Periodic Reporting for period 4 - DrugComb (Informatics approaches for the rational selection of personalized cancer drug combinations)
Período documentado: 2021-12-01 hasta 2023-05-31
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 2.6 billion euros for a drug to reach the market. Therefore, the current treatment efficacy is largely short-term. 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. Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity, and prevent the emergence of drug resistance. However, the discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, the 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 (ii) 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.
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. Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity, and prevent the emergence of drug resistance. However, the discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, the 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 (ii) 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.
1) We have developed a web portal called DrugTargetCommons (http://drugtargetcommons.fimm.fi/) to manually curate and annotate drug-target activity data from publications [1-2]. The drug-target activity data has been applied in the recent DREAM Challenge to predict drug targets and drug sensitivities for cancer cell lines [3].
2) We have developed a network pharmacology approach that utilizes dynamic modeling of signaling pathways [4]. Furthermore, we have developed a computational tool called CES (Combined Essentiality Score) to predict the potential drug targets from functional genetic screens [5].
3) To make the drug combination screening data FAIR (Findable, Accessible, Interoperable, and Reusable), we have developed a web portal called DrugComb (https://drugcomb.fimm.fi/) [6]. We have developed a CSS score to evaluate the drug combination sensitivity [7]. To evaluate the statistical significance of drug response data, we have developed a numerical method to evaluate the transition probability of cell growth using birth-and-death models [8].
4) Together with my collaborators in the clinics, we have successfully applied these computational tools to evaluate the potential drug combinations for cancer patients, e.g. in T-PLL (T cell prolymphocytic leukemia) [9] and ovarian cancer patient samples [10].
5) More recently, we have developed AI-based text-mining approaches to mine the literature for extracting experimental drug-target interaction data [11]. We have also developed a data integration platform called MICHA for the FAIRification of drug sensitivity screening data [12]. We have successfully updated the DrugComb data portal with more comprehensive drug sensitivity screening data, not only for cancers but also for other diseases such as COVID-19 [13]. We have updated the SynergyFinder tool to allow the analysis of higher-order drug combinations with statistical significance testing [14]. We have also participated in multiple collaborative projects that involve the prediction and testing of drug combinations [15].
Taken together, we have successfully achieved the scientific goals of DrugComb by developing informatics approaches for predicting, understanding, and testing personalized drug combinations in cancer. All the methods are offered with open-source tools that are frequently used by life science researchers.
References:
[1] Tang et al. https://doi.org/10.1016/j.chembiol.2017.11.009 [2] Tanoli et al. https://doi.org/10.1093/database/bay083 [3] Douglass et al. https://doi.org/10.1016/j.xcrm.2021.100492 [4] Tang et al. https://doi.org/10.1038/s41540-019-0098-z [5] Wang et al. https://doi.org/10.1016/j.ebiom.2019.10.051 [6] Zagidullin et al. https://doi.org/10.1093/nar/gkz337. [7] Malyutina et al. https://doi.org/10.1371/journal.pcbi.1006752 [8] Pessia and Tang https://doi.org/10.1007/s10543-020-00836-x [9] He et al. https://doi.org/10.1158/0008-5472.CAN-17-3644 [10] He et al. https://doi.org/10.1093/bib/bbab272 [11] Aldahdooh et al. https://doi.org/10.1186/s12859-022-04768-x [12] Tanoli et al. https://doi.org/10.1093/bib/bbab350. [13] Zheng et al. https://doi.org/10.1093/nar/gkab438 [14] Zheng et al. https://doi.org/10.1016/j.gpb.2022.01.004 [15] Jafari et al. https://doi.org/10.1038/s41467-022-29793-5.
2) We have developed a network pharmacology approach that utilizes dynamic modeling of signaling pathways [4]. Furthermore, we have developed a computational tool called CES (Combined Essentiality Score) to predict the potential drug targets from functional genetic screens [5].
3) To make the drug combination screening data FAIR (Findable, Accessible, Interoperable, and Reusable), we have developed a web portal called DrugComb (https://drugcomb.fimm.fi/) [6]. We have developed a CSS score to evaluate the drug combination sensitivity [7]. To evaluate the statistical significance of drug response data, we have developed a numerical method to evaluate the transition probability of cell growth using birth-and-death models [8].
4) Together with my collaborators in the clinics, we have successfully applied these computational tools to evaluate the potential drug combinations for cancer patients, e.g. in T-PLL (T cell prolymphocytic leukemia) [9] and ovarian cancer patient samples [10].
5) More recently, we have developed AI-based text-mining approaches to mine the literature for extracting experimental drug-target interaction data [11]. We have also developed a data integration platform called MICHA for the FAIRification of drug sensitivity screening data [12]. We have successfully updated the DrugComb data portal with more comprehensive drug sensitivity screening data, not only for cancers but also for other diseases such as COVID-19 [13]. We have updated the SynergyFinder tool to allow the analysis of higher-order drug combinations with statistical significance testing [14]. We have also participated in multiple collaborative projects that involve the prediction and testing of drug combinations [15].
Taken together, we have successfully achieved the scientific goals of DrugComb by developing informatics approaches for predicting, understanding, and testing personalized drug combinations in cancer. All the methods are offered with open-source tools that are frequently used by life science researchers.
References:
[1] Tang et al. https://doi.org/10.1016/j.chembiol.2017.11.009 [2] Tanoli et al. https://doi.org/10.1093/database/bay083 [3] Douglass et al. https://doi.org/10.1016/j.xcrm.2021.100492 [4] Tang et al. https://doi.org/10.1038/s41540-019-0098-z [5] Wang et al. https://doi.org/10.1016/j.ebiom.2019.10.051 [6] Zagidullin et al. https://doi.org/10.1093/nar/gkz337. [7] Malyutina et al. https://doi.org/10.1371/journal.pcbi.1006752 [8] Pessia and Tang https://doi.org/10.1007/s10543-020-00836-x [9] He et al. https://doi.org/10.1158/0008-5472.CAN-17-3644 [10] He et al. https://doi.org/10.1093/bib/bbab272 [11] Aldahdooh et al. https://doi.org/10.1186/s12859-022-04768-x [12] Tanoli et al. https://doi.org/10.1093/bib/bbab350. [13] Zheng et al. https://doi.org/10.1093/nar/gkab438 [14] Zheng et al. https://doi.org/10.1016/j.gpb.2022.01.004 [15] Jafari et al. https://doi.org/10.1038/s41467-022-29793-5.
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 of 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. Secondly, while 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 informatics approaches to understand drug-target interaction 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. 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. The project as a whole is expected to provide widely applicable research tools, helping to fill an important gap in personalized medicine. All the informatics tools will be available in the DrugComb data portal at: https://drugcomb.fimm.fi/ as well as the SynergyFinderPlus web tool: http://synergyfinder.org.