Tumor mutation score is more powerful than tumor mutation burden in predicting response to immunotherapy in non-small cell lung cancer.

Affiliation

Li Y(1), Chen Z(2), Tao W(1), Sun N(3), He J(4).
Author information:
(1)Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China.
(2)Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
(3)Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.18 Panjiayuannanli, Beijing, 100021, China. [Email]
(4)Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.18 Panjiayuannanli, Beijing, 100021, China. [Email]

Abstract

Tumor mutation burden (TMB) predicts response to immunotherapy in non-small cell lung cancer (NSCLC). The current TMB evaluation is expensive and not satisfactory. Here, novel tumor mutation score (TMS) was defined as the number of genes with mutations in candidate genes and compared with TMB and PD-L1 in 240 NSCLC patients and validated in 34 NSCLC patients. Eighteen genes were significantly associated with longer progression-free survival (PFS) or better response. The number of mutated genes within 18 favorable genes were defined as TMS18. TMS18 (HR = 0.307, P < 0.001) had smaller hazard ratio and P value than TMB (HR = 0.455, P = 0.004) and PD-L1 expression (HR = 0.403, P = 0.005) in survival analysis. Moreover, TMS18 had significantly higher AUC than TMB and TMS18 combined with PD-L1 improved the accuracy. Universal cutoff of TMS18 enriched more patients with benefits. These findings were largely consistent in the validation cohort. Taken together, TMS18 was more powerful than TMB in predicting response of ICIs in NSCLC. Selective TMS was more feasible and cost-effective than unselective TMB. TMS18 combined with PD-L1 might yield better efficiency in predicting response of ICIs in NSCLC with future validation in larger cohorts.