An integrative scoring system for survival prediction following gefitinib therapy in non-small cell lung cancer: From a long-term real-world study.

Abstract

e21041 Background: Gefitinib is first-line therapy for patients with EGFR-activating mutation in non-small cell lung cancer (NSCLC). Meanwhile, ̃30% patients were progression within 12 months even harboring EGFR-activating mutation. Furthermore, integrative scoring models for prediction of gefitinib outcomes are still lacking. Variations in genes of DNA repair and cell cycle pathway may have potential impacts on proliferation, migration and invasion of tumor cells. Therefore, we investigated a series of single nucleotide polymorphisms (SNP) in DNA repair and cell cycle genes based on an efficient algorithm machine learning algorithm for establishing an integrative scoring system for survival prediction following gefitinib therapy in NSCLC patients. Methods: A total of 282 patients with activating EGFR mutations were enrolled and divided into train cohort and test cohort, randomly. 125 SNPs in 47 candidate genes were selected by Heploview 4.2 and sequenced by Agena MassARRAY System. Predictive features were selected by randomForestSRC, an efficient algorithm machine learning algorithm to analyze survival data, for PFS following gefitinib therapy. The top candidate variables were presented into COX regression models with hazard ratio as weight for each predictor (HR = 1-1.49 = 1; HR = 1.5-2.49 = 2; HR = 2.5-3.49 = 3), providing p < 0.1. This study was approved by the ethical committee of Sun Yat-Sen University Cancer Center. Results: The progression risk predictive model was established by random forest survival and validated by COX regression analysis, included XRCC1 rs3213263(CT vs CC, RR = 1.997, 95%CI = 1.314-3.036, p = 0.001), FOXO3 rs75544369(GA vs GG, RR = 1.942, 95%CI = 1.139-3.309, p = 0.012), FOXM1 rs2302257(CC vs GG, RR = 2.889, 95%CI = 1.477-5.691, p = 0.002) and ERCC1 rs10408989(TT vs GT, RR = 1.735, 95%CI = 1.152-2.613, p = 0.008; GG vs GT, RR = 3.788, 95%CI = 1.625-8.832, p = 0.002). The progression risk score ranged from 0-10. 49 patients (25.52%) and 20 patients (22.22%) were scored 0, 18(9.38%) and 10(11.11%) patients were scored more than 5 in train cohort and test cohort, respectively. According to the risk score of patients, we divided the patients into four groups, the median PFS were 26.23(95%CI:14.23-35.37), 16.80(95%CI:12.87-23.60), 11.13(95%CI:8.87-16.70) and 6.25 (95% CI:4.43-11.67) months for 0, 2, 3&4 and ≥5 group (p < 0.0001) in the train cohort. Meanwhile, the median PFS were 18.80(95%CI:11.80-64.53),13.60(95%CI:9.17-27.33), 13.47(95%CI:9.83-18.40), 13.47(95%CI:9.83-18.40) and 7.40(95%CI:3.33- NA) months for 0, 2, 3&4 and ≥5 group in validation cohort (p = 0.0017). Conclusions: The risk score system is a simple tool for risk stratification in patients undergoing gefitinib. Widespread application of the score system will require further independent validation.