Abstract 3646: DeepSynBa: A deep learning method to improve drug combination predictions using full dose-response matrices and contextual features of cell lines and cancer indications

Abstract Introduction: Many cancer monotherapies have limited activity in clinic, making combinations a relevant treatment strategy. The number of possible combinations is vast, and the responses can be context-specific, making it challenging to predict combination effects. Existing computational models typically predict a single aggregated synergy score, i.e. Bliss or Loewe, for a given drug combination. However, these approaches exhibit high prediction uncertainty and limited actionability because they fail to differentiate between potency and efficacy by oversimplifying the drug-response surface using a single synergy score. To address these limitations, we introduce DeepSynBa, which models the full dose-response matrix of drug pairs rather than an aggregated synergy score. DeepSynBa formulates this task as a regression problem and uses cell line-specific features and drug embeddings to predict the entire drug-response matrix within a deep learning framework. Following SynBa’s approach of modelling the dose-response surface [1], DeepSynBa includes an intermediate layer that estimates pharmacological parameters, which are then used to predict dose-response values across dosages. This design also enables post-hoc calculation of traditional synergy scores like Loewe and Bliss, maintaining compatibility with existing synergy predictors. Results: We evaluated DeepSynBa on the NCI-ALMANAC dataset, which contains 104 FDA-approved cancer drugs, 60 cell lines, and nearly 300, 000 drug pair-cell line combinations. DeepSynBa outperforms comboFM [2] and comboLTR [3], two state-of-the-art methods for direct dose-response matrix prediction across multiple evaluation scenarios, such as novel drug combinations, cell lines, and drugs. DeepSynBa achieves RMSE values of 7.72 and 7.71 in the new drug combination and new cell line settings, respectively, which is an improvement of approximately 65% compared to comboFM, the next-best model. DeepSynBa also consistently attained lower RMSE and higher Pearson and Spearman correlations across nine tissue types including breast, colon, and leukemia. Conclusions: DeepSynBa provides a more accurate framework for predicting the full dose-response matrix than existing methods. Additionally, DeepSynBa supports experimental design by providing detailed predictions of drug combination responses across dosages. For untested combinations, by utilizing the parameter layer which separates efficacy from potency, biologists can select dosage ranges that optimize efficacy while limiting off-target toxicity, hence increasing the likelihood of identifying optimal therapeutic dosages. References: 1.Zhang H. et al. Bioinformatics, 39, 1:i121-i130 (2023).2.Julkunen H. et al. Nature Communications, 11, 1:1-11 (2020).3.Wang T. et al. Bioinformatics, 37, 1:i93-i101(2021). Citation Format: Marta Milo, Haoting Zhang, Halil ibrahim Kuru, A. Ercument Cicek, Oznur Tastan, Carl Henrik Ek, Magnus Rattray. DeepSynBa: A deep learning method to improve drug combination predictions using full dose-response matrices and contextual features of cell lines and cancer indications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3646.