Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma

GOF Mutant p53 in Cancers: A Therapeutic Challenge

Simple Summary

In normal cells, p53 is a protein which regulates the cell cycle progression to ensure normal cell division, growth, and development. However, in cancer, changes in the p53 DNA sequence, called genetic mutation, results in the protein either losing its normal function or exhibiting advanced pro-tumorigenic functions that lead to cancer. Importantly, cancers with mutations in the p53 protein often represent ones which are more aggressive and more resistant to chemotherapy. As a result, many studies have and continue to investigate multiple ways to target mutant p53-bearing cancer using targeted therapy, gene therapy, immunotherapy, and combination therapies. Knowledge of these strategies is important in improving the overall therapeutic response of cancers with mutant p53. This review highlights new strategies and discusses the progression of such therapies.

Abstract

TP53 is mutated in the majority of human cancers. Mutations can lead to loss of p53 expression or expression of mutant versions of the p53 protein. These mutant p53 proteins have oncogenic potential. They can inhibit any remaining WTp53 in a dominant negative manner, or they can acquire new functions that promote tumour growth, invasion, metastasis and chemoresistance. In this review we explore some of the mechanisms that make mutant p53 cells resistant to chemotherapy. As mutant p53 tumours are resistant to many traditional chemotherapies, many have sought to explore new ways of targeting mutant p53 tumours and reinstate chemosensitivity. These approaches include targeting of mutant p53 stability, mutant p53 binding partners and downstream pathways, p53 vaccines, restoration of WTp53 function, and WTp53 gene delivery. The current advances and challenges of these strategies are discussed.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.