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).

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.

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.