Small molecule inhibitors are designed to exert their efficacy by binding to cellular protein targets, triggering a cascade of downstream alterations in cancer signaling networks. High-throughput phenotype-based drug screening has successfully identified compounds that lead to promising efficacy. However, a crucial next step is to illustrate the cellular responses of drug treatment and to identify potential genetic biomarkers predictive of drug sensitivity or resistance.
Emerging studies have indicated that cancer cells harbor specific genetic variations that play important roles when responding to drug treatment. Identifying key functional genetic signatures of drug perturbation has become one of the major challenges for precision medicine. Functional genetic screening approaches, such as siRNA and shRNA based knock-down or CRISPR-Cas9 based knock-out, have been extensively employed to identify cancer essential genes both in vitro and in vivo. On the other hand, drug sensitivity screening is commonly used to study the effects of drug-target inhibitions at the protein level. Moreover, mutagenesis is also employed to understand the complex interactions of drugs and proteins with specific amino acid changes. To facilitate the clinical relevance of the functional screening approaches, model systems such as 3D organoid and patient-derived xenografts (PDX) have been developed. Furthermore, recent technologies in single-cell labeling and deep sequencing have significantly improved the resolution of these experiments, generating tremendous amounts of data expected to contribute to a systematic mapping of genetic dependencies of drug treatment.
To make full use of data from functional screens for facilitating effective drug and target discovery, novel computational and bioinformatics tools will be required. For example, in RNAi and CRISPR-based functional screening approaches, advanced algorithms to determine the optimal guide sequences are critical and computational tools to distinguish off-target effects have to be utilized. In another domain, recent developments have been aimed at harmonizing existing functional screening datasets, making them more accessible and comparable. Versatile computational tools and various information resources shall provide the basis for more integrative analysis using functional genetics screening data.
In this research topic, we will discuss recent advances in functional screening experiments and related computational technologies and applications, and show how these can be utilized for the discovery of drugs and molecular targets. We encourage authors to submit systematic reviews, opinion letters, original experimental research, and computational tools with specific relevance for these applications. Research papers describing novel experiments are expected to improve our understanding of genetic functions and drug response. Research papers focusing on computational tool development should demonstrate how their methods improve the accuracy, interpretation, or accessibility of the functional screening data. With the coordinated efforts from both experimental and computational perspectives, we hope that the research topic could provide a timely update on functional screening for translational medicine and drug discovery.
Small molecule inhibitors are designed to exert their efficacy by binding to cellular protein targets, triggering a cascade of downstream alterations in cancer signaling networks. High-throughput phenotype-based drug screening has successfully identified compounds that lead to promising efficacy. However, a crucial next step is to illustrate the cellular responses of drug treatment and to identify potential genetic biomarkers predictive of drug sensitivity or resistance.
Emerging studies have indicated that cancer cells harbor specific genetic variations that play important roles when responding to drug treatment. Identifying key functional genetic signatures of drug perturbation has become one of the major challenges for precision medicine. Functional genetic screening approaches, such as siRNA and shRNA based knock-down or CRISPR-Cas9 based knock-out, have been extensively employed to identify cancer essential genes both in vitro and in vivo. On the other hand, drug sensitivity screening is commonly used to study the effects of drug-target inhibitions at the protein level. Moreover, mutagenesis is also employed to understand the complex interactions of drugs and proteins with specific amino acid changes. To facilitate the clinical relevance of the functional screening approaches, model systems such as 3D organoid and patient-derived xenografts (PDX) have been developed. Furthermore, recent technologies in single-cell labeling and deep sequencing have significantly improved the resolution of these experiments, generating tremendous amounts of data expected to contribute to a systematic mapping of genetic dependencies of drug treatment.
To make full use of data from functional screens for facilitating effective drug and target discovery, novel computational and bioinformatics tools will be required. For example, in RNAi and CRISPR-based functional screening approaches, advanced algorithms to determine the optimal guide sequences are critical and computational tools to distinguish off-target effects have to be utilized. In another domain, recent developments have been aimed at harmonizing existing functional screening datasets, making them more accessible and comparable. Versatile computational tools and various information resources shall provide the basis for more integrative analysis using functional genetics screening data.
In this research topic, we will discuss recent advances in functional screening experiments and related computational technologies and applications, and show how these can be utilized for the discovery of drugs and molecular targets. We encourage authors to submit systematic reviews, opinion letters, original experimental research, and computational tools with specific relevance for these applications. Research papers describing novel experiments are expected to improve our understanding of genetic functions and drug response. Research papers focusing on computational tool development should demonstrate how their methods improve the accuracy, interpretation, or accessibility of the functional screening data. With the coordinated efforts from both experimental and computational perspectives, we hope that the research topic could provide a timely update on functional screening for translational medicine and drug discovery.
