Behind enemy lines

Chemotherapy is modern medicine’s first line of defense against cancer: chemotherapy drugs kill dividing cells by damaging DNA, preventing tumor cells from multiplying and tumors themselves from growing. To pick which chemotherapy regimens to use, oncologists rely on broad guidelines, based on the average success rates of chemotherapies. But these guidelines don’t take into account the genetic diversity of tumors, which can make some tumors drug resistant.

New research in the lab of Sourav Bandyopadhyay, PhD, promises to take the guesswork out of matching individual cancer cases to the most effective choices of chemotherapy. The study, published yesterday in Cell Reports, systematically tested the impact of dozens of cancer treatments on hundreds of cancer cell lines, allowing the researchers to identify mutations that made certain cancer lines resistant to different chemotherapies.

Bandyopadhyay and his colleagues used their findings to accurately predict which tumors would exhibit drug resistance in a human clinical trial of a particular class of chemotherapy drugs.

“We know very little about how gene mutations in tumor cells can change how a tumor might respond, or not, to certain chemotherapy drugs,” Bandyopadhyay told the UCSF News Center. “Mapping these sorts of connections could make it possible to optimize which drugs patients get, based on their tumor genetics.”

A faculty member in the Department of Bioengineering and Therapeutic Sciences, a joint department of the UCSF Schools of Pharmacy and Medicine, Bandyopadhyay is also a member of the UCSF Helen Diller Family Comprehensive Cancer Center and the Quantitative Biosciences Institute.

Bandyopadhyay’s team developed an automated system for testing 23 FDA-approved chemotherapies and 8 other cancer treatments against over 600 cell lines, each bearing genetic mutations related to human breast or ovarian cancers, or to DNA repair (which often is disrupted in cancer). The group systematically mixed-and-matched each treatment with each cancer line, resulting in 80,000 experiments that were automatically monitored by a custom microscope to determine the outcome of each drug/mutation combination.

The results were combined to produce a map of gene-drug interactions that could someday help guide clinicians to the most effective chemotherapy regimen for each patient, using a tumor’s genetic profile. As a proof of principle, Bandyopadhyay collaborated with a Colorado-based biotech company, Clovis Oncology, to show that this map could predict which patients with ovarian cancer would develop resistance to a particular type of chemotherapy, in a stage II clinical trial.

The findings have been made publicly available for use by scientists and clinicians. Bandyopadhyay is hopeful that an improved understanding of the biology behind drug resistance in cancer cell lines will soon lead to more targeted chemotherapies for cancer patients.

Other authors on the study included Xin Zhao, PhD; Swati Kaushik, PhD; Antoine Barthelet; and Khyati N. Shah, PhD, of UCSF; and Lilliane Robillard, PhD; Kevin K. Lin, PhD; Andy D. Simmons, PhD; Mitch Raponi, PhD; and Thomas C. Harding, PhD, of Clovis Oncology.

The study was funded by the National Cancer Institute (U01CA168370), the UCSF Program in Breakthrough Biomedical Research (PBBR) and the UCSF Breast Oncology SPORE development award.

Conflict of Interest Statement: Co-authors Robillard, Lin, Simmons, Raponi, and Harding are employees of Clovis Oncology.

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