AACR Project GENIE: Powering Precision Medicine through an International Consortium

Cancer Discovery - Tập 7 Số 8 - Trang 818-831 - 2017
Fabrice André, Mónica Arnedos, Alexander S. Baras, José Baselga, Philippe L. Bédard, Michael F. Berger, Mariska Bierkens, Fabien Calvo, Ethan Cerami, Debyani Chakravarty, Kristen K. Dang, Nancy E. Davidson, Catherine Del Vecchio Fitz, Semih Doğan, Raymond N. DuBois, Matthew D. Ducar, P. Andrew Futreal, Jianjiong Gao, Francisco Moacir Pinheiro Garcia, Stuart M. Gardos, Christopher D. Gocke, Benjamin Groß, Justin Guinney, Zachary Heins, Stephanie Hintzen, Hugo M. Horlings, Jan Hudeček, David M. Hyman, Suzanne Kamel‐Reid, Cyriac Kandoth, Walter Kinyua, Priti Kumari, Ritika Kundra, Marc Ladanyi, Céline Lefèbvre, Michele L. Lenoue-Newton, Eva M. Lepisto, Mia A. Levy, Neal I. Lindeman, James Lindsay, David Liu, Zhibin Lu, Laura E. MacConaill, Ian Maurer, David S. Maxwell, Gerrit A. Meijer, Funda Meric‐Bernstam, Christine Micheel, Clinton Miller, Gordon B. Mills, Nathanael D. Moore, Petra M. Nederlof, Larsson Omberg, John A. Orechia, Ben Ho Park, Trevor J. Pugh, Brendan Reardon, Barrett J. Rollins, Mark J. Routbort, Charles L. Sawyers, Deborah Schrag, Nikolaus Schultz, Kenna Shaw, Priyanka Shivdasani, Lillian L. Siu, David B. Solit, Gabe S. Sonke, Jean‐Charles Soria, Parin Sripakdeevong, Natalie Stickle, Thomas Stricker, Shawn M. Sweeney, Barry S. Taylor, Jelle J. ten Hoeve, Stacy B. Thomas, Eliezer M. Van Allen, Laura J. van‘t Veer, Tony van de Velde, Harm van Tinteren, Victor E. Velculescu, Carl Virtanen, Emile E. Voest, Lucy Lu Wang, Chetna Wathoo, Stuart Watt, Celeste Yu, Thomas Yu, Emily Yu, Ahmet Zehir, Hongxin Zhang

Tóm tắt

Abstract The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine. Significance: The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy. Cancer Discov; 7(8); 818–31. ©2017 AACR. See related commentary by Litchfield et al., p. 796. This article is highlighted in the In This Issue feature, p. 783

Từ khóa


Tài liệu tham khảo

Grossman, 2016, Toward a shared vision for cancer genomic data, N Engl J Med, 375, 1109, 10.1056/NEJMp1607591

Garraway, 2013, Precision oncology: an overview, J Clin Oncol, 31, 1803, 10.1200/JCO.2013.49.4799

Tannock, 2016, Limits to personalized cancer medicine, N Engl J Med, 375, 1289, 10.1056/NEJMsb1607705

Prasad, 2016, Perspective: The precision-oncology illusion, Nature, 537, S63, 10.1038/537S63a

Joyner, 2016, What happens when underperforming big ideas in research become entrenched?, JAMA, 316, 1355, 10.1001/jama.2016.11076

Shrager, 2014, Rapid learning for precision oncology, Nat Rev Clin Oncol, 11, 109, 10.1038/nrclinonc.2013.244

Obermeyer, 2016, Predicting the future — big data, machine learning, and clinical medicine, N Engl J Med, 375, 1216, 10.1056/NEJMp1606181

Siu, 2016, Facilitating a culture of responsible and effective sharing of cancer genome data, Nat Med, 22, 464, 10.1038/nm.4089

Blue Ribbon, 2016, Panel report to the National Cancer Advisory Board [Internet]

AACR Project GENIE Publicly Releases Large Cancer Genomic Data Set [Internet]

International Cancer Genome Consortium, 2010, International network of cancer genome projects, Nature, 464, 993, 10.1038/nature08987

Derry, 2012, Developing predictive molecular maps of human disease through community-based modeling, Nat Genet, 44, 127, 10.1038/ng.1089

Gao, 2013, Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci Signal, 6, l1, 10.1126/scisignal.2004088

Cerami, 2012, The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data, Cancer Discov, 2, 401, 10.1158/2159-8290.CD-12-0095

Jones, 2015, Personalized genomic analyses for cancer mutation discovery and interpretation, Sci Transl Med, 7, 283ra53, 10.1126/scitranslmed.aaa7161

OncoTree [Internet]

Lawrence, 2014, Discovery and saturation analysis of cancer genes across 21 tumour types, Nature, 505, 495, 10.1038/nature12912

Miao, 2016, Genomic determinants of cancer immunotherapy, Curr Opin Immunol, 41, 32, 10.1016/j.coi.2016.05.010

Campbell, 2016, Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas, Nat Genet, 48, 607, 10.1038/ng.3564

Cancer Genome Atlas Network, 2012, Comprehensive molecular characterization of human colon and rectal cancer, Nature, 487, 330, 10.1038/nature11252

Cancer Genome Atlas Network, 2012, Comprehensive molecular portraits of human breast tumours, Nature, 490, 61, 10.1038/nature11412

Forbes, 2015, COSMIC: exploring the world's knowledge of somatic mutations in human cancer, Nucleic Acids Res, 43, D805, 10.1093/nar/gku1075

Chang, 2016, Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity, Nat Biotechnol, 34, 155, 10.1038/nbt.3391

Chakravarty, OncoKB: a precision oncology knowledge base, JCO PO 2017 May 16 [Epub ahead of print]

Conley, 2016, Abstract IA38: NCI MATCH: a national precision medicine trial – Conception, development, and adjustment, Cancer Epidemiol Biomarkers Prev, IA38

Gracia, 2007, The learning healthcare system: workshop summary, 2013

Tunis, 2009, The national oncologic PET registry: lessons learned for coverage with evidence development, J Am Coll Radiol, 6, 360, 10.1016/j.jacr.2009.01.016

Haber, 2014, Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA, Cancer Discov, 4, 650, 10.1158/2159-8290.CD-13-1014

Thông tin
Thông tin xuất bản

Cancer Discovery

Tập 7 Số 8

818-831

Thông tin tác giả