Targeted therapies and personalized treatments are the most promising assets to treat cancer. However, in many patients, a tumor’s innate or acquired resistance to a given therapy will render the treatment ineffective. To increase therapeutic options and to overcome drug resistance, cancer researchers have been actively investigating drug combinations. The rationale is that by targeting multiple mechanisms simultaneously, the potency of the treatment is increased and tumor cells are less likely to develop resistance and become refractory to treatment.

One of the large hurdles for advancing combination therapy is the selection of patients who are likely to benefit from specific combinations. Many of the single agent drugs under development today are targeted to specific proteins, and are often only effective in tumors with activity in the corresponding pathway. Similarly, the selection of sub- populations using genetic or genomic biomarkers will be critical for the successful administration of combination agents. In the selection of combination treatments, one of the more desirable properties is drug synergy – an exaggerated response over and beyond additive effects. Drug synergism has the potential to increase anti-tumor potency without (necessarily) an attendant increase in toxicity, but is very complex to study due to enormous number of potential combinations and their dosages.

To accelerate the understanding of drug synergy, AstraZeneca has partnered with the European Bioinformatic Institute, the Sanger Institute, Sage Bionetworks, and the distributed DREAM community to launch the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. This Challenge is designed to explore fundamental traits that underlie effective combination treatments and synergistic drug behavior using baseline genomic data, i.e. data collected pretreatment. As the basis of the Challenge, AstraZeneca is releasing ~11.5k experimentally tested drug combinations measuring cell viability over 118 drugs and 85 cancer cell lines (primarily colon, lung, and breast), and monotherapy drug response data for each drug and cell line. Moreover, in coordination with the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Sanger Institute, genomic data including gene expression, mutations (whole exome), copy-number alterations, and methylation data will be released into the public domain and made accessible for Challenge participants through the Challenge data repository.