Chemotherapy is by far, the most common form of Cancer treatment given around the globe. However, our current knowledge on specific indicators of drug resistance or reduced effect is quite limited. In order to optimize how physicians prescribe various chemotherapies, researchers from the University of California San Francisco decided to generate a quantitative chemotherapy gene interaction map. Upon completion, this map is able to guide clinicians in prescribing almost all FDA approved chemotherapeutic agents for both breast and ovarian cancer not based on popularity, but on the genetic nature of a patients tumor. In order to generate this map, researchers first genetically knocked down 625 different genes related to breast & ovarian cancer, as well as DNA damage repair, in MCF10A cells, immortal epithelial cells similar to serous ovarian cancer cells. Once an individual gene was knocked down via siRNA, each cell line was exposed to one of 31 different drugs. These drugs contained 23 FDA approved chemotherapeutic agents for the treatment of breast and/or ovarian cancer, 4 PARP inhibitors, 2 similarly targeted therapies, and 2 common drug combinations, making grand total of 19,4069 different gene-drug interactions. Of these interactions, researchers found that 1,042 gene knockouts lead to drug resistance where 740 actually induced drug sensitivity, averaging 27 positive and 22 negative interactions per individual drug. To ease the readability of the gene map, researchers then grouped individual drugs by their specific class and mode of action, confirming that all drugs in each group had similar levels of drug sensitivity. These 6 groups included microtubule inhibitors, PARP inhibitors, Topo inhibitors, Replication inhibitors, Crosslinking agents, & Alkylating agents. By grouping the drugs by mechanism of action, the researchers were able to reduce the size of the gene map to show the effect of 125 mutations on efficacy of each drug class. Through further investigation, the researchers were also able to identify which groups of drugs had synergistic effects when combined in the presence of particular deletions as well as the mechanism by which 2 genes are able to cause PARP resistance upon knockdown. The authors are hopeful that this interaction map will assist clinicians when prescribing chemotherapeutic medication for their patients, and have even put the full map on a public website in order to reach and hopefully help as many cancer patients as possible.
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