CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response

Abstract

Predicting the response and identifying additional targets that will improve the efficacy of chemotherapy is a major goal in cancer research. Through large-scale in vivo and in vitro CRISPR knockout screens in pancreatic ductal adenocarcinoma cells, we identified genes whose genetic deletion or pharmacologic inhibition synergistically increase the cytotoxicity of MEK signaling inhibitors. Furthermore, we show that CRISPR viability scores combined with basal gene expression levels could model global cellular responses to the drug treatment. We develop drug response evaluation by in vivo CRISPR screening (DREBIC) method and validated its efficacy using large-scale experimental data from independent experiments. Comparative analyses demonstrate that DREBIC predicts drug response in cancer cells from a wide range of tissues with high accuracy and identifies therapeutic vulnerabilities of cancer-causing mutations to MEK inhibitors in various cancer types.

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

“…Indeed, the analysis of the mutational landscape of cancer has also uncovered the existence of mutual exclusivity and co-occurrence patterns among driver gene alterations [16,69]. Many computational tools have been developed to identify those combinatorial patterns experimentally (i.e via CRISPR-Cas9 screens [70,71]) or computationally [72][73][74][75][76][77][78][79]. Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions.…”

“…Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions. While functional redundancy has been used to reveal unknown functional interactions [79], the synthetic lethality concept has been very successfully applied to the identification of novel therapeutic targets [70,71] or rational drug combinations [71], and to the prediction of drug response in cell lines [71] and patients [78].…”

In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster

“…In general, despite leaving copy number effect out of consideration, MAGeCK ( 137 ) remains the most widely used tool in various biological fields such as identifying cancer drivers ( 151 ), drug targets ( 152 ), and pathway components ( 153 ). Its prominent advantages over other tools are the all-around service covering both upstream and downstream analyses, relative ease of use, and the excellent ranking criteria that deal well with variable gRNA efficacies.…”

Artificial intelligence, regenerative surgery, robotics? What is realistic for the future of surgery?

“…Genetic engineering will also play a big part in the next 50 years. The ability to not only rapidly sequence our genes, but also to manipulate this to aid our diagnosis and treatment will be indispensable; for example, targeting defects using CRSIPR-cas9 [ 20 ]. It has been predicted that this could introduce a completely new sub-speciality of surgery; genome surgery [ 21 ].…”