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

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.

ATP and Adenosine Metabolism in Cancer: Exploitation for Therapeutic Gain

“…Therefore, a better understanding of the mechanisms linking intratumoral adenosine metabolism to other components of the cancer purinome can aid in the development of novel adenosine-related biomarkers associated with treatment response and survival and, on this basis, identify rational enzyme-based strategies for therapeutic intervention, personalized for the specific needs of each individual cancer. There is also an increased need for the development of novel blood-based biomarker platforms that reliably detect cancer (Haemmerle et al, 2018;Doherty et al, 2019;Nguyen and Caldas, 2021). The measurement of soluble and/or exosomal purinergic activities in the blood of cancer patients might represent a compelling addition to this 'liquid biopsy' arsenal.…”

“…The postgenomic era has led to a revolution in cancer therapies and implies the need for more personalized therapies. The molecular heterogeneity of cancer tissue can be evaluated by a variety of methods, including RNAi and CRISPR screens, pharmacogenomics studies, single-cell RNA sequencing (scRNA-seq), whole-genome sequencing and whole-exome sequencing genome-wide approaches (Doherty et al, 2019;Nguyen and Caldas, 2021). This is particularly relevant for the multifaceted network of purine metabolism.…”

“…One of the key issues in interpreting preclinical findings from the cancer purinome is the question of whether data obtained in established human cancer cell lines and genetically engineered mouse models translate to the clinic. Hundreds of human cancer cell lines have been established and are widely used to study cancer biology and to enable high-throughput functional genomic and proteomic screening (Iorio et al, 2016;Frejno et al, 2020;Nguyen and Caldas, 2021). Circulating cancer cells or samples derived from biopsies can be propagated into organoids as a model system designed to recapitulate the genetic, histologic, and molecular features of the tumor of origin.…”

“…Circulating cancer cells or samples derived from biopsies can be propagated into organoids as a model system designed to recapitulate the genetic, histologic, and molecular features of the tumor of origin. Those organoids are amenable to drug screening and predict clinical drug responses (Bleijs et al, 2019;Nguyen and Caldas, 2021). Although cancer cell lines and patientderived tumor organoids and xenografts are powerful tools to define signaling, metabolic, and transcriptomic pathways implicated in tumor behavior, these model systems do not necessarily capture all molecular phenotypes of a particular tumor type and in addition do not preserve the tumor architecture and microenvironment.…”

In vitro breast cancer models for studying mechanisms of resistance to endocrine therapy

“…These studies demonstrate the power of conducting unbiased whole-genome in vitro functional genomic screens to reveal biomarkers of therapy response and the application of these findings to assist in the interpretation of clinically annotated genomic datasets to generate a deeper understanding of disease mechanisms. Further functional genomic investigations using both improved screening libraries and techniques and more relevant disease models will provide an even greater understanding of these processes, as recently reviewed by Nguyen and Caldas [ 135 ].…”