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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - DrugComb (Informatics approaches for the rational selection of personalized cancer drug combinations)

Teaser

Summary

Each year we spend more than 100 billion euros on cancer medicine, however, the efficacy is far from optimal, as many cancer drugs benefit at most 25% of the patients who take them. Likewise, pharmaceutical companies have tested hundreds of cancer drugs in clinical trials, but more than 90% of them have failed. These disappointing results contribute to a surging cost of 2.5 billion euros for a drug to reach to the market. Therefore, the current treatment efficacy is largely short-term. The society would critically need more effective treatments to ensure a better quality of life for patients while keeping the cost at a sustainable level.
Despite the scientific advances in the understanding of cancer, there remains a major gap between the vast knowledge of molecular biology and effective anticancer treatments. Even when there is an initial treatment response, cancer cells can easily develop drug resistance. To reach effective and sustained clinical responses, many cancer patients who become resistant to standard treatments urgently need multi-targeted drug combinations, which shall effectively inhibit the cancer cells and block the emergence of drug resistance, while incurring minimal side effects on normal cells. Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but a more systems-level approach is needed. This project aims to accelerate the discovery of personalized multi-targeted drug combinations using computational approaches to (i) predict and prioritize the most effective drug combinations and (ii) to evaluate the degree of synergy in the drug combination experiment and (iii) to understand and translate the mechanisms of drug combinations into treatment suggestions for patients. Through my close connections with leading experimental and clinical researchers, the proposed computational analysis pipeline has exceptionally high potential to lead to novel, more effective and safe treatments compared to the current cytotoxic and single-targeted monotherapies.

Work performed

I have written a book chapter to summarize the recent advances in the informatics approaches for drug combination predicting, testing and modeling (Tang, 2017, Publication No.1).

We have developed a series of methods to quantify and visualize the degree of synergies in the experimental data including the ZIP model (Yadav et al., 2015) and more recently the SynergyFinder tool (Ianevski et al., 2017, Publication No. 2). The computational implementations have been made freely available as either web-based applications (https://synergyfinder.fimm.fi/) or external plug-ins in open-source software packages (https://bioconductor.org/packages/release/bioc/html/synergyfinder.html).

To build a consensus knowledgebase for drug-target interactions, we have developed Drug Target Commons (DTC), a community-based crowdsourcing initiative to improve the curation and wider use of drug-target bioactivity data for drug discovery, target identification and repurposing applications. The web-based DTC platform aims to standardize the collection, management, curation and annotation of the notoriously heterogeneous compound-target bioactivity measurements, with the goal to provide the most comprehensive, reproducible and sustainable bioactivity knowledgebase for the end-users. The DTC web platform is up and running (https://drugtargetcommons.fimm.fi) and we have recently published the major updates by including more curated drug target data (Tang et al., 2018, Publication No. 3; Tanoli et al., 2018, Publication No.4).

We have developed a network pharmacology model called TIMMA that utilizes set theory to predict synergistic drug combinations based on monotherapy drug sensitivity data and drug-target interaction data for a given cancer cell type. The TIMMA method has been recently applied to patient derived cancer samples in T-cell prolymphocytic leukemia (T-PLL), by integrating the ex vivo sensitivities of targeted compounds and drug-target interaction data. TIMMA predicted patient-specific drug combinations with high accuracy (He et al., 2018, Publication No. 5).

References:
Yadav, B. et al.(2015) Searching for drug synergy in complex dose-response landscapes using an interaction potency model. Comput. Struct. Biotechnol. J., 13, 504-513
Tang, J. (2017) Informatics approaches for predicting, understanding and testing cancer drug combinations. Methods Mol. Biol. 1636:485-506. Publication No.1
Ianevski, A. et al. (2017) SynergyFinder: a web application for analyzing drug combination dose-response matrix data. Bioinformatics. 33, 2413-2415. Publication No.2
Tang, J. et al. (2018) DrugTargetCommons: a community-effort to build a consensus knowledgebase for drug-target interactions. Cell Chem. Biol. 25, 224-229. Publication No.3
Tanoli, Z.R. et al. (2018) Drug Target Commons 2.0: a community-platform for systematic analysis of drug-target interaction profiles. Database. 2018, bay083. Publication No.4
He, L. et al. (2018) Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients. Cancer Res. 78, 2407-2418. Publication No.5

Final results

This project will develop computational systems pharmacology approaches to predict, test and understand drug combinations that go beyond the state-of-the-art in many ways. First, many new promising targeted therapies fail in clinical trials due to lack of efficacy, most likely because we have limited understanding on which patient subpopulations are expected responders and what are the most predictive molecular biomarkers for treatment response. The state-of-the-art patient stratification is based on either clinical phenotypes or genomics signatures but they do not necessarily predict drug responses. In this project, we have developed computational approaches that utilize ex vivo drug sensitivity data to stratify patient-derived samples (Tang 2017). Secondly, while the next generation sequencing has been very successful at characterizing the heterogeneity associated with each cancer type, these findings often do not lead to therapeutic targets that can be utilized in drug combination strategies. I have developed the informatics approaches to understand drug target interaction (Tang et al., 2018; Tanoli et al., 2018) and utilized such information in network pharmacology models to establish the mechanistic link between cancer genomics and drug sensitivity, based on which we can predict clinically actionable and synergistic drug combinations (He et al., 2018). Thirdly, we have applied the proposed drug combination discovery pipeline not only to the established cancer cell lines, but also to cancer patient samples. We have developed efficient computational tools for evaluating the significance of drug combination experimental data, which is needed for demonstrating that the drug combination predictions can be translated into treatment suggestions (Ianevski et al., 2017). The project as a whole is expected to provide widely applicable research tools, helping to fill an important gap in personalized medicine where the methodological basis for the rational selection of drug combinations is yet to be established. All the informatics tools that are developed in this project will be available in the DrugComb data portal at: https://drugcomb.fimm.fi/.