SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
+49 381 498-7571
A systems methodology to assess the risk of tumor relapse in melanoma - MelEVIR
The aim of the project is to develop, test and prepare for translation into clinical practice a systems-biology-based diagnostic tool for assessing the probability of tumor relapse in melanoma patients, based on the profiling of pEVs. In the methodology proposed, in vitro and clinical data are integrated using data-driven mathematical modeling. The insights obtained from patient data analysis, reconstruction of biochemical networks, and model simulations are used to a) select a set of microRNAs, long non-coding RNAs, and proteins present in pEVs of patients to be measured in a blood test as surrogates of immune system activity against MRD and b) assess the probability of tumor relapse in the close future.
In clinical practice, a critical necessity nowadays is the detection of the so-called “minimal residual disease” (MRD) after primary surgery in highly metastatic tumors like melanoma. The MRD consists of residual tumor cells and micro-metastases, which could be dormant for years and even decades before they eventually re-emerge as solid metastases. At present, relapse detection is handled by the assessment of tumor markers. However, these markers are not always specific, and often they are not very sensitive because only sizable tumors secrete enough material for detection. Alternatively, imaging systems like the positron emission tomograph (PET) can detect tumors in the millimeter range, but are expensive and labor-intensive. In preliminary work, we discovered that certain markers in plasma-derived extracellular vesicles (pEVs) have the potential to predict and detect tumor relapse in melanoma. A remarkable finding was that circulating pEVs in cancer patients are released in abundance by the innate immune system, reacting very early and swiftly to the appearance of circulating and disseminated tumor cells. We hypothesize that this information can be used for diagnostic purposes because it can be used to assess the strength and efficacy of the immune response against the MRD.
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An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma
Singh N, Eberhardt M, Wolkenhauer O, Vera J, Gupta SK
BMC Bioinformatics 2020, 21 (1), 1-17
Drug Repositioning Inferred from E2F1-Coregulator Interactions Studies for the Prevention and Treatment of Metastatic Cancers.
Goody D, Gupta SK, Engelmann D, Spitschak A, Marquardt S, Mikkat S, Meier C, Hauser C, Gundlach JP, Egberts JH, Martin H, Schumacher T, Trauzold A, Wolkenhauer O, Logotheti S, Pützer BM.
Theranostics. 2019 Feb 20;9(5):1490-1509.