Advertisement
  • Loading metrics

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

  • Leah E. Mechanic ,

    Contributed equally to this work with: Leah E. Mechanic, Sara Lindström

    mechanil@mail.nih.gov

    Affiliation Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Sara Lindström ,

    Contributed equally to this work with: Leah E. Mechanic, Sara Lindström

    Affiliation Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, United States of America

  • Kenneth M. Daily,

    Affiliation Sage Bionetworks, Seattle, Washington, United States of America

  • Solveig K. Sieberts,

    Affiliation Sage Bionetworks, Seattle, Washington, United States of America

  • Christopher I. Amos,

    Affiliation Department of Biomedical Data Sciences, Dartmouth College, Lebanon, New Hampshire, United States of America

  • Huann-Sheng Chen,

    Affiliation Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Nancy J. Cox,

    Affiliation Division of Genetic Medicine, Vanderbilt University, Nashville, Tennessee, United States of America

  • Marina Dathe,

    Affiliation Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Eric J. Feuer,

    Affiliation Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Michael J. Guertin,

    Affiliation Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America

  • Joshua Hoffman,

    Affiliation Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America

  • Yunxian Liu,

    Affiliation Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America

  • Jason H. Moore,

    Affiliation Division of Informatics, Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Chad L. Myers,

    Affiliation Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota, United States of America

  • Marylyn D. Ritchie,

    Affiliations Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Pennsylvania State University, University Park, Pennsylvania, United States of America, Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America

  • Joellen Schildkraut,

    Affiliation Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America

  • Fredrick Schumacher,

    Affiliation Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America

  • John S. Witte,

    Affiliation Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America

  • Wen Wang,

    Affiliation Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota, United States of America

  • Scott M. Williams,

    Affiliation Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America

  • U4C Challenge Participants ,

    Listing of the U4C Challenge Participants and U4C Challenge Data Contributors is provided in the Acknowledgments.

  • U4C Challenge Data Contributors ,

    Listing of the U4C Challenge Participants and U4C Challenge Data Contributors is provided in the Acknowledgments.

  •  [ ... ],
  • Elizabeth M. Gillanders

    Affiliation Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • [ view all ]
  • [ view less ]

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

  • Leah E. Mechanic, 
  • Sara Lindström, 
  • Kenneth M. Daily, 
  • Solveig K. Sieberts, 
  • Christopher I. Amos, 
  • Huann-Sheng Chen, 
  • Nancy J. Cox, 
  • Marina Dathe, 
  • Eric J. Feuer, 
  • Michael J. Guertin
PLOS
x

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 [25]. 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).

thumbnail
Fig 1. Stimulation of innovation in U4C.

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.

https://doi.org/10.1371/journal.pgen.1006945.g001

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 [1621]. 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.

References

  1. 1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA: a cancer journal for clinicians. 2017;67(1):7–30. Epub 2017/01/06. pmid:28055103
  2. 2. Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, et al. The OncoArray Consortium: a Network for Understanding the Genetic Architecture of Common Cancers. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2016. Epub 2016/10/05. pmid:27697780
  3. 3. Han MR, Long J, Choi JY, Low SK, Kweon SS, Zheng Y, et al. Genome-wide association study in East Asians identifies two novel breast cancer susceptibility loci. Human molecular genetics. 2016. Epub 2016/06/30. pmid:27354352
  4. 4. Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nature genetics. 2015;47(4):373–80. Epub 2015/03/10. pmid:25751625
  5. 5. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. American journal of human genetics. 2012;90(1):7–24. Epub 2012/01/17. pmid:22243964
  6. 6. Mechanic LE, Lindstrom S, Gillanders E. NCI Up for a Challenge (U4C): Stimulating Innovation in Breast Cancer Genetic Epidemiology 2015 [3/23/2017]. Available from: www.synapse.org/upforachallenge.
  7. 7. Hoffman JD, Graff RE, Emami NC, Tai CG, Passarelli MN, Hu D, et al. Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk. PLoS Genet. 2017;13(3):e1006690. pmid:28362817
  8. 8. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nature genetics. 2015;47(9):1091–8. Epub 2015/08/11. pmid:26258848
  9. 9. Wang W, Xu Z, Constanzo M, Boone C, Lange CA, Myers CL. Pathway-based Discovery of Genetic Interactions in Breast Cancer. PLoS Genet. 2017;e1006973.
  10. 10. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102(43):15545–50. pmid:16199517
  11. 11. Liu Y, Walavalkar NM, Dozmorov MG, Rich SS, Civelek M, Guertin MJ. Identification of Breast Cancer Associated Variants That Modulate Transcription Factor Binding. PLoS Genet. 2017;e1006761.
  12. 12. Barbeira A, Dickinson SP, Torres JM, Bonazzola R, Zheng J, Torstenson ES, et al. Integrating tissue specific mechanisms into GWAS summary results. bioRxiv. 2017. 10.1101/045260
  13. 13. Gao G, Pierce BL, Olopade OI, Im HK, Huo D. Trans-ethnic Predicted Expression Genome-wide Association Analysis Identifies a Gene for Estrogen Receptor-negative Breast Cancer. PLoS Genet. 2017;e1006727.
  14. 14. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nature genetics. 2007;39(10):1181–6. Epub 2007/09/28. pmid:17898773
  15. 15. Barsh GS, Cooper GM, Copenhaver GP, Gibson G, McCarthy MI, Tang H, et al. PLOS Genetics Data Sharing Policy: In Pursuit of Functional Utility. PLoS Genet. 2015;11(12):e1005716. pmid:26655768
  16. 16. Cai Q, Long J, Lu W, Qu S, Wen W, Kang D, et al. Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium. Human molecular genetics. 2011;20(24):4991–9. Epub 2011/09/13. pmid:21908515
  17. 17. Feng Y, Stram DO, Rhie SK, Millikan RC, Ambrosone CB, John EM, et al. A comprehensive examination of breast cancer risk loci in African American women. Human molecular genetics. 2014;23(20):5518–26. Epub 2014/05/24. pmid:24852375
  18. 18. Huo D, Feng Y, Haddad S, Zheng Y, Yao S, Han YJ, et al. Genome-wide Association Studies in Women of African Ancestry Identified 3q26.21 as a Novel Susceptibility Locus for Estrogen Receptor Negative Breast Cancer. Human molecular genetics. 2016. Epub 2016/09/07. pmid:28171663
  19. 19. Siddiq A, Couch FJ, Chen GK, Lindstrom S, Eccles D, Millikan RC, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Human molecular genetics. 2012;21(24):5373–84. Epub 2012/09/15. pmid:22976474
  20. 20. Zaitlen N, Pasaniuc B, Gur T, Ziv E, Halperin E. Leveraging genetic variability across populations for the identification of causal variants. American journal of human genetics. 2010;86(1):23–33. Epub 2010/01/21. pmid:20085711
  21. 21. Fejerman L, Ahmadiyeh N, Hu D, Huntsman S, Beckman KB, Caswell JL, et al. Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25. Nature communications. 2014;5:5260. Epub 2014/10/21. pmid:25327703