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.

Homogenous TP53mut-associated tumor biology across mutation and cancer types revealed by transcriptome analysis.

TP53 is the most frequently mutated gene in human cancer. While no TP53-targeting drugs have been approved in the USA or Europe so far, preclinical and clinical studies are underway to investigate targeting of specific or all TP53 mutations, for example, by restoration of the functionality of mutated TP53 (TP53mut) or protecting wildtype TP53 (TP53wt) from negative regulation. We performed a comprehensive mRNA expression analysis in 24 cancer types of TCGA to extract (i) a consensus expression signature shared across TP53 mutation types and cancer types, (ii) differential gene expression patterns between tumors harboring different TP53 mutation types such as loss of function, gain of function or dominant-negative mutations, and (iii) cancer-type-specific patterns of gene expression and immune infiltration. Analysis of mutational hotspots revealed both similarities across cancer types and cancer type-specific hotspots. Underlying ubiquitous and cancer type-specific mutational processes with the associated mutational signatures contributed to explaining this observation. Virtually no genes were differentially expressed between tumors harboring different TP53 mutation types, while hundreds of genes were over- and underexpressed in TP53mut compared to TP53wt tumors. A consensus list included 178 genes that were overexpressed and 32 genes that were underexpressed in the TP53mut tumors of at least 16 of the investigated 24 cancer types. In an association analysis of immune infiltration with TP53 mutations in 32 cancer subtypes, decreased immune infiltration was observed in six subtypes, increased infiltration in two subtypes, a mixed pattern of decreased and increased immune cell populations in four subtypes, while immune infiltration was not associated with TP53 status in 20 subtypes. The analysis of a large cohort of human tumors complements results from experimental studies and supports the view that TP53 mutations should be further evaluated as predictive markers for immunotherapy and targeted therapies.

Ioncopy: an R Shiny app to call copy number alterations in targeted NGS data.

BACKGROUND: Somatic copy number alterations (CNAs) contribute to the clinically targetable aberrations in the tumor genome. For both routine diagnostics and biomarkers research, CNA analysis in a single assay together with somatic mutations is highly desirable. RESULTS: Ioncopy is a validated method and easy-to-use software for CNA calling from targeted NGS data. Copy number and significance of CNA are estimated for each gene in each sample. Copy number gains and losses are called after multiple testing corrections controlling FWER or FDR. CONCLUSIONS: Ioncopy facilitates calling of CNAs in a cohort of tumors tissues with or without using normal (germline) DNA controls.