Harnessing Clinical Sequencing Data for Survival Stratification of Patients with Metastatic Lung Adenocarcinomas

Abstract

Purpose: Broad panel sequencing of tumors facilitates routine care of people with cancer as well as clinical trial matching for novel genome-directed therapies. We sought to extend the use of broad panel sequencing results to survival stratification and clinical outcome prediction.

Patients and methods: Using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification.

Results: With OncoCast, we stratified this patient cohort into four risk groups based on tumor genomic profile. Patients whose tumors harbored a high-risk profile had a median survival of 7.3 months (95% CI 5.5-10.9), compared to a low risk group with a median survival of 32.8 months (95% CI 26.3-38.5), with a hazard ratio of 4.6 (P<2e-16), far superior to any individual gene predictor or standard clinical characteristics. We found that co-mutations of both STK11 and KEAP1 are a strong determinant of unfavorable prognosis with currently available therapies. In patients with targetable oncogenes including EGFR/ALK/ROS1 and received targeted therapies, the tumor genetic background further differentiated survival with mutations in TP53 and ARID1A contributing to a higher risk score for shorter survival.

Conclusion: Mutational profile derived from broad-panel sequencing presents an effective genomic stratification for patient survival in advanced lung adenocarcinoma. OncoCast is available as a public resource that facilitates the incorporation of mutational data to predict individual patient prognosis and compare risk characteristics of patient populations.

Keywords: Metastatic lung adenocarcinoma; mutational profiling; prognostic model.

FIG 1.

Prognostic relevance and clonality of cancer genes. (A) Plot of selection frequency in each model and regression coefficients for individual genes; the black vertical bar shows favorable versus unfavorable association with overall survival. (B) Clonality analysis of the cancer gene alterations. Circle size is proportional to mutation frequency.

FIG 2.

An integrated prognostic scoring system for metastatic lung adenocarcinomas. (A) Histogram of the prognostic risk score computed using the OncoCast model in 1,054 metastatic lung adenocarcinomas. The risk score is scaled from 0 to 10, with a higher score indicating higher likelihood of shorter survival. Dashed lines indicate the percentile cutoffs used to stratify patients into four risk subgroups (low, intermediate-low, intermediate-high, and high). (B) Boxplots of concordance index for predicting overall survival using clinical demographic factors including age, sex, smoking, and the OncoCast risk score. CN, copy number; mut, mutation.

FIG 3.

Outcome prediction on the basis of risk score. (A) Kaplan-Meier plot of survival curves for the four risk subgroups (Low, intermediate-low [Int-low], intermediate-high [Int-high], and high). Colored areas represent 95% CIs. The average risk score (Avg RS), median overall survival (OS), and 1-year and 3-year survival probabilities are reported for each group. (B) Forest plot of hazard ratios (HRs) for age, sex, smoking status, and individual driver gene alterations compared with the OncoCast integrated scoring approach.

FIG 4.

Overlaying OncoCast risk score with tumor mutation burden, mutational signature, smoking status, and mutation status for commonly mutated genes. APOBEC, apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like.

FIG 5.

Genomic risk stratification and distribution of common mutation and comutation patterns by each risk group.

FIG 6.

(A) Risk score distribution from the OncoCast-TR (targeted therapies) model stratifies patients into two subsets. (B) Overall survival probability for the two TR patient subsets (TR1 and TR2). (C) Gene importance plot for the OncoCast-TR model.