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Scientists closer to mapping cancer genes to help speed up new precision drugs

Scientists at the U.K.'s Wellcome Sanger Institute and the Broad Institute of MIT and Harvard are getting closer to creating a comprehensive map of genes necessary for cancer survival, which could help speed up the discovery of new drug targets and development of new cancer medicines.

The Cancer Dependency Map, which intends to bridge the gap between genomic sequencing and providing precision medicine to patients, aims to create a detailed rulebook of treatments for patients, by mapping the genes critical for the survival of cancer cells and analyzing the resulting datasets.

To find these drug targets, Cancer DepMap researchers use the CRISPR technology to edit genes in cancer cells that have been taken from tumors in patients and grown in the lab, turning them off one-by-one to measure how critical they are for the cancer to survive, and so tracking down which genes are the most likely to make viable drug targets.

In this new study, researchers analyzed data from two recently published CRISPR-Cas9 genetic screens and found the same genes essential to cancer survival, called dependencies, in both.

The researchers have validated the two largest CRISPR-Cas9 genetic screens in 725 cancer models, across 25 different cancer types; the two datasets together form the largest genetic screen of cancer cell lines to date, which will provide the basis for the Cancer Dependency Map in around 1,000 cancer models.

The results were published Dec. 20 in Nature Communications.

"This is the first analysis of its kind and is really important for the whole cancer research community," said Aviad Tsherniak of the Broad Institute of MIT and Harvard. "Not only have we reproduced common and specific dependencies across the two datasets, but we have taken biomarkers of gene dependency found in one dataset and recovered them in the other."

The study is important because it demonstrates the validity of the experimental methods and the consistency of the data, as well as showing the two large cancer dependency datasets are compatible, said Francesco Iorio of the Wellcome Sanger Institute and Open Targets.

"By joining them together, we will have access to much greater statistical power to narrow down the list of targets for the next generation of cancer treatments," Iorio said.