Dataset Information

Molecular Signatures of Fusion Proteins in Cancer.

ABSTRACT: Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 structural, functional, and regulatory features of ∼2200 gene fusions. We report subgroups of fusions that likely act as driver mutations and find that gene fusions disproportionately affect pathways regulating cellular shape and movement. Although fusion proteins are similar across different cancer types, they affect cancer type-specific pathways. Key indicators of fusion-forming proteins include high and nontissue specific expression, numerous splice sites, and higher centrality in protein-interaction networks. Together, these findings provide unifying and cancer type-specific trends across diverse oncogenic fusion proteins.

SUBMITTER: Latysheva NS

PROVIDER: S-EPMC7088938 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Molecular Signatures of Fusion Proteins in Cancer.

Latysheva Natasha S NS Babu M Madan MM

ACS pharmacology & translational science 20190320 2

Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 s ...[more]

PMID: 32219217

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Project description:Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

Project description:BackgroundSerum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels.MethodsWe mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors.ResultsWe show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors.ConclusionsOur findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Dataset Information

Molecular Signatures of Fusion Proteins in Cancer.

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Molecular Signatures of Fusion Proteins in Cancer.

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