{"entity": "researcher", "timestamp": "2026-05-15T01:31:31.443Z", "family": "Trac", "given": "Quang Thinh", "initials": "QT", "orcid": "0000-0003-2429-0287", "affiliations": ["Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels v\u00e4g 12A, Stockholm 17177, Sweden."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/043294bad46e4ccaa3d0d7cd43ebdccd.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/043294bad46e4ccaa3d0d7cd43ebdccd"}}, "publications": [{"entity": "publication", "iuid": "5753418da8414fd289afbad0fb47aa21", "links": {"self": {"href": "https://publications.scilifelab.se/publication/5753418da8414fd289afbad0fb47aa21.json"}, "display": {"href": "https://publications.scilifelab.se/publication/5753418da8414fd289afbad0fb47aa21"}}, "title": "Pathway activation model for personalized prediction of drug synergy.", "authors": [{"family": "Trac", "given": "Quang Thinh", "initials": "QT", "orcid": "0000-0003-2429-0287", "researcher": {"href": "https://publications.scilifelab.se/researcher/043294bad46e4ccaa3d0d7cd43ebdccd.json"}}, {"family": "Huang", "given": "Yue", "initials": "Y"}, {"family": "Erkers", "given": "Tom", "initials": "T"}, {"family": "\u00d6stling", "given": "P\u00e4ivi", "initials": "P"}, {"family": "Bohlin", "given": "Anna", "initials": "A"}, {"family": "Osterroos", "given": "Albin", "initials": "A"}, {"family": "Vesterlund", "given": "Mattias", "initials": "M", "orcid": "0000-0001-9471-6592", "researcher": {"href": "https://publications.scilifelab.se/researcher/0942e438993b494db2a3db914852c808.json"}}, {"family": "Jafari", "given": "Rozbeh", "initials": "R"}, {"family": "Siavelis", "given": "Ioannis", "initials": "I"}, {"family": "Backvall", "given": "Helena", "initials": "H"}, {"family": "Kiviluoto", "given": "Santeri", "initials": "S"}, {"family": "Orre", "given": "Lukas", "initials": "L"}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}, {"family": "Lehti\u00f6", "given": "Janne", "initials": "J", "orcid": "0000-0002-8100-9562", "researcher": {"href": "https://publications.scilifelab.se/researcher/8406a97bac744a59b1bc951978994581.json"}}, {"family": "Lehmann", "given": "Soren", "initials": "S"}, {"family": "Kallioniemi", "given": "Olli", "initials": "O"}, {"family": "Pawitan", "given": "Yudi", "initials": "Y"}, {"family": "Vu", "given": "Trung Nghia", "initials": "TN", "orcid": "0000-0001-7945-5750", "researcher": {"href": "https://publications.scilifelab.se/researcher/d90993bc42694d969a24a50f21393b76.json"}}], "type": "journal article", "published": "2025-06-03", "journal": {"title": "Elife", "issn": "2050-084X", "volume": "13", "issn-l": "2050-084X"}, "abstract": "Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce Drug synergy Interaction Prediction (DIPx), an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge human cell-line dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the model's ability to handle novel combinations. The Spearman's correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.", "doi": "10.7554/eLife.100071", "pmid": "40459126", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC12133153"}, {"db": "pii", "key": "100071"}], "notes": [], "created": "2025-11-28T10:54:44.321Z", "modified": "2025-11-28T10:54:44.431Z"}, {"entity": "publication", "iuid": "90f4fb69f7bd4582a7c2990e09b12c05", "links": {"self": {"href": "https://publications.scilifelab.se/publication/90f4fb69f7bd4582a7c2990e09b12c05.json"}, "display": {"href": "https://publications.scilifelab.se/publication/90f4fb69f7bd4582a7c2990e09b12c05"}}, "title": "Discovery of druggable cancer-specific pathways with application in acute myeloid leukemia.", "authors": [{"family": "Trac", "given": "Quang Thinh", "initials": "QT", "orcid": "0000-0003-2429-0287", "researcher": {"href": "https://publications.scilifelab.se/researcher/043294bad46e4ccaa3d0d7cd43ebdccd.json"}}, {"family": "Zhou", "given": "Tingyou", "initials": "T"}, {"family": "Pawitan", "given": "Yudi", "initials": "Y", "orcid": "0000-0003-0324-7052", "researcher": {"href": "https://publications.scilifelab.se/researcher/095052d8ea7c480b9c32b373795465b4.json"}}, {"family": "Vu", "given": "Trung Nghia", "initials": "TN", "orcid": "0000-0001-7945-5750", "researcher": {"href": "https://publications.scilifelab.se/researcher/d90993bc42694d969a24a50f21393b76.json"}}], "type": "journal article", "published": "2022-09-29", "journal": {"title": "Gigascience", "issn": "2047-217X", "volume": "11", "issn-l": "2047-217X"}, "abstract": "An individualized cancer therapy is ideally chosen to target the cancer's driving biological pathways, but identifying such pathways is challenging because of their underlying heterogeneity and there is no guarantee that they are druggable. We hypothesize that a cancer with an activated druggable cancer-specific pathway (DCSP) is more likely to respond to the relevant drug. Here we develop and validate a systematic method to search for such DCSPs, by (i) introducing a pathway activation score (PAS) that integrates cancer-specific driver mutations and gene expression profile and drug-specific gene targets, (ii) applying the method to identify DCSPs from pan-cancer datasets, and (iii) analyzing the correlation between PAS and the response to relevant drugs. In total, 4,794 DCSPs from 23 different cancers have been discovered in the Genomics of Drug Sensitivity in Cancer database and validated in The Cancer Genome Atlas database. Supporting the hypothesis, for the DCSPs in acute myeloid leukemia, cancers with higher PASs are shown to have stronger drug response, and this is validated in the BeatAML cohort. All DCSPs are publicly available at https://www.meb.ki.se/shiny/truvu/DCSP/.", "doi": "10.1093/gigascience/giac091", "pmid": "36173247", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC9520771"}, {"db": "pii", "key": "6730547"}], "notes": [], "created": "2023-11-27T21:53:02.313Z", "modified": "2024-01-16T13:48:34.955Z"}]}