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Supplementary Methods, Tables 1-9, Figure Legends 1-3 from High-Throughput Detection of Actionable Genomic Alterations in Clinical Tumor Samples by Targeted, Massively Parallel Sequencing

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posted on 2023-04-03, 20:25 authored by Nikhil Wagle, Michael F. Berger, Matthew J. Davis, Brendan Blumenstiel, Matthew DeFelice, Panisa Pochanard, Matthew Ducar, Paul Van Hummelen, Laura E. MacConaill, William C. Hahn, Matthew Meyerson, Stacey B. Gabriel, Levi A. Garraway

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ARTICLE ABSTRACT

Knowledge of “actionable” somatic genomic alterations present in each tumor (e.g., point mutations, small insertions/deletions, and copy-number alterations that direct therapeutic options) should facilitate individualized approaches to cancer treatment. However, clinical implementation of systematic genomic profiling has rarely been achieved beyond limited numbers of oncogene point mutations. To address this challenge, we utilized a targeted, massively parallel sequencing approach to detect tumor genomic alterations in formalin-fixed, paraffin-embedded (FFPE) tumor samples. Nearly 400-fold mean sequence coverage was achieved, and single-nucleotide sequence variants, small insertions/deletions, and chromosomal copynumber alterations were detected simultaneously with high accuracy compared with other methods in clinical use. Putatively actionable genomic alterations, including those that predict sensitivity or resistance to established and experimental therapies, were detected in each tumor sample tested. Thus, targeted deep sequencing of clinical tumor material may enable mutation-driven clinical trials and, ultimately, “personalized” cancer treatment.Significance: Despite the rapid proliferation of targeted therapeutic agents, systematic methods to profile clinically relevant tumor genomic alterations remain underdeveloped. We describe a sequencing-based approach to identifying genomic alterations in FFPE tumor samples. These studies affirm the feasibility and clinical utility of targeted sequencing in the oncology arena and provide a foundation for genomics-based stratification of cancer patients. Cancer Discovery; 2(1); 82–93. ©2011 AACR.Read the Commentary on this article by Corless and Spellman, p. 23This article is highlighted in the In This Issue feature, p. 1