KRAS and TP53 co-mutation predicts benefit of immune checkpoint blockade in lung adenocarcinoma.

BACKGROUND: Predictive biomarkers in use for immunotherapy in advanced non-small cell lung cancer are of limited sensitivity and specificity. We analysed the potential of activating KRAS and pathogenic TP53 mutations to provide additional predictive information. METHODS: The study cohort included 713 consecutive immunotherapy patients with advanced lung adenocarcinomas, negative for actionable genetic alterations. Additionally, two previously published immunotherapy and two surgical patient cohorts were analyzed. Therapy benefit was stratified by KRAS and TP53 mutations. Molecular characteristics underlying KRASmut/TP53mut tumours were revealed by the analysis of TCGA data. RESULTS: An interaction between KRAS and TP53 mutations was observed in univariate and multivariate analyses of overall survival (Hazard ratio [HR] = 0.56, p = 0.0044 and HR = 0.53, p = 0.0021) resulting in a stronger benefit for KRASmut/TP53mut tumours (HR = 0.71, CI 0.55-0.92). This observation was confirmed in immunotherapy cohorts but not observed in surgical cohorts. Tumour mutational burden, proliferation, and PD-L1 mRNA were significantly higher in TP53-mutated tumours, regardless of KRAS status. Genome-wide expression analysis revealed 64 genes, including CX3CL1 (fractalkine), as specific transcriptomic characteristic of KRASmut/TP53mut tumours. CONCLUSIONS: KRAS/TP53 co-mutation predicts ICI benefit in univariate and multivariate survival analyses and is associated with unique molecular tumour features. Mutation testing of the two genes can be easily implemented using small NGS panels.

Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR-mutated lung cancer treated with the first-/second-generation tyrosine kinase inhibitors.

This study analyzed whether extended molecular profiling can predict the development of epidermal growth factor receptor (EGFR) gene T790M mutation, which is the most frequent resistance alteration in non-small cell lung cancer (NSCLC) after treatment with the first-/second-generation (1G/2G) EGFR inhibitors (tyrosine kinase inhibitors [TKIs]), but only weakly associated with clinical characteristics. Whole exome sequencing (WES) was performed on pretreatment tumor tissue with matched normal samples from NSCLC patients with (n = 25, detected in tissue or blood rebiopsies) or without (n = 14, negative tissue rebiopsies only) subsequent EGFR p.T790M mutation after treatment with 1G/2G EGFR TKI. Several complex genetic biomarkers were assessed using bioinformatic methods. After treatment with first-line afatinib (44%) or erlotinib/gefitinib (56%), median progression-free survival and overall survival were 12.1 and 33.7 months, respectively. Clinical and tumor genetic characteristics, including age (median, 66 years), sex (74% female), smoking (69% never/light smokers), EGFR mutation type (72% exon 19 deletions), and TP53 mutations (41%) were not significantly associated with T790M mutation (p > 0.05). By contrast, complex biomarkers including tumor mutational burden, the clock-like mutation signature SBS1 + 5, tumor ploidy, and markers of subclonality including mutant-allele tumor heterogeneity, subclonal copy number changes, and median tumor-adjusted variant allele frequency were significantly higher at baseline in tumors with subsequent T790M mutation (all p < 0.05). Each marker alone could predict subsequent development of T790M with an area under the curve (AUC) of 0.72-0.77, but the small number of cases did not allow confirmation of better performance for biomarker combinations in leave-one-out cross-validated logistic regression (AUC 0.69, 95% confidence interval: 0.50-0.87). Extended molecular profiling with WES at initial diagnosis reveals several complex biomarkers associated with subsequent development of T790M resistance mutation in NSCLC patients receiving first-/second-generation TKIs as the first-line therapy. Larger prospective studies will be necessary to define a forecasting model.