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Despite the great successes of targeted cancer drugs and the promise of novel immunotherapies, the vast majority of people diagnosed with cancer are still first treated with chemotherapy. Now a new study by UCSF researchers using techniques drawn from computational biology could make it much easier for physicians to use the genetic profile of a patient’s tumor to pick the chemotherapy treatment with the fewest side effects and best chance of success.
“Since 95 percent of cancer patients still get chemo, we realized we could make a major impact on cancer treatment by helping clinicians prescribe the right chemotherapy drug,” said Sourav Bandyopadhyay, PhD, a professor of bioengineering and therapeutic sciences in UCSF’s Schools of Pharmacy and Medicine and senior author on the new study.
Sourav Bandyopadhyay, PhD, a professor of bioengineering and therapeutic sciences and senior author of the study.Chemotherapies are potent toxins delivered into the bloodstream to kill tumor cells throughout the body by damaging DNA in rapidly dividing cells. However, these poisons can also do significant harm to other dividing cells such as those found in the stomach lining and in hair and nail follicles, as well as the blood and immune stem cells in the bone marrow. In addition, cancer cells’ susceptibility to these agents varies widely, and tumors often develop resistance to drugs that initially seem effective.
There are more than 100 chemotherapy agents in wide use, but oncologists have very little information to guide their decisions about which of these drugs to use in a given patient. These decisions are typically guided by the drugs’ average historical success rate for different types of cancer, rather than any understanding of how the chemotherapy drug will interact with the genetic profile of a specific tumor.
“We know very little about how gene mutations in tumor cells can change how a tumor might respond or not …