 snp-nexus.org; IPA Pathway Database Single SNPS and gene-based random forests followed by pathway analysis (IPA) Consistency across studies (used 1 study for training and others for testing)
muStat Breast Cancer muGWAS phs000147, phs000812 None u-statistics for multivariate data (neighboring SNPs) integrating knowledge about genetics, leveraging information content and study-specific genome-wide significance Consistency across studies (CGEMS, phs000147, and cohorts EPIC and PBCS of BPC3, phs000812) and consistency with published results from functional and expression data
snpsnbits Identify Breast Cancer Pathways Using Iscore Screening phs000147, phs000517, phs000799, phs000812, phs000851 NHGRI GWAS Catalogue, SNPedia Used SNPs from literature and identified in GWAS to identify pathways (or gene sets) associated with breast cancer. Interactions and new SNPs were identified using an iscore 2 data sets for training, 3 data sets for testing
Team Transcription Identification of Breast Cancer Associated Variants That Modulate Transcription Factor Binding NHGRI GWAS Cataloguea ENCODE, Roadmap Epigenomics, TCGA, GTEx Integrative genomics approach included identifying transcription factor motifs and association with breast cancer, SNPs in LD with top GWAS, SNPs within motifs and DNase I hypersensitivity sites, and eQTL analysis Consistency across multiple data sets and cell types
Team UCSF Team UCSF Up For A Challenge Submission phs000147, phs000383, phs000517, phs000799, phs000812, phs000851, phs000912 UK Biobank, GTEx GWAS, GWAGE using PrediXscan, meta-analysis and admixture mapping Replicated previous GWAS findings in the data sets, entry findings were replicated in UK biobank
U4C Maroons U Chicago Maroons Project for U4C phs000147, phs000383, phs000799, phs000812, phs000851 GAME-ON breast cancer GWAS summary statistics, Depression Genes and Network, and GTEx GWAGE using MetxScan and meta-analysis Consistency across data sets, replicated in GAME-ON breast cancer GWAS
UCLA Team Multi-Ethnic Meta-Analysis and Fine Mapping in Breast Cancer phs000383, phs000812, phs000851, phs000912 None Mixed-model association, meta-analysis, forestPMplot, and fine mapping (CAVIAR) Consistency across data sets
UMN-CSBIO Genetic Interactions in Breast Cancer phs000147, phs000812 Hapmap PhaseIII, Molecular Signatures Database (MSigDB v3.0) curated pathway database, Gene Annotation (hg19) Pathway interactions using annotated gene sets from MSigDB v3.0 Replication in second data set (phs000147)
UNC-BIAS U4C Breast Cancer Challenge phs000147, phs000517, phs000799,
phs000851		 None SNP and LD block-based (SNP set) association in subgroups and overall and meta-analysis Consistency across data sets
UNH STATS Data Mining of Genome-Wide Association Studies for New Hypotheses on the Possible Effect of Pathways on Breast Cancer Risk phs000799, phs000851 None Variable clustering, variable elimination and bootstrap forests For 2 different studies, divided each study into training and validation sets, i.e., cross validation within study and replication among 2 studies
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aThis team used results from GWAS data as reported in the NHGRI GWAS catalogue (https://www.ebi.ac.uk/gwas/).

Abbreviations: BPC3, Breast and Prostate Cancer Cohort Consortium; CAVIAR, CAusal Variants Identification in Associated Regions; CGEMS, Cancer Genetic Markers of Susceptibility; dbGaP, Database of Genotypes and Phenotypes; ENCODE, Encyclopedia of DNA Elements; EPIC, European Prospective Investigation into Cancer; eQTL, expression quantitative trait loci; GAME-ON, Genetic Associations and Mechanisms in Oncology; GTEx, Genotype-Tissue Expression project; GWAGE, genome-wide association of gene expression; GWAS, genome-wide association studies; IPA, Ingenuity Pathway Analysis; iscore, influence score; LD, linkage disequilibrium; MSigDB, Molecular Signatures Database; NHGRI, National Human Genome Research Institute; PBCS, Polish Breast Cancer Case-Control Study; SNP, single nucleotide polymorphism; TCGA, The Cancer Genome Atlas; U4C, Up For A Challenge.

Team UCSF performed a genome-wide association of gene expression [7]. Using the gene-based association method PrediXcan [8], 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) [9], to explicitly search for pathway-level interactions guided by annotated gene sets from the Molecular Signatures Database (MSigDB) [10]. 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 [11]. 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 [12], 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) [13].

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) [14]. Such streamlined access to data promoted the success of U4C and is completely in agreement with the PLOS Genetics editorial policy [15]. 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.

Acknowledgments

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.

Funding Statement

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.

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