Development and validation of a prediction model for the recurrence of stage 1 EGFR mutation positive NSCLC in patients using machine learning with WES-based gene sets.

Abstract

8563 Background: Predicting the risk of postoperative recurrence is becoming increasingly important in EGFR mutation positive ( EGFR-m) non-small cell lung cancer (NSCLC). Only a few reports conducted whole exome sequencing (WES) for genomic profiling in large scale EGFR-m NSCLC cases. Methods: We conducted WES and analyzed the clinical course of patients (pts) with NSCLC who underwent surgery between 1985 and 2019 in the PRISM project of our institute. We evaluated the EGFR-m patients’ characteristics, recurrence-free survival (RFS), and developed a prediction model using machine learning, Overlapping Group LASSO to predict whether the EGFR-m patients will recur within 5 years or not as "high-risk" and "low-risk". To develop and validate the prediction model, we divided the data from 2006 as the training cohort data set and the data before 2006 as the validation cohort data set. After the development of the prediction model, we performed a stratified Cox proportional hazards model to compare the RFS between the predicted groups. Results: A total of 585/1351(43.3%) pts were EGFR-m included in the PRISM project. Of all pts, stage I, EGFR-m were 205 pts. The median RFS of the stage I, EGFR-m 123.1 months (m). In the training cohort, the model detected 43 pts for high-risk and 88 pts for low-risk. In the validation cohort, the model predicted 29 pts for high-risk and 45 pts for low-risk. Median RFS of high-risk vs. low-risk were 52.2 m vs. 105 m (HR 2.43, p=0.01). The 1-year RFS rate, 2-year RFS rate, and 5-year RFS rates for high-risk and low-risk pts were 100% vs. 98.5%, 66.7% vs. 89.2%, and 41.7% vs. 66.1%, respectively. Twenty-eight gene set coefficients were non-zero in the prediction model. The gene sets with large positive coefficients that were considered important for prediction as "high risk" were the gene sets affected by KRAS gene overexpression and p53 gene knockdown. In contrast, gene sets repressed by mTOR inhibition and genes repressed by TBK1 gene knockdown and KRAS gene overexpression had large negative coefficients. Conclusions: We developed and validated the prediction model whether the EGFR-m patients will recur within 5 years or not. EGFR-m NSCLC recurrence appears to be high risk with pathways associated with KRAS and p53 genes, and low risk with mutations in the gene sets suppressed by the mTOR pathway and TBK1 gene. [Table: see text]