Multiomics Data Analysis Identifies Cancers’ Genetic Vulnerabilities

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Centogene’s CentoMD® rare disease variant database will be integrated into Qiagen’s bioinformatics suite in a rare disease bioinformatics collaboration by the companies. [Source: alengo/Getty]

Researchers based at Queens University Belfast have created a program and specialized algorithm that can identify genetic vulnerabilities in cancer cells that could be used to create new therapies in the future.

There is a known genetic phenomenon where if a cell has a particular genetic variant, it might mean other genes in the cell are vulnerable to therapeutic interventions such as inhibition with certain chemicals.

“Gene dependency relationships, including synthetic lethality, may produce cancer ‘Achilles heels’; indeed, cancer cells typically accumulate large numbers of genetic aberrations and therefore are vulnerable to therapeutic strategies that exploit gene dependencies,” write the authors in the journal Nucleic Acids Research.

The program created by the research team is designed to look for these genetic relationships and when tested against other methods was very effective at finding them in a range of different cancer cells and tissues.

Ian Overton, Ph.D., and Simon McDade, Ph.D., both senior lecturers at the Patrick G Johnston Centre for Cancer Research at Queen’s University Belfast, led the study, which involved developing an online open access, platform called Synthetic Lethality with Gene expression and Genomics (SynLeGG) and an accompanying algorithm called MultiSEp.

SynLeGG finds and visualizes these ‘loss signatures’ in omics data fed into the program to identify these achilles heel genetic dependencies. It relies on the MultiSEp algorithm to automatically assign cells into gene expression groups, which allows further mutational analysis and discovery of the genetic dependency relationships for each cell type.

The researchers tested the efficacy of the SynLeGG and MultiSEp combination at finding these potential drug targets on 783 cancer cell lines and 30 different tissue samples.

When compared with SynLethDB, a respected database of synthetic lethality gene pairs, the new method developed in this study achieved 2.8- to 8.5-fold greater coverage.

“Understanding the molecular fingerprints of cancer can pinpoint ways to target drugs precisely to those patients where they will be most effective. Our work makes a step towards more effective and personalized cancer treatments, ultimately saving lives,” said Overton.

“We make our results available on the ‘Synthetic Lethality with Genetics and Genomics’ web server, opening a window to share these rich resources with researchers across the scientific community – in order to accelerate progress in cancer research globally.”

The researchers hope that other scientists and drug developers will use their program to look for these potential drug targets.

“Our results provide the wider scientific community access to key datasets generated by cutting edge technologies, and a toolkit with which to analyze this data… Ultimately, we hope that, by increasing the reach of this data we can expedite more targeted and effective cancer treatments,” said Mark Wappett, an honorary lecturer at Queen’s University Belfast and Head of Bioinformatics at Almac Discovery.

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