Ken Chen1, Funda Meric-Bernstam2, Hao Zhao1, Qingxiu Zhang3, Nader Ezzeddine3, Lin-Ya Tang3, Yuan Qi1, Yong Mao1, Tenghui Chen1, Zechen Chong1, Wanding Zhou1, Xiaofeng Zheng1, Amber Johnson3, Kenneth D Aldape4, Mark J Routbort5, Rajyalakshmi Luthra5, Scott Kopetz6, Michael A Davies7, John de Groot8, Stacy Moulder9, Ravi Vinod10, Carol J Farhangfar11, Kenna Mills Shaw3, John Mendelsohn3, Gordon B Mills12, Agda Karina Eterovic13. 1. Department of Bioinformatics and Computational Biology and. 2. Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX; Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Surgical Oncology. 3. Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX; 4. Department of Pathology. 5. Department of Hematopathology. 6. Department of GI Medical Oncology. 7. Department of Melanoma Medical Oncology. 8. Department of Neuro Oncology. 9. Department of Breast Medical Oncology, and. 10. Department of Sarcoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; 11. Levine Cancer Institute, Carolinas HealthCare System, Charlotte, NC; 12. Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Systems Biology, MD Anderson Cancer Center, Houston, TX. 13. Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Systems Biology, MD Anderson Cancer Center, Houston, TX. aketerovic@mdanderson.org.
Abstract
BACKGROUND: Further advances of targeted cancer therapy require comprehensive in-depth profiling of somatic mutations that are present in subpopulations of tumor cells in a clinical tumor sample. However, it is unclear to what extent such intratumor heterogeneity is present and whether it may affect clinical decision-making. To study this question, we established a deep targeted sequencing platform to identify potentially actionable DNA alterations in tumor samples. METHODS: We assayed 515 formalin-fixed paraffin-embedded (FFPE) tumor samples and matched germline DNA (475 patients) from 11 disease sites by capturing and sequencing all the exons in 201 cancer-related genes. Mutations, indels, and copy number data were reported. RESULTS: We obtained a 1000-fold mean sequencing depth and identified 4794 nonsynonymous mutations in the samples analyzed, of which 15.2% were present at <10% allele frequency. Most of these low level mutations occurred at known oncogenic hotspots and are likely functional. Identifying low level mutations improved identification of mutations in actionable genes in 118 (24.84%) patients, among which 47 (9.8%) otherwise would have been unactionable. In addition, acquiring ultrahigh depth also ensured a low false discovery rate (<2.2%) from FFPE samples. CONCLUSIONS: Our results were as accurate as a commercially available CLIA-compliant hotspot panel but allowed the detection of a higher number of mutations in actionable genes. Our study reveals the critical importance of acquiring and utilizing high sequencing depth in profiling clinical tumor samples and presents a very useful platform for implementing routine sequencing in a cancer care institution.
BACKGROUND: Further advances of targeted cancer therapy require comprehensive in-depth profiling of somatic mutations that are present in subpopulations of tumor cells in a clinicaltumor sample. However, it is unclear to what extent such intratumor heterogeneity is present and whether it may affect clinical decision-making. To study this question, we established a deep targeted sequencing platform to identify potentially actionable DNA alterations in tumor samples. METHODS: We assayed 515 formalin-fixed paraffin-embedded (FFPE) tumor samples and matched germline DNA (475 patients) from 11 disease sites by capturing and sequencing all the exons in 201 cancer-related genes. Mutations, indels, and copy number data were reported. RESULTS: We obtained a 1000-fold mean sequencing depth and identified 4794 nonsynonymous mutations in the samples analyzed, of which 15.2% were present at <10% allele frequency. Most of these low level mutations occurred at known oncogenic hotspots and are likely functional. Identifying low level mutations improved identification of mutations in actionable genes in 118 (24.84%) patients, among which 47 (9.8%) otherwise would have been unactionable. In addition, acquiring ultrahigh depth also ensured a low false discovery rate (<2.2%) from FFPE samples. CONCLUSIONS: Our results were as accurate as a commercially available CLIA-compliant hotspot panel but allowed the detection of a higher number of mutations in actionable genes. Our study reveals the critical importance of acquiring and utilizing high sequencing depth in profiling clinicaltumor samples and presents a very useful platform for implementing routine sequencing in a cancer care institution.
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