Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis

Identification of cytokine-induced cell communications by pan-cancer meta-analysis

Cancer immune responses are complex cellular processes in which cytokine-receptor interactions play central roles in cancer development and response to therapy; dysregulated cytokine-receptor communication may lead to pathological processes, including cancer, autoimmune diseases, and cytokine storm; however, our knowledge regarding cytokine-mediated cell-cell communication (CCI) in different cancers remains limited. The present study presents a single-cell and pan-cancer-level transcriptomics integration of 41,900 cells across 25 cancer types. We developed a single-cell method to actively express 62 cytokine-receptor pairs to reveal stable cytokine-mediated cell communications involving 84 cytokines and receptors. The correlation between the sample-based CCI profile and the interactome analysis indicates multiple cytokine-receptor modules including TGFB1, IL16ST, IL15, and the PDGF family. Some isolated cytokine interactions, such as FN1-IL17RC, displayed diverse functions within over ten single-cell transcriptomics datasets. Further functional enrichment analysis revealed that the constructed cytokine-receptor interaction map is associated with the positive regulation of multiple immune response pathways. Using public TCGA pan-cancer mutational data, co-mutational analysis of the cytokines and receptors provided significant co-occurrence features, implying the existence of cooperative mechanisms. Analysis of 10,967 samples from 32 TCGA cancer types revealed that the 84 cytokine and receptor genes are significantly associated with clinical survival time. Interestingly, the tumor samples with mutations in any of the 84 cytokines and receptors have a substantially higher mutational burden, offering insights into antitumor immune regulation and response. Clinical cancer stage information revealed that tumor samples with mutations in any of the 84 cytokines and receptors stratify into earlier tumor stages, with unique cellular compositions and clinical outcomes. This study provides a comprehensive cytokine-receptor atlas of the cellular architecture in multiple cancers at the single-cell level.

Clinicopathological Features of Non-Small Cell Lung Carcinoma with BRAF Mutation

(1) Background: BRAF mutations affect 4-5% of lung adenocarcinomas. This study aimed to analyze the clinicopathological features of lung carcinomas with BRAF mutations, focusing on V600E vs. non-V600E and the presence of co-mutations. (2) Methods: All BRAF-mutated lung carcinomas were retrieved from a molecular diagnostic unit (the reference unit for four different hospitals). The samples were analyzed using next-generation sequencing. Statistical analyses included log-rank tests for overall survival (OS) and progression-free survival (PFS). (3) Results: In total, 60 BRAF-mutated lung carcinomas were retrieved: 24 (40.0%) with V600E and 36 (60.0%) with non-V600E mutations, and 21 (35.0%) with other co-mutations and 39 (65.0%) with only BRAF mutations. Survival data were available for 54/60 (90.0%) cases. Targeted therapy was documented in 11 cases. Patients with V600E mutations exhibited a better prognosis than patients with non-V600E mutations (p = 0.008 for OS, p = 0.018 for PFS); this was confirmed in PFS (p = 0.036) when considering only patients who received no targeted therapy. Patients with co-mutations displayed no prognostic difference compared to patients carrying only BRAF mutations (p = 0.590 for OS, p = 0.938 for PFS). (4) Conclusions: BRAF-mutated lung carcinomas with V600E (40.0%) had a better prognosis than those without V600E. Concomitant co-mutations (35.0%) did not affect the prognosis.

Somatic mutation effects diffused over microRNA dysregulation

MOTIVATION

As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios.

RESULTS

To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA-miRNA network.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php.

CoMutDB: the landscape of somatic mutation co-occurrence in cancers

MOTIVATION

Somatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking.

RESULTS

Using multi-omics data from over 30 000 subjects and 1747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online database CoMutDB: http://www.innovebioinfo.com/Database/CoMutDB/Home.php.

A Pan-Cancer Assessment of RB1/TP53 Co-Mutations

Simple Summary

Cancers are caused by genetic alterations called mutations. In some cases, specific mutation combinations act synergistically to provide unique advantages for cancer development. These mutation combinations are observed more frequently than by random chance. In this study, we investigated a large public tumor mutation database and found the most diverse and frequent concurrent mutations occur in TP53 and RB1. We enumerated the cancer types with TP53/RB1 co-mutations and investigated the patient outcome and the specific characteristics of cancer cells with TP53/RB1 co-mutations, especially the drugs that can and cannot be used to kill these cells. Our work provides a tool for cancer researchers to investigate co-mutations and provides insights into the treatment of TP53/RB1 co-mutated cancers.

Abstract

Nearly all tumors have multiple mutations in cancer-causing genes. Which of these mutations act in tandem with other mutations to drive malignancy and also provide therapeutic vulnerability? To address this fundamental question, we conducted a pan-cancer screen of co-mutation enrichment (looking for two genes mutated together in the same tumor at a statistically significant rate) using the AACR-GENIE 11.0 data (AACR, Philadelphia, PA, USA). We developed a web tool for users to review results and perform ad hoc analyses. From our screen, we identified a number of such co-mutations and their associated lineages. Here, we focus on the RB1/TP53 co-mutation, which we discovered was the most frequently observed co-mutation across diverse cancer types, with particular enrichment in small cell carcinomas, neuroendocrine carcinomas, and sarcomas. Furthermore, in many cancers with a substantial fraction of co-mutant tumors, the presence of concurrent RB1/TP53 mutations is associated with poor clinical outcomes. From pan-cancer cell line multi-omics and functional screening datasets, we identified many targetable co-mutant-specific molecular alterations. Overall, our analyses revealed the prevalence, cancer type-specificity, clinical significance, and therapeutic vulnerabilities of the RB1/TP53 co-mutation in the pan-cancer landscape and provide a roadmap forward for future clinical translational research.

EZH2 regulates a SETDB1/ΔNp63α axis via RUNX3 to drive a cancer stem cell phenotype in squamous cell carcinoma

Enhancer of zeste homolog 2 (EZH2) and SET domain bifurcated 1 (SETDB1, also known as ESET) are oncogenic methyltransferases implicated in a number of human cancers. These enzymes typically function as epigenetic repressors of target genes by methylating histone H3 K27 and H3-K9 residues, respectively. Here, we show that EZH2 and SETDB1 are essential to proliferation in 3 SCC cell lines, HSC-5, FaDu, and Cal33. Additionally, we find both of these proteins highly expressed in an aggressive stem-like SCC sub-population. Depletion of either EZH2 or SETDB1 disrupts these stem-like cells and their associated phenotypes of spheroid formation, invasion, and tumor growth. We show that SETDB1 regulates this SCC stem cell phenotype through cooperation with ΔNp63α, an oncogenic isoform of the p53-related transcription factor p63. Furthermore, EZH2 is upstream of both SETDB1 and ΔNp63α, activating these targets via repression of the tumor suppressor RUNX3. We show that targeting this pathway with inhibitors of EZH2 results in activation of RUNX3 and repression of both SETDB1 and ΔNp63α, antagonizing the SCC cancer stem cell phenotype. This work highlights a novel pathway that drives an aggressive cancer stem cell phenotype and demonstrates a means of pharmacological intervention.