Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach

Abstract

A massive amount of somatic mutations has been cataloged in large-scale projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium projects. The majority of the somatic mutations found in tumor genomes are neutral 'passenger' rather than damaging "driver" mutations. Now, understanding their biological consequences and prioritizing them for druggable targets are urgently needed. Thanks to the rapid advances in structural genomics technologies (e.g. X-ray), large-scale protein structural data has now been made available, providing critical information for deciphering functional roles of mutations in cancer and prioritizing those alterations that may mediate drug binding at the atom resolution and, as such, be druggable targets. We hypothesized that mutations at protein-ligand binding-site residues are likely to be druggable targets. Thus, to prioritize druggable mutations, we developed SGDriver, a structural genomics-based method incorporating the somatic missense mutations into protein-ligand binding-site residues using a Bayes inference statistical framework. We applied SGDriver to 746,631 missense mutations observed in 4997 tumor-normal pairs across 16 cancer types from The Cancer Genome Atlas. SGDriver detected 14,471 potential druggable mutations in 2091 proteins (including 1,516 recurrently mutated proteins) across 3558 cancer genomes (71.2%), and further identified 298 proteins harboring mutations that were significantly enriched at protein-ligand binding-site residues (adjusted p value < 0.05). The identified proteins are significantly enriched in both oncoproteins and tumor suppressors. The follow-up drug-target network analysis suggested 98 known and 126 repurposed druggable anticancer targets (e.g. SPOP and NR3C1). Furthermore, our integrative analysis indicated that 13% of patients might benefit from current targeted therapy, and this -proportion would increase to 31% when considering drug repositioning. This study provides a testable strategy for prioritizing druggable mutations in precision cancer medicine.

Fig. 1.

Computational pipeline to prioritize significantly mutated proteins (SMPs) and druggable mutations via SGDriver. A, Summary of samples in 16 major cancer types used in this study. B, Histogram view of the total mutational load in individual tumor type. C, Workflow of SGDriver. SGDriver is under a hypothesis that if a gene product (protein) has more somatic deleterious mutations at its protein–ligand binding-site residues, this protein would be more likely cancer-associated or related to anticancer drug responses. D, Landscape of SMGs during pan-cancer analysis. E, Heat map showing the distribution of druggable mutations in ligand-binding residues for top 13 gene products. F, Workflow for computational prescription during precision cancer medicine. The 16 major cancer types are acute myeloid leukemia (LAML), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), colon and rectal adenocarcinoma (COAD/READ), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).

Fig. 2.

Mutation frequencies and distribution pattern in 16 cancer types. A, Missense mutation frequencies across 16 major cancer types. Distribution of missense mutation frequencies in whole protein sequences (All) versus protein–ligand binding-site residues (LBS) in 16 major cancer types. B, C, Cumulative distribution of deleterious mutations at protein–ligand binding-site residues, their two immediate flanking residues, and nonbinding residues. Cumulative frequencies of SIFT (B) and PolyPhen-2 scores (C) for protein–ligand binding-site residues (direct positions), ±1 flanking residues (D + 1 flanking positions), and nonbinding residues (outside positions). Dotted line in each graph denotes the cutoff used to define “deleterious” mutations: SIFT score ≤ 0.05 and PolyPhen2 ≥ 0.909. Abbreviations of 16 cancer types in Figs. 2–5, and 7 are provided in the legend of Fig. 1.

Fig. 3.

Manhattan plot of putative significantly mutated proteins in pan-cancer analysis using SGDriver. Each dot represents a gene or its protein. Red dots represent the Cancer Gene Census genes. Yellow dots represent the significantly mutated genes collected from literatures (supplemental Table S3). The horizontal red line denotes the false discovery rate at 0.05.

Fig. 4.

Putative significantly mutated proteins in individual cancer analysis for 16 cancer types identified by SGDriver. Each dot represents a gene or its protein. Red dots represent the genes with adjusted p value (q) < 10⁻¹⁰. Yellow dots represent the genes with q between 10⁻¹⁰ and 0.05 (excluding 0.05).

Fig. 5.

Spectra of druggability for the predicted significantly mutated proteins (SMPs) in cancer. A, Distribution of SMPs targeted by U.S. FDA approved anticancer drugs, repurposing drugs (approved for treatment of other diseases), and experimental drugs. B, Illustration of identifying known druggable mutations by three example proteins (BRAF, EGFR, and KRAS). C, Illustration of identifying new druggable mutations by three example proteins (SPOP, ESR1, and NR3C1). Protein PDB files were downloaded from the PDB database (http://www.rcsb.org/). Protein three-dimensional structures in B and C were drawn by PyMol software (https://www.pymol.org).

Fig. 6.

Global drug-target interaction network by targeting the predicted significantly mutated proteins (SMPs) in cancer. In this network, nodes include 180 targets (SMPs, squares) and 668 U.S. FDA approved drugs (circles), and edges denote the interactions with pharmacological profiles (IC₅₀, EC₅₀ or K_i) less than 10 μM. All drugs were grouped using the Anatomical Therapeutic Chemical (ATC) classification system codes. The original Cytoscape file is provided in supplementary file.

Fig. 7.

Survey of clinical benefit rate from therapeutic agents. A, Frequency of missense mutations at the protein–ligand binding-site residues of the top 50 significantly mutated proteins (SMPs) with the lowest adjusted p value. B, Summary of U.S. FDA approved or experimental agents targeting the predicted SMPs in each cancer type. The broken line indicates the number of cancer patients who may potentially benefit from the treatment of these agents.