A 20-gene mutation signature predicts the efficacy of immune checkpoint inhibitor therapy in advanced non-small cell lung cancer patients

Abstract

Background There is an unmet need to identify novel predictive biomarkers that enable more accurate identification of individuals who can benefit from immune checkpoint inhibitor (ICI) therapy. The US FDA recently approved tumor mutational burden (TMB) score of ≥ 10 mut/Mb as a threshold for pembrolizumab treatment of solid tumors. Our study aimed to test the hypothesis that specific gene mutation signature may predict the efficacy of ICI therapy more precisely than high TMB (≥ 10). Methods We selected 20 candidate genes that may predict for the efficacy of ICI therapy by the analysis of data from a published cohort of 350 advanced non-small cell lung cancer (NSCLC) patients. Then, we compared the influences of various gene mutation signatures on the efficacy of ICI treatment. They were also compared with PD-L1 and TMB. The Kaplan-Meier method was utilized to evaluate the prognosis univariates, while selected univariates were adopted to develop a systematic nomogram. Results A high mutation signature, where three or more of the 20 selected genes were mutated, was associated with the significant benefits of ICI therapy. Specifically, patients with high mutation signature were confirmed to have better prognosis for ICI treatment, compared with those with wild type (the median PFS: 7.17 vs. 2.90 months, p = 0.0004, HR = 0.47 (95% [CI]:0.32–0.68); the median OS: unreached vs. 9 months, p = 1.8E-8, HR = 0.17 (95% [CI]:0.11–0.25)). Moreover, those patients with the high mutation signature achieved significant ICI treatment benefits, while there was no difference of OS and PFS between patients without the signature but TMB-H (≥ 10) and those without the signature and low TMB(< 10). Finally, we constructed a novel nomogram to evaluate the efficacy of ICI therapy. Conclusion A high mutational signature with 3 or more of the 20-gene panel could provide more accurate predictions for the outcomes of ICI therapy than TMB ≥ 10 in NSCLC patients.

Unlocking the Potential of Biomarkers for Immune Checkpoint Inhibitors in Cancer Therapy

“…Additionally, it encompasses the examination of circulating and systemic markers within the host [ 4 , 28 , 29 ]. These encompass immune gene signatures [ 30 ], tumor-infiltrating lymphocytes (TILs), diverse T cell populations (such as CD8+, regulatory T cells, and T helper cells) [ 24 , 25 ], myeloid-derived suppressor cells (MDSCs) [ 31 ], and even the composition of the gut microbiome [ 32 ]. Thanks to advanced omics techniques, advancements in computational tools, the application of artificial intelligence, and a systems biology approach, emerging studies combine multiple markers to define expression patterns or nomograms that may accurately predict the outcomes of ICIs [ 30 , 33 , 34 ].…”

“…These encompass immune gene signatures [ 30 ], tumor-infiltrating lymphocytes (TILs), diverse T cell populations (such as CD8+, regulatory T cells, and T helper cells) [ 24 , 25 ], myeloid-derived suppressor cells (MDSCs) [ 31 ], and even the composition of the gut microbiome [ 32 ]. Thanks to advanced omics techniques, advancements in computational tools, the application of artificial intelligence, and a systems biology approach, emerging studies combine multiple markers to define expression patterns or nomograms that may accurately predict the outcomes of ICIs [ 30 , 33 , 34 ]. However, while the clinical feasibility of such an approach, as well as the utilization of single markers, necessitate further investigation, the validation and standardization of these methodologies across different cancer types and treatment contexts remain crucial challenges.…”