Abstract
BackgroundAlthough large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.MethodsTo overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.ResultsWithin the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.ConclusionsOur results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.
Highlights
Large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers
In reporting potential druggability across the The Cancer Genome Atlas (TCGA) cohort, we considered all tumors with mutational evidence; we only considered tumors with mRNA and protein/phosphoprotein outliers for genes that could be validated against Genomics of Drug Sensitivity in Cancer (GDSC) data regardless of level of approval
With drug repurposing across cancer types, in which a drug used primarily in cancer type A with mutation X is repurposed for cancer type B with mutation X, we find that an additional 5.4% of patients may be treated with a Food and Drug Administration (FDA)-approved drug-variant interaction (Figs. 2 and 3, Additional file 2: Table S12)
Summary
Large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers. With the development of novel therapeutics and next-generation sequencing (NGS), medicine is entering an era in which cancer treatment can be tailored to the tumor molecular profile of the individual patient. Pan-cancer analyses have identified significantly mutated genes shared across cancer type subsets [5,6,7], suggesting the potential for treating patients based on the genetic profile of their tumor, regardless of cancer type. Sengupta et al Genome Medicine (2018) 10:60 profiles from NGS and other platforms to infer druggability is an ongoing challenge [12, 17, 18]. Sensitive in DEPO Predicted functional mutations from HotSpot3D Cancer Type Specific.
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