DrugComb is an open-access, community-driven data portal where the results of drug combination screening studies for a large variety of cancer cell lines are accumulated, standardized and harmonized. An actively expanding array of data visualization and computational tools is provided for the analysis of drug combination data. All the data and informatics tools are made freely available to a wider community of cancer researchers.

Background

Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinics but so far showed limited efficacy, and we have limited understanding on why certain patients are non-responding. Even when there is an initial treatment response, cancer cells with high mutational potential and functional redundancy can easily develop drug resistance by activation of compensating pathways. To reach effective and sustained clinical responses, we critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance.

The Research Program in Systems Oncology at the Faculty of Medicine and the Individualized Systems Medicine (ISM) platform at FIMM aim to identify novel therapeutic options that are most likely to be translated into clinics. Cancer patient samples are collected from clinics and cultured for drug sensitivity testing and molecular profiling. Since exhaustive experimentation of all the possible drug combinations for each specific cancer type or patient is not possible, computational methods offer the improved efficiency to predict the most potential drug combinations.

Research Strategy

To facilitate the rational design of drug combinations toward a future of truly personalized cancer medicine, we will develop model-based clustering methods for the identification of patient subgroups that require specific treatment (“the right drug to the right patient”). For patients resistant to chemotherapy, we will develop network modelling approaches to predict the most potential drug combinations. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. We will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be modelled in cancer signaling networks to infer their mechanisms of action. Drug combinations with selective efficacy in individual patient samples or sample subgroups will be further translated into treatment options. The proposed drug combination prediction, modelling and testing pipeline has the potential to lead to novel, more effective and safe treatments compared to the current cytotoxic and monotherapies.

References

[1] Zagidullin, B., Aldahdooh, J., Zheng, S., Wang, W., Wang, Y., Saad, J., Malyutina, A., Jafari, M., Tanoli, M., Pessia, A. and Tang. J. DrugComb: an integrative cancer drug combination data portal. Nucleic Acids Research, gkz337, https://doi.org/10.1093/nar/gkz337

[2] Malyutina, A., Majumder, M.M., Wang, W., Pessia, A., Heckman, C. and Tang, J. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. Under Review.

[3] Wang, W., Malyutina, A., Pessia, A., Heckman, C. and Tang, J. Improving the consistency of functional genomics screens using molecular features - a multi-omics, pan-cancer study. Under Review.

[4] Tang, J. (2017) Informatics approaches for predicting, understanding and testing cancer drug combinations. Methods Mol. Biol. 1636:485-506. Book chapter in Kinase Signaling Networks, Springer

[5] Ianevski, A., He, L., Aittokallio, T. and Tang, J. (2017) SynergyFinder: a web application for analyzing drug combination dose-response matrix data. Bioinformatics. 33, 2413-2415

[6] He, L., Kulesskiy, E., Saarela, J., Turunen, L., Wennerberg, K., Aittokallio, T. and Tang, J. (2017) Methods for high-throughput drug combination screening and synergy scoring. Methods Mol. Biol. 1:711:351-398. Book chapter in Cancer Systems Biology, Springer.

[7] Yadav, B., Wenerberg, K., Aittokallio, T. and Tang, J. (2015) Searching for drug synergy in complex dose-response landscapes using an interaction potency model. Comput. Struct. Biotechnol. J. 13, 504-513

[8] Tang, J., Wennerberg, K. and Aittokallio, T. (2015) What is synergy? The Saariselkä agreement revisited. Front. Pharmacol. 6, 181