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Supplemental Methods, Supplemental Tables 1-2, Supplemental Figures 1-4 from AACR Project GENIE: Powering Precision Medicine through an International Consortium

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posted on 2023-04-03, 21:22 authored by Fabrice André, Monica Arnedos, Alexander S. Baras, José Baselga, Philippe L. Bedard, Michael F. Berger, Mariska Bierkens, Fabien Calvo, Ethan Cerami, Debyani Chakravarty, Kristen K. Dang, Nancy E. Davidson, Catherine Del Vecchio Fitz, Semih Dogan, Raymond N. DuBois, Matthew D. Ducar, P. Andrew Futreal, Jianjiong Gao, Francisco Garcia, Stu Gardos, Christopher D. Gocke, Benjamin E. Gross, Justin Guinney, Zachary J. Heins, Stephanie Hintzen, Hugo Horlings, Jan Hudeček, David M. Hyman, Suzanne Kamel-Reid, Cyriac Kandoth, Walter Kinyua, Priti Kumari, Ritika Kundra, Marc Ladanyi, Céline Lefebvre, 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 M. Micheel, Clinton Miller, Gordon 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 R Mills Shaw, Priyanka Shivdasani, Lillian L. Siu, David B. Solit, Gabe S. Sonke, Jean Charles Soria, Parin Sripakdeevong, Natalie H. Stickle, Thomas P. 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 L. Wang, Chetna Wathoo, Stuart Watt, Celeste Yu, Thomas V. Yu, Emily Yu, Ahmet Zehir, Hongxin Zhang

Supplemental Methods. Supplemental Table 1: ââ,¬â€¹Genomic Data Characterization by Center. Supplemental Table 2: ââ,¬â€¹Gene Panels Submitted by Each Center. Figure S1: Number of putative germline SNPs per sample, before and after uniform germline filtering. Figure S2ââ,¬â€¹. Distribution of total somatic mutation burden per sample stratified by sequencing panel. Figure S3: ââ,¬â€¹Log-scale comparison of mutation frequencies at hotspot sites between GENIE (data aggregated from all sequencing panels) and cancerhotspots.org (CHS) using a binomial test. Figure S4:ââ,¬â€¹ Comparison of mutation frequencies at hotspot sites in each GENIE sequencing panel with cancerhotspots.org (CHS) using a binomial test.

Funding

Howard Hughes Medical Institute

NCI

Princess Margaret Cancer Foundation

Cancer Core Ontario Applied Clinical Research Unit

University of Toronto Division of Medical Oncology Strategic Innovation

Ontario Ministry of Health & Long Term Care Academic Health Services Centre

Funding Plan Innovation Award

Susan G. Komen

NIH

Dr. Miriam and Sheldon G. Adelson Medical Research Foundation

CCSG

CPRIT

T.J. Martell Foundation and CCSG

Maryland Cigarette Restitution Fund Research Grant

Commonwealth Foundation

Pfizer and Eli Lilly

Dutch Ministry of Health

Dutch Cancer Society

History

ARTICLE 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

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