Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery.

Long V Nguyen,Carlos Caldas

doi:10.15252/emmm.202013189

Abstract

Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre‐clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large‐scale pharmacogenomics studies and drug screening approaches to improve pre‐clinical drug screening and biomarker discovery.

Highlights

See the Glossary for abbreviations used in this article. Introduction kinase inhibitors such as Imatinib for chronic myelogenous leukaemia harbouring the BCR-ABL translocation (Annunziata et al, 2020), BRAF/MEK inhibitors for BRAF mutated cancers (Zaman et al, 2019), HER2-targeted therapies in breast cancer (Wang & Xu, 2019), and inhibitors of EGFR or ALK kinases in lung adenocarcinomas driven by EGFR mutation or ALK fusions (Bernicker et al, 2019)
Efforts have been taken to model cancer in different systems, study how specific genomic alterations result in changes to tumour growth, and test different therapies in these model systems as a surrogate readout for how the patient’s cancer will respond to treatment
We will review RNA interference (RNAi) and CRISPR technologies applied to functional genomic screens for the discovery of novel therapeutic targets, drug screens, and combination therapy screens using different cancer models in order to identify novel molecular biomarkers that can predict response to drug therapy

Summary

Introduction

Introduction kinase inhibitors such asImatinib for chronic myelogenous leukaemia harbouring the BCR-ABL translocation (Annunziata et al, 2020), BRAF/MEK inhibitors for BRAF mutated cancers (Zaman et al, 2019), HER2-targeted therapies in breast cancer (Wang & Xu, 2019), and inhibitors of EGFR or ALK kinases in lung adenocarcinomas driven by EGFR mutation or ALK fusions (Bernicker et al, 2019). We will review RNA interference (RNAi) and CRISPR technologies applied to functional genomic screens for the discovery of novel therapeutic targets, drug screens, and combination therapy screens using different cancer models in order to identify novel molecular biomarkers that can predict response to drug therapy.

Results

Conclusion