Genomic and transcriptomic analyses identify a prognostic gene signature and predict response to therapy in pleural and peritoneal mesothelioma

Abstract

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. A better understanding of mesothelioma genomics and transcriptomics could advance therapies. Here, we present a mesothelioma cohort of 122 patients along with their germline and tumor whole-exome and tumor RNA sequencing data as well as phenotypic and drug response information. We identify a 48-gene prognostic signature that is highly predictive of mesothelioma patient survival, including CCNB1, the expression of which is highly predictive of patient survival on its own. In addition, we analyze the transcriptomics data to study the tumor immune microenvironment and identify synthetic-lethality-based signatures predictive of response to therapy. This germline and somatic whole-exome sequencing as well as transcriptomics data from the same patient are a valuable resource to address important biological questions, including prognostic biomarkers and determinants of treatment response in mesothelioma.

Keywords: WES; mesothelioma; response to therapy; synthetic lethality; transcriptome.

Graphical abstract

Figure 1

Overview of the NCI mesothelioma data and mutational signature analysis Shown are demographic information, tumor histology, and mutational profiles of patients with mesothelioma. The mesothelioma cohort in this study is comprised of 122 patients, including patients with pleural (n = 59), peritoneal (n = 61), and tunica vaginalis (n = 2) forms of mesothelioma. The mutational profile consists of germline mutations in P and LP cancer predisposition genes as well as somatic mutations in genes associated with signaling pathways and receptors, DNA repair genes, TFs and repressors, splicing factors, and enzymes. Copy number changes are indicated for the relevant genes.

Figure 2

GO enrichment analysis and validation of mesothelioma prognostic signature (A) GO enrichment analysis of the mesothelioma prognostic signature using the GOrilla tool. GO terms with p < 1e−4 are shown here (list of all GO terms with p < 1e−3 are listed in Table S5C). (B–D) Survival analysis using a K-M plot of patients in the top 50th (high risk) and bottom 50th (low risk) percentiles based on their predicted risk scores in (B) the NCI mesothelioma dataset in cross-validation, (C) the TCGA mesothelioma dataset, and (D) the Bueno et al. mesothelioma dataset. Log rank test p values are shown.

Figure 3

Mesothelioma prognostic signature analysis (A) The protein-protein interaction (PPI) network of the genes in the mesothelioma prognostic signature identified 3 cliques in the NCI mesothelioma dataset by STRING. (B) Protein complex enrichment analysis of clique 1 genes. These genes are enriched in 2 complexes, the CCNB1-CCNF complex and the CDK1-CCNB1-CCNF complex (Fisher’s exact test, FDR < 0.02). The x axis represents Fisher’s test negative log₁₀ p values. (C) The correlation matrix was computed on the matrix of the 48-gene mesothelioma prognostic signature across all patients. Hierarchical clustering identified three clusters that significantly overlapped with the three cliques obtained in the PPI network. Fisher’s exact test p values are shown. (D) The mesothelioma prognostic signature is enriched for 8 transcription factors (TFs) (details provided in Table S5H). (E) Correlation between expression of mesothelioma signature genes and corresponding TFs. Expression of every gene in the mesothelioma prognostic signature cohort that is potentially regulated by one or more of the 8 enriched TFs shown in (D) was correlated with expression of the corresponding mapped TF (FDR < 0.2). The number of such enriched TFs that were significantly correlated (FDR < 0.2) with these genes is plotted as a bar graph. The count of enriched TFs that are significantly correlated vs. the total number of TFs mapped to a particular gene is shown in parentheses. (F) Median essentiality values from CRISPR-Cas9 gene knockout essentiality screens from 7 pleural mesothelioma cell lines (ACCMESO1, NCIH2452, NCIH2052, MPP89, ISTMES2, MSTO211H, and NCIH28) for each gene are computed and compared between genes in the mesothelioma prognostic signature and the remaining genes. Less essentiality value implies that the gene is more essential. One-sided Wilcoxon rank-sum test p value is shown.

Figure 4

Immune cell abundance estimates (A) Boxplot of the relative fractions of the immune cell abundance across all mesothelioma samples in the NCI mesothelioma dataset (NCI MESO), TCGA pan-cancer patients (TCGA PAN-CANCER; except mesothelioma), TCGA lung adenocarcinoma (TCGA LUAD), and TCGA lung squamous cell carcinoma (TCGA LUSC). The relative immune cell abundance for each immune cell type is shown as a fraction on the y axis. (B) K-M survival plot of patients with the top 33rd and bottom 33rd percentiles of relative abundance of M2 macrophages in the NCI mesothelioma dataset. Time is shown in years. Log rank test p value is shown.

Figure 5

Overall SL transcriptomics-based response prediction across several mesothelioma clinical trials (A) We use SELECT to predict patient response for anti-PD1 drugs in the NCI mesothelioma patient cohort using patient gene expression data. Bar plots show ROC-AUC and area under precision recall (PR-AUC) values using the SELECT-derived risk to predict responders (complete or partial response) and non-responders (stable disease or progressive disease) to anti-PD1 immunotherapy (16 samples). ROC-AUC and PR-AUC values range from 0–1, with higher values indicating higher performance of the predictor. ROC-AUC for a random predictor is expected to be 0.5. The 95% confidence interval for ROC-AUC varied from 0.7–1. (B) Bar plots show ROC-AUC and PR-AUC values using the SELECT-derived risk to predict responders (complete or partial response) and non-responders (progressive disease) to combinations with the chemotherapy drug pemetrexed (41 samples). The 95% confidence interval for ROC-AUC varied from 0.46–0.88. (C) Scatterplot showing the percentage of responders (objective response rate) from mesothelioma clinical trials and comparing it with the percentage of predicted responders (coverage) using SELECT in the NCI mesothelioma dataset. Spearman’s ρ (rho) and p values are shown. The shaded region is the 95% confidence level interval for a linear model. (D) Bar plot showing the percentage of predicted responders for each drug. (E) Heatmap showing the responders in all patients and drugs for NCI mesothelioma data. The x axis corresponds to patient samples and y axis to each drug. Red indicates that the patient is predicted to respond to that drug, and green indicates non-response. 50% of the total percentage of patients are predicted to respond to at least 1 drug in the NCI mesothelioma dataset.