Of all genes, 760 are the cause of multiple types of cancer cells and those cancers are strongly dependent on these genes for their growth and survival.
However, about 10 percentages of them are common across multiple cancers, suggesting that a relatively small number of therapies targeting these core dependencies might each hold promise for combating several tumors.
Cancer cells thrive despite harboring mutations that should kill them. By mapping the dependencies cancer cells rely on for survival, researchers hope to reveal new treatment opportunities.
To generate these findings, the research team conducted genome-wide RNA interference (RNAi) screens on 501 cell lines representing more than 20 types of cancer, silencing more than 17,000 genes individually in each line to identify genetic dependencies unique to cancerous cells.
Cancer cells can harbor a broad variety of genetic errors, from small mutations to wholesale swaps of DNA between chromosomes. If an error shuts down a critical gene, a cancerous cell will compensate by adjusting other genes’ activity, frequently developing a dependence on such adaptations in order to persist.
Identifying these dependencies provides opportunities for scientists to gain deeper insight into cancer biology and determine new therapeutic targets.
RNAi silences genes using small pieces of RNA called small interfering RNAs (siRNAs). To run a genome-wide RNAi screen, researchers expose cells to pools of siRNAs and track the cells’ behavior.
Using a set of molecular features (e.g., mutations, gene copy numbers, expression patterns) from each cell line, the team also generated biomarker-based models that helped explain the biology behind 426 of the 769 dependencies. Most of those biomarkers fell into four broad categories:
Mutation(s) of a gene;
• Loss of a copy or reduced expression of a gene;
• Increased expression of a gene;
• Reliance on a gene functionally or structurally related to another, lost gene (a.k.a., a paralog dependence).
Surprisingly, more than 80 percent of the dependencies with biomarkers were associated with changes (up or down) in a gene’s expression. Mutations, often used as the grounds for pursuing a gene as a drug target, accounted for merely 16 percent of biomarker-associated. dependencies.
Twenty percent of the dependencies the team discovered were associated with genes previously identified as potential drug targets.
Read the original HERE…