Anti-CTLA4 monoclonal antibodies: the past and the future in clinical application

Recently, two studies using ipilimumab, an anti-CTLA-4 monoclonal antibody (mab) demonstrated improvements in overall survival in the treatment of advanced melanoma. These studies utilized two different schedules of treatment in different patient categories (first and second line of treatment). However, the results were quite similar despite of different dosage used and the combination with dacarbazine in the first line treatment. We reviewed the result of randomized phase II-III clinical studies testing anti-CTLA-4 antibodies (ipilimumab and tremelimumab) for the treatment of melanoma to focus on practical or scientific questions related to the broad utilization of these products in the clinics. These analyses raised some considerations about the future of these compounds, their potential application, dosage, the importance of the schedule (induction/manteinance compared to induction alone) and their role as adjuvants. Anti-CTLA-4 antibody therapy represents the start of a new era in the treatment of advanced melanoma but we are on the steep slope of the learning curve toward the optimization of their utilization either a single agents or in combination

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe