Integrated molecular characterisation of the MAPK pathways in human cancers reveals pharmacologically vulnerable mutations and gene dependencies

Abstract

The mitogen-activated protein kinase (MAPK) pathways are crucial regulators of the cellular processes that fuel the malignant transformation of normal cells. The molecular aberrations which lead to cancer involve mutations in, and transcription variations of, various MAPK pathway genes. Here, we examine the genome sequences of 40,848 patient-derived tumours representing 101 distinct human cancers to identify cancer-associated mutations in MAPK signalling pathway genes. We show that patients with tumours that have mutations within genes of the ERK-1/2 pathway, the p38 pathways, or multiple MAPK pathway modules, tend to have worse disease outcomes than patients with tumours that have no mutations within the MAPK pathways genes. Furthermore, by integrating information extracted from various large-scale molecular datasets, we expose the relationship between the fitness of cancer cells after CRISPR mediated gene knockout of MAPK pathway genes, and their dose-responses to MAPK pathway inhibitors. Besides providing new insights into MAPK pathways, we unearth vulnerabilities in specific pathway genes that are reflected in the re sponses of cancer cells to MAPK targeting drugs: a revelation with great potential for guiding the development of innovative therapies.

Treatment landscape of triple-negative breast cancer — expanded options, evolving needs

“…The MAPK cascade is a highly conserved signalling pathway with oncogenic roles 113,114 . Activating mutations in genes encoding MAPK pathway components (such as KRAS, HRAS, BRAF and MEK1/2) are very rare in TNBC; however, the amplification of these genes and non-canonical mechanisms of MAPK pathway activation, including the overexpression of growth factor receptors (such as EGFR, FGFR1 and IGF1R) or loss of negative regulators (including NF1 or DUSP4), have been described and might occur in ~3% of TNBCs, although their incidence is hard to estimate 32,104 .…”

A Machine Learning and Bioinformatic Analysis Reveals an Associated between Cell Surface Receptor Transcript Levels with Drug Response of Breast Cancer Cells and the Drug Off-Target Effects

“…The latter is a promising approach that is likely to improve treatment outcomes, as the strategy would not depend on the biological importance of the targeted CSRs within downstream signalling pathways: irrespective of its biological function, any CSR that is expressed in tumour cells but not in healthy cells would be a viable target. This approach to anticancer therapy promises fewer disease remissions and better efficacy than current treatments as non-specifically cytotoxic compounds would not, like current chemotherapeutics, only kill tumour cells with a specific molecular phenotype [34][35][36][37][38]. For as long as the cells in the tumour express the targeted CSR, the non-specific toxin(s) that is(are) delivered to these cells will kill all the cells in their proximity, including those that do not express the CSR.…”

Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

“…Multiple omics data are often generated to examine various biological aspects of drug-resistant cancer cells (Figure 1). Target genotypes and phenotypes examined using omics data (Table 1) include: cancer-associated mutations (Niehr et al, 2018;Marczyk et al, 2020;Sinkala et al, 2021); changes in the expression level of specific genes (Niehr et al, 2018;Nava et al, 2019;Kagohara et al, 2020;Marczyk et al, 2020;Poojan et al, 2020;Sinkala et al, 2021); changes in chromosome structure (Kagohara et al, 2020;Marczyk et al, 2020;Aissa et al, 2021); epigenetic alterations (e.g., methylation or acetylation states of histone • Methylome (reduced representation bisulfite sequencing)…”

“…• ceRNA network for correlation between lncRNA and mRNA levels using RNA-seq data proteins) (Nava et al, 2019;Kagohara et al, 2020;Marczyk et al, 2020;Poojan et al, 2020;Sinkala et al, 2021); and the presence of heterogeneity of a cell population (Niehr et al, 2018), often increasingly examined at a single-cell resolution (Kagohara et al, 2020;Aissa et al, 2021). In a recent study for cell line heterogeneity, for example, application of single-cell DNA and RNA sequencing (RNA-seq) to 20 triple-negative breast cancer (TNBC) patients revealed that rare pre-existing clones having genotypes associated with chemoresistance were adaptively selected in response to neoadjuvant chemotherapy, which subsequently led to acquired transcriptional reprogramming (Kim et al, 2018).…”