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.