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A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma.

Gregory D Jones¹, Whitney S Brandt¹, Ronglai Shen^2,3

¹Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.

|December 23, 2020

Summary

Integrating tumor genomics with clinicopathologic features improves recurrence prediction in lung adenocarcinoma (LUAD) after surgery. This new model, PRecur, offers better risk stratification than the TNM system alone for early-stage LUAD patients.

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Area of Science:

Oncology
Genomics
Translational Research

Background:

Current adjuvant therapy recommendations for lung adenocarcinoma (LUAD) rely solely on TNM staging, neglecting crucial genomic and clinicopathologic factors.
A more precise method is needed to identify patients at high risk of recurrence after surgical resection.

Purpose of the Study:

To identify tumor genomic factors associated with recurrence in resected stages I-III LUAD, independent of clinicopathologic variables.
To develop and validate a machine-learning prediction model (PRecur) integrating genomic and clinicopathologic features for improved recurrence risk prediction compared to the TNM system.

Main Methods:

A prospective cohort of 426 patients with completely resected stages I-III LUAD underwent broad-panel next-generation sequencing and clinicopathologic data analysis.
Relapse-free survival (RFS) was the primary endpoint, analyzed using Kaplan-Meier and Cox regression.
The PRecur model was developed using gradient-boosting survival regression and externally validated on The Cancer Genome Atlas (TCGA) LUAD dataset.

Main Results:

Alterations in SMARCA4, TP53, and the fraction of genome altered were independently associated with RFS.
The PRecur model demonstrated superior predictive ability for RFS compared to the TNM-based model (CPE 0.73 vs. 0.61).
External validation using TCGA data confirmed PRecur's ability to effectively separate risk groups.

Conclusions:

Integrating tumor genomics and clinicopathologic features significantly enhances risk stratification and recurrence prediction in early-stage LUAD post-surgery.
The PRecur model offers a more accurate approach to identifying patients at risk, potentially improving clinical trial accrual for adjuvant therapies.