New study conducted by USCF maps the links between 625 genes and various different chemotherapy treatments
Despite successes of targeted cancer drugs and the promise of novel immunotherapies, the vast majority of people diagnosed with cancer are treated with chemotherapy. Now a new study by UCSF researchers using techniques drawn from computational biology could make it easier for physicians to use the patient’s genetic profile of the tumour to pick the chemotherapy treatment with the less number of side effects and highest chance of success.
Chemotherapies are potent toxins delivered into the bloodstream to kill tumour 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 can vary widely, and tumours often develop resistance to drugs that initially seem effective.
There are currently more than 100 chemotherapy agents in wide use, but oncologists have minimal information to guide 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, as opposed to any understanding of how the chemotherapy drug will interact with the genetic profile of a specific tumour.
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.
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 common 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 tumour cells to common classes of chemotherapy treatment.
As a proof of principle, the researchers collaborated with Clovis Oncology, a biotech company based in Boulder, Colorado, which is running a clinical trial of drugs known as PARP inhibitors in patients with stage II ovarian cancer. Based on their gene-drug interaction map, the researchers predicted that mutations in two genes, called ARID1A and GPBP1, could contribute to ovarian cancer’s ability to develop resistance to this class of drugs. Results from the clinical trial bore out these predictions: patients with these mutations were significantly more likely to develop resistance.
Bandyopadhyay’s team has deposited the trove of data generated in the new study in a database maintained by the National Cancer Institute so that other researchers can mine it for information about drug combinations and derive new biological insights about the basis for chemotherapy’s success or failure.
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.