{"entity": "researcher", "timestamp": "2026-08-28T00:26:36.100Z", "family": "Chantzi", "given": "Efthymia", "initials": "E", "orcid": "0000-0002-8312-4572", "affiliations": ["Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden. efthymia.chantzi@medsci.uu.se."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/aa492934bb4543e086c489a767da32d8.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/aa492934bb4543e086c489a767da32d8"}}, "publications": [{"entity": "publication", "iuid": "08d1593edce24a8a8bbcf9eada767eb4", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/08d1593edce24a8a8bbcf9eada767eb4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/08d1593edce24a8a8bbcf9eada767eb4"}}, "title": "COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining.", "authors": [{"family": "Chantzi", "given": "Efthymia", "initials": "E", "orcid": "0000-0002-8312-4572", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/aa492934bb4543e086c489a767da32d8.json"}}, {"family": "Jarvius", "given": "Malin", "initials": "M"}, {"family": "Niklasson", "given": "Mia", "initials": "M"}, {"family": "Segerman", "given": "Anna", "initials": "A"}, {"family": "Gustafsson", "given": "Mats G", "initials": "MG"}], "type": "journal article", "published": "2019-06-04", "journal": {"title": "BMC Bioinformatics", "issn": "1471-2105", "volume": "20", "issue": "1", "pages": "304", "issn-l": "1471-2105"}, "abstract": "Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated.\n\nMotivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects. COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells.\n\nCOMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases.", "doi": "10.1186/s12859-019-2908-0", "pmid": "31164078", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC6549340"}, {"db": "pii", "key": "10.1186/s12859-019-2908-0"}], "notes": [], "created": "2026-08-21T12:37:34.390Z", "modified": "2026-08-21T12:37:34.436Z"}, {"entity": "publication", "iuid": "d85b51d533f241bcb7c98ccea4c41de3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d85b51d533f241bcb7c98ccea4c41de3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d85b51d533f241bcb7c98ccea4c41de3"}}, "title": "COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies.", "authors": [{"family": "Chantzi", "given": "Efthymia", "initials": "E", "orcid": "0000-0002-8312-4572", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/aa492934bb4543e086c489a767da32d8.json"}}, {"family": "Jarvius", "given": "Malin", "initials": "M"}, {"family": "Niklasson", "given": "Mia", "initials": "M"}, {"family": "Segerman", "given": "Anna", "initials": "A"}, {"family": "Gustafsson", "given": "Mats G", "initials": "MG"}], "type": "journal article", "published": "2018-11-26", "journal": {"title": "BMC Bioinformatics", "issn": "1471-2105", "volume": "19", "issue": "1", "pages": "453", "issn-l": "1471-2105"}, "abstract": "Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements.\n\nCOMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer. This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use.\n\nCOMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.", "doi": "10.1186/s12859-018-2458-x", "pmid": "30477419", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC6257977"}, {"db": "pii", "key": "10.1186/s12859-018-2458-x"}], "notes": [], "created": "2026-08-21T12:37:32.300Z", "modified": "2026-08-21T12:37:32.362Z"}]}