Journal Article

Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue

Minnai, Francesca, Tosi, Davide, Chierici, Marco, Incarbone, Matteo, Noci, Sara, Cotroneo, Chiara Elisabetta, Mattioni, Giovanni, Jurman, Giuseppe, Colombo, Francesca, Dragani, Tommaso A., Bartolini, Barbara

2023

Overview

Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in nontumoral lung tissue. Gene expression data from noninvolved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels are associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis‐expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1, and DUSP14). Pathway analysis indicated that the signature is enriched for ECM components. We identified 32 cis‐eQTLs for CNTNAP1, including 6 with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR < 1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue. Levels of four transcripts in non‐tumoral lung tissue were inversely associated with survival. Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by survivval analysis. For the associated gene CNTNAP1, cis‐eQTLs were identified, showing a correlation with transcript levels, and also associated with prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients’ non‐involved lung tissue.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc
• Subject: Adenocarcinoma / Adenocarcinoma of Lung - genetics / Age / B cells / Cancer / Gene expression / Genes / Genetic Predisposition to Disease / Genotype / Genotypes / Humans / Learning algorithms / Lung - pathology / lung neoplasm / Lung Neoplasms - genetics / Lung Neoplasms - pathology / Lungs / Machine learning / Medical prognosis / Mutation / Original / ORIGINAL ARTICLES / Patient outcomes / Patients / Polymorphism, Single Nucleotide / Prognosis / Quantitative genetics / Quantitative trait loci / quantitative trait locus / Smoking / Survival / Variables