Abstract
Interpretation of complex cancer genome data, generated by tumor target profiling platforms, is key for the success of personalized cancer therapy. How to draw therapeutic conclusions from tumor profiling results is not standardized and may vary among commercial and academically-affiliated recommendation tools. We performed targeted sequencing of 315 genes from 75 metastatic breast cancer biopsies using the FoundationOne assay. Results were run through 4 different web tools including the Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal, for drug and clinical trial recommendations. These recommendations were compared amongst each other and to those provided by FoundationOne. The identification of a gene as targetable varied across the different recommendation sources. Only 33% of cases had 4 or more sources recommend the same drug for at least one of the usually several altered genes found in tumor biopsies. These results indicate further development and standardization of broadly applicable software tools that assist in our therapeutic interpretation of genomic data is needed. Existing algorithms for data acquisition, integration and interpretation will likely need to incorporate artificial intelligence tools to improve both content and real-time status.
Highlights
Molecular target profiling of cancer is readily available in the clinic through commercial diagnostic companies and CLIA-accredited academic laboratories
The purpose of this study was to examine to what extent different web tools and a widely used commercial service identify similar therapeutic options for a given set of genomic anomalies in a cancer
Target profiling was performed by FoundationOne assay
Summary
Molecular target profiling of cancer is readily available in the clinic through commercial diagnostic companies and CLIA-accredited academic laboratories. Many academic laboratories, perform tumor-only sequencing and it is increasingly recognized that several of the variants (up to 15–20%) that are assumed to be somatic mutations may be germline alterations. Many computational algorithms exist that www.impactjournals.com/oncotarget predict functional impact for a variant (SIFT, PolyPhen, Mutation Assessor, etc) but the true accuracy of these predictions is uncertain and they often yield conflicting results for the same variant [9]. Laboratory validation of biological impact is only available for very few mutations and variants. Yes www.impactjournals.com/oncotarget the drugs and clinical trial options reported in the results reflect the options that were available when the test was performed.
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