A new study systematically mapped connections between 625 breast and ovarian cancer genes and nearly every FDA-approved chemotherapy for breast or ovarian cancer.
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.
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 to certain chemotherapy drugs. Mapping these sorts of connections could make it possible to optimize which drugs patients get based on their tumor genetics,” said Bandyopadhyay, a member of the UCSF Helen Diller Family Comprehensive Cancer Center and the Quantitative Biosciences Institute.
Now — in a paper published online April 17, 2018 in Cell Reports — Bandyopadhyay’s lab has systematically mapped connections between 625 breast and ovarian cancer genes and nearly every FDA-approved chemotherapy for breast or ovarian cancer. Led by Hsien-Ming “Kevin” Hu, PhD, Bandyopadhyay’s group developed a high-throughput combinatorial approach that allowed them to perform 80,000 experiments in laboratory dishes in a matter of weeks. The authors said their results, which they have made publicly available, constitute an invaluable resource to help clinicians predict which chemotherapies will be most effective against tumor cells with particular genetic mutations, and how to rationally combine therapies to prevent cancers from developing resistance.
“We’re trying to take a systems view of chemotherapy resistance,” Bandyopadhyay said. “With rarer mutations in particular there aren’t enough patients for large clinical trials to be able to identify biomarkers of resistance, but by considering all the different potential genetic factors that have been identified together in one study, we can robustly predict from experiments in laboratory dishes how cancers with different genetic mutations will respond to different treatments.”
The team began by identifying hundreds of genes frequently mutated in human cancers: 200 implicated in breast cancer, 170 linked to ovarian cancer, and 134 involved in DNA repair, which is compromised in many types of cancer. They then mimicked the effects of such mutations in lab dishes by systematically inactivating each of these cancer-associated genes in healthy human cells, creating 625 different perturbations that mirrored distinct genetic mutations seen in real breast and ovarian cancers.