Cancer is a disease that is not only complex, but also wildly heterogeneous. Current cancer treatments are by and large based on “one size fits all” approach, which often results in poor treatment outcomes. While various sources of patient-derived data, such as molecular diagnostics, histopathology and genetic studies provide a glimpse into the inherent complexity of cancer, we still struggle with making informed choices on which therapy would work best for individual patients. That is why poor treatment efficacy, drug resistance or debilitating side effects are unfortunately all too well-known to us. To make cancer more manageable, combining drugs together is believed to be one of the most promising avenues.
Assistant Professor Jing Tang, a principal investigator of the Network Pharmacology group at FIMM and at the UH Faculty of Medicine has led the development of DrugComb, an open resource for harmonizing cancer drug combination studies. Dr. Tang hopes that DrugComb would become a collaborative data analysis platform that would bring forth more effective and safer cancer treatments.
We aim at personalized drug combinations that maximize the potential to specifically target cancer cells while sparing healthy ones.
- Dr. Jing Tang
To facilitate the discovery of synergistic and effective drug combinations, Dr. Tang and his team developed DrugComb, a web-based data portal which contains screening data for nearly half a million drug combinations tested in a variety of cancer cell lines. Extensive curation allows direct comparison of results across studies, despite different research methodologies and experimental procedures. Furthermore, DrugComb provides computational tools and web server that allow users to analyze and visualize drug combination screening results.
To be able to annotate drug combinations in terms of their mechanisms of action, DrugComb is linked with other biological and chemical databases, such as STITCH for exploring molecular interactions, ChEMBL and PubChEM for drug-target and drug-structure predictions.
The life cycle of drug combination screen data in DrugComb.
"Putting all the pieces together is not a simple task”, explains Bulat Zagidullin, a lead author of the study. “We hope that all these components could help cancer researchers determine the most promising drug combinations that can subsequently be tested in follow up studies.”
Such a data harmonization effort is a difficult task for a single research group or even an institute, That is why DrugComb has been designed in the first place to enable a crowdsourcing workflow.
“We engineered data submission tools to encourage users to share and redistribute their data in a standardized manner. We fully support FAIR (findable, assessable, interoperable and reusable) Data Principles, so as to enhance the practical use of DrugComb data”, explains Jehad Aldahdooh, another lead author of the project.
Ongoing developments are two-fold: authors continuously seek new drug combination screen data and extensive efforts are put into engineering efficient statistical and machine learning methods to predict and identify previously untested synergistic drug combinations.
“Making a data portal is just a beginning. We aim to keep on further developing DrugComb into an online platform to predict, test and understand drug combinations. Not only for cancer cell lines but also for patient-derived samples, so that it may lead to novel, more effective and safe treatments compared to the current cytotoxic and single-targeted therapies.”
Read more about the Network pharmacology for precision medicine group.
DrugComb: an integrative cancer drug combination data portal. Bulat Zagidullin, Jehad Aldahdooh, Shuyu Zheng, Wenyu Wang, Yinyin Wang, Joseph Saad, Alina Malyutina, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto Pessia and Jing Tang. Nucleic Acids Research Webserver Issue 2019.
European Research Council (ERC) starting grant DrugComb (Informatics approaches for the rational selection of personalized cancer drug combinations) [No. 716063]; European Commission H2020 EOSC-life (Providing an open collaborative space for digital biology in Europe [No. 824087]; Academy of Finland Research Fellow grant [No. 317680]; China Scholarship Council grant [No. 201706740080]; Finland's EDUFI Fellowship [No. TM-18-10928]