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
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 hair and nail follicles, as well as the blood and immune stem cells in the bone marrow.
"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.
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, Ph.D., 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.
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.
The researchers then exposed cells from each of these lines to a panel of 31 different drug treatments—including 23 chemotherapy compounds approved by the FDA for breast and ovarian cancers, six targeted cancer drugs, and two standard drug combinations. An automated microscopy system monitored the cells' health and recorded which groups of cells were killed, which survived, and which developed resistance when exposed to a particular treatment.
The resulting "map" of gene-drug interactions allowed the researchers to accurately predict the responses of multiple human cancer cell lines to different chemotherapy agents based on the cell lines' genetic profiles and also revealed new genetic factors that appear to determine the response of breast and ovarian tumor cells to common classes of chemotherapy treatment.
In future, Bandyopadhyay says, better understanding how chemotherapy agents impact specific biological pathways should allow drug trials to focus on patients who are more likely to respond to the drugs being tested and enable clinicians to identify targeted or combination therapies for patients with a genetic predisposition to resistance.