Gene-Drug Interaction Map Developed to Aid Personalized Chemotherapy

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 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.

Researchers at the University of California, San Francisco (UCSF) have generated a detailed, quantitative gene-drug interaction map as an open resource that could help clinicians prescribe the most effective type of chemotherapy for each cancer patient, based on the tumor’s genetic profile.

Reporting on the map in Cell Reports, the researchers, led by Sourav Bandyopadhyay, Ph.D., a professor of bioengineering and therapeutic sciences at UCSF’s Schools of Pharmacy and Medicine, describe how they generated the resource by systematically knocking down each of 625 different DNA repair and cancer-relevant genes in cultured cells, and assessing how the cells responded to a wide range of chemotherapy drugs.

The UCSF scientists say that as well as allowing researchers to predict how genetically defined human cancer cell lines respond different chemotherapy agents, the map has uncovered new genetic factors that appear to determine how breast and ovarian tumors respond to common chemotherapy drug classes. As proof of principle, they used the gene-drug interaction map to identify two gene mutations that appeared to contribute to ovarian cancer resistance to poly(ADP-ribose) polymerase (PARP) inhibitors, and confirmed their finding in patients participating in a clinical trial.

“We know very little about how gene mutations in tumor cells can change how a tumor might respond or not to certain chemotherapy drugs,” says Dr. Bandyopadhyay, a member of the UCSF Helen Diller Family Comprehensive Cancer Center and the Quantitative Biosciences Institute. “We’re trying to take a systems view of chemotherapy resistance.…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 UCSF team published its work in a paper entitled “A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance.”

The vast majority of cancer patients receive chemotherapy, but the decision on which of the more than 100 chemotherapy agents to use is often based on historical average response rates, rather than on an understanding of genetic factors that may impact on treatment effectiveness or tumor resistance, the authors explain. “…choosing from multiple possible chemotherapy options can complicate clinical decision making,” they write. “Therefore, optimizing the use of chemotherapies is a significant and pressing challenge in precision oncology.”

As a starting point for deriving their gene–chemotherapy drug interaction map, the researchers looked to existing databases to gather a set of common tumor suppressor genes, together with more than 200 breast cancer and 170 ovarian cancer genes, and 134 genes known to be involved in DNA repair. Each of the genes was then systematically knocked out in healthy human cells, and the resulting cell lines exposed to a panel of 29 different drugs, including “nearly all FDA-approved chemotherapies for breast and ovarian cancer, four PARP inhibitors, and two other targeted therapies,” as well as two common drug combinations. The treated cells were monitored using an automated microscopy system to identify cell survival, death, and the development of resistance in response to each chemotherapy drug, or the two-drug combinations.

The resulting map predicted cancer cell line response to different chemotherapeutic agents, identified drugs with similar mechanisms of action, and identified interactions that could be recapitulated with small-molecule inhibitors of specific targets, “revealing synergistic drug combinations,” the authors claim. As an initial proof of principle, they used the map to uncover a predicted relationship between alterations in two genes, ARID1A and GPBP1, and ovarian cancer resistance to PARP inhibitor therapy. Through a collaboration with Clovis Oncology, they were the able to confirm that ovarian cancer patients in the firm’s Phase II PARP inhibitor trial were, as predicted, more likely to develop resistance to therapy if they carried mutations in the same two genes. “We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination,” they write.

The team is making their chemical–genetic interaction map publicly available, with the hope that it will provide valuable new insights into the biological basis for chemotherapy success and failure, and potentially help researchers identify effective new chemotherapy combinations against tumors with specific genetic signatures.

“This work highlights the utility of a systematic chemical–genetic interaction map as a resource for the identification of clinically relevant biomarkers of drug susceptibility, as well as a foundation for integration with other cancer datasets to enhance drug and biomarker development,” the authors write. “This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. In contrast to most standard genetic screens, this approach provides a quantitative readout that approximates genetic interaction strength and allows for the comparison of responses across many drugs.”