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JCO Precis Oncol. 2019;3. doi: 10.1200/PO.18.00307. Epub 2019 Mar 28.

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

Author information

1
Department of Epidemiology and Biostatistics.
2
Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine.
3
Department of Medicine.
4
Department of Pathology.
5
Marie-Jose and Henry R. Kravis Center for Molecular Oncology.
6
Human Oncology and Pathogenesis Program.

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

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