Clinical Actionability Enhanced through Deep Targeted Sequencing of Solid Tumors

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 intra-tumor heterogeneity is present and whether it may affect clinical decision making. To unravel this challenge, we established a deep targeted sequencing platform to identify potentially actionable DNA alterations in tumor samples. Methods We assayed 515 FFPE tumor samples and matched germline (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 average sequencing depth and identified 4794 non-synonymous mutations in the samples analyzed, which 15.2% were present at less than 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%) would otherwise be unactionable. In addition, acquiring ultra-high depth also ensured a low false discovery rate (less than 2.2%) from FFPE samples. Conclusion 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 revealed the critical importance of acquiring and utilizing high depth in profiling clinical tumor samples and presented a very useful platform for implementing routine sequencing in a cancer care institution.

Targeting the HGF/MET Axis Counters Primary Resistance to KIT Inhibition in KIT-Mutant Melanoma

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