Abstract
BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients. METHODS: We developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm. RESULTS: A mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80-0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51-7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63-15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels. CONCLUSIONS: The small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications.