Researchers are one step closer to cancer precision medicine

A new, computational model has brought us one step closer to precision medicine for cancer.

Researchers from the Faculty of Medicine and the Institute for Molecular Medicine (FIMM) at the University of Helsinki have developed a computational model, Combined Essentiality Scoring (CES), that could bring us closer to cancer precision medicine.

The model enables accurate identification of essential genes in cancer cells for development of anti-cancer drugs.

Essential genes important in cancer

Cancer cells grow faster with the activation of certain genes. Targeted therapies aim at inhibiting these genes that are activated only in cancer cells, and thus minimising side effects to normal cells.

High-throughput genetic screening has been established for evaluating the importance of individual genes for the survival of cancer cells. Such an approach allows researchers to determine the so-called gene essentiality scores for nearly all genes across a large variety of cancer cell lines.

However, challenges with replicability of the estimated gene essentiality have hindered its use for drug target discovery.

Wenyu Wang, first author of the study, said: “shRNA and CRISPR-Cas9 are the two common techniques used to perform high-throughput genetic screening.

“Despite improved quality control, the gene essentiality scores from these two techniques differ from each other on the same cancer cell lines.”

How can we do better?

To harmonise genetic screening data, researchers proposed a novel computational method called Combined Essentiality Scoring (CES) that predicts cancer essential genes using the information from shRNA and CRISPR-Cas9 screens plus molecular features of cancer cells.

The team demonstrated that CES could detect essential genes with higher accuracy than the existing computational methods.

Furthermore, the team showed that two predicted essential genes were indeed correlated with poor prognosis separately for breast cancer and leukaemia patients, suggesting their potential as drug targets.

Assistant Professor Jing Tang, corresponding author of the study, said: “Improving gene essentiality scoring is just a beginning. Our next aim is to predict drug-target interactions by integrating drug sensitivity and gene essentiality profiles.

“Given the ever-increasing volumes of functional screening datasets, we hope to extend our knowledge of drug target profiles that will eventually benefit drug discovery in personalised medicine.”