Dataset Information

Genetic-Driven Druggable Target Identification and Validation.

ABSTRACT: Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.

SUBMITTER: Floris M

PROVIDER: S-EPMC6088790 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Genetic-Driven Druggable Target Identification and Validation.

Floris Matteo M Olla Stefania S Schlessinger David D Cucca Francesco F

Trends in genetics : TIG 20180523 7

Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optima ...[more]

PMID: 29803319

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Project description:BackgroundTP53, encoding the tumor suppressor p53, is frequently mutated in various cancers, producing mutant p53 proteins (mutp53) which can exhibit neomorphic, gain-of-function properties. The latter transform p53 into an oncoprotein that promotes metastatic tumor progression via downstream effectors such as ENTPD5, an endoplasmic reticulum UDPase involved in the calnexin/calreticulin cycle of N-glycoprotein biosynthesis. Elucidating the mechanisms underlying the pro-metastatic functions of the mutp53-ENTPD5 axis is crucial for developing targeted therapies for aggressive metastatic cancer.MethodsWe analyzed pancreatic, lung, and breast adenocarcinoma cells with p53 missense mutations to study the impact of mutp53 and ENTPD5 on the N-glycoproteins integrin-α5 (ITGA5) and integrin-β1 (ITGB1), which heterodimerize to form the key fibronectin receptor. We assessed the role of the mutp53-ENTPD5 axis in integrin-dependent tumor-stroma interactions and tumor cell motility using adhesion, migration, and invasion assays, identifying and validating therapeutic intervention targets. We employed an orthotopic xenograft model of pancreatic ductal adenocarcinoma to examine in vivo targeting of mutp53-ENTPD5-mediated ITGA5 regulation for cancer therapy.ResultsMutp53 depletion diminished ITGA5 and ITGB1 expression and impaired tumor cell adhesion, migration, and invasion, rescued by ENTPD5. The mutp53-ENTPD5 axis maintained ITGA5 expression and function via the calnexin/calreticulin cycle. Targeting this axis using ITGA5-blocking antibodies, α-glucosidase inhibitors, or pharmacological degradation of mutp53 by HSP90 inhibitors, such as Ganetespib, effectively inhibited ITGA5-mediated cancer cell motility in vitro. In the orthotopic xenograft model, Ganetespib reduced ITGA5 expression and metastasis in an ENTPD5-dependent manner.ConclusionsThe mutp53-ENTPD5 axis fosters ITGA5 and ITGB1 expression and tumor cell motility through the calnexin/calreticulin cycle, contributing to cancer metastasis. ITGA5-blocking antibodies or α-glucosidase inhibitors target this axis and represent potential therapeutic options worth exploring in preclinical models. The pharmacologic degradation of mutp53 by HSP90 inhibitors effectively blocks ENTPD5-ITGA5-mediated cancer cell motility and metastasis in vivo, warranting further clinical evaluation in p53-mutant cancers. This research underscores the significance of understanding the complex interplay between mutp53, ENTPD5, and the calnexin/calreticulin cycle in integrin-mediated metastatic tumor progression, offering valuable insights for the development of potential therapeutic strategies.

Dataset Information

Genetic-Driven Druggable Target Identification and Validation.

Publications

Genetic-Driven Druggable Target Identification and Validation.

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