Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers

Abstract

Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records. We systematically identified 776 genomic alterations associated with survival outcomes across 20 distinct cancer types treated with specific immunotherapies, chemotherapies, or targeted therapies. Additionally, we demonstrate how mutations in particular pathways correlate with treatment response. Leveraging the large number of identified predictive mutations, we developed a machine learning model to generate a risk score for response to immunotherapy in patients with advanced non-small cell lung cancer (aNSCLC). Through rigorous computational analysis of large-scale clinico-genomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology.

Fig. 1. Overview of mutation statistics and mutation-survival associations.

a Proportion of patients in each cancer with a given gene mutated (normalized per row). b Prognostic effects of mutations in individual genes on overall survival (OS), measured by adjusted hazard ratio (HR) with two-sided Wald test. Genes that are significantly correlated with OS (P < 0.05) in a cancer type are colored red or blue. Red (blue) indicates that mutations in that gene have negative (positive) prognostic effects on the survival of patients. Genes that are not significantly associated with patient outcomes are shown as a gray square. Here we focus on 20 types of cancers including advanced non-small cell lung cancer (aNSCLC), metastatic breast cancer (mBC), metastatic colorectal cancer (mCRC), metastatic pancreatic cancer (mPCa), ovarian cancer (OC), metastatic prostate cancer (PC), gastric cancer (GC), advanced melanoma (aMel), advanced bladder cancer (aBCa), endometrial carcinoma (EC), metastatic renal cell carcinoma (mRCC), head and neck cancer (HNC), small-cell lung cancer (SCLC), multiple myeloma (MM), acute myeloid leukemia (AML), hepatocellular carcinoma (HCC), diffuse large B cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL).

Fig. 2. Gene-treatment interaction analysis across cancer types.

Gene–treatment interactions are shown for (a) lung and upper tract cancers, b breast and ovarian cancers, c skin cancers, d gastrointestinal cancers, e. genitourinary cancers. For each cancer type and first-line treatment, genes with significant interactions with that treatment are listed (two-sided Wald test P value < 0.05 and overall FDR  < 0.05). The font size of a gene indicates the fraction of patients in that treatment group with mutations in that gene. The color indicates the interaction hazard ratio (HR) of the gene. Red (blue) indicates that mutations in the gene have negative (positive) interactions and impact the survival of patients receiving a particular treatment.

Fig. 3. Pathway-treatment interaction analysis across cancer types.

Pathway–treatment interactions are shown for (a) lung and upper tract cancers, b breast and ovarian cancers, c skin cancers, (d). gastrointestinal cancers, e. genitourinary cancers. For each cancer type and first-line treatment, pathways with significant interactions with that treatment are listed (two-sided Wald test P value < 0.05 and overall FDR  < 0.05). The font size of a pathway indicates the fraction of patients in that treatment group with any gene mutation in that pathway. The color indicates the interaction hazard ratio (HR) of the pathway. Red (blue) indicates that mutations in the pathway have negative (positive) interactions and impact the survival of patients receiving a particular treatment.

Fig. 4. Comparison of immunotherapy and non-immunotherapy in aNSCLC patient groups.

Immunotherapy versus non-immunotherapy is compared as (a) first-line and (b) second-line therapies in four aNSCLC patient groups consisting of patients with tumor mutation burden (TMB) low and Random Survival Forest (RSF) score low, patients with TMB low and RSF score high, patients with TMB high and RSF score low, and patients with TMB high and RSF score high. The circle sizes represent the percentage of patients in each group. The circle colors represent the overall survival hazard ratio (HR) for patients who received immunotherapy vs. non-immunotherapy as a. first-line and b. second-line therapy. Cross-validation standard deviation is reported for each HR.