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Identifier 000419452
Title In silico tumor growth validation based on human brain cancer models.
Alternative Title Επαλήθευση της υπολογιστικής προσομοίωσης ανάπτυξης του όγκου βασισμένη σε μοντέλα για καρκίνο εγκεφάλου ανθρώπου
Author Ωραιοπούλου, Μαριάμ-Ελένη
Thesis advisor Βακής. Αντώνιος
Reviewer Σάκκαλης, Ευάγγελος
Μαυρουδής, Δημήτριος
Παπαματθαϊάκης, Ιωσήφ
Ζαχαράκης, Ιωάννης
Χαραλαμπόπουλος, Ιωάννης
Στυλιανόπουλος, Τριαντάφυλλος
Abstract Glioblastoma (GB) is the most malignant brain cancer and is not considered a curable disease so far. A multidisciplinary framework that integrates basic and translational research is presented targeting both the validation of computer-based predictions of GB growth progress, while attempting a better understanding of its pathophysiology. In this attempt, a carefully planned combination of in vitro, in vivo and in silico experimental approaches were mobilized. Specifically, patient-specific cell cultures were established and used in experimental assays to assess GB pathophysiologic factors and parametrize/initialize/validate the computational predictive algorithms, accordingly. Focusing on proliferation, the combined in vitro-in silico approach supported that the intra-tumoral heterogeneity together with the overall proliferation reflected in both the proliferation rate and the mechanical cell contact inhibition, but not the cell size, can predict the evolution of different GB cell lines. Focusing on invasion, we showed that the primary GB spheroids adopt a novel, cohesive pattern mimicking perivascular invasion in the brain, while the U87MG and the T98G adopt a typical, starburst, invasive pattern, under the same 3D in vitro experimental setup. Our proposed mathematical approach suggested that allowing phenotypic heterogeneity within the tumor population is sufficient for variable invasive morphologies to emerge. GB adjuvant chemotherapy includes Temozolomide (TMZ); yet, not all patients are responsive. The latest trends in GB clinical trials usually refer to Doxorubicin (DOX); yet, it is unable to adequately overpass the blood brain barrier. A range of TMZ and DOX concentrations were used to treat the primary GB spheroids based on the IC50 values previously estimated in 2D. The effective concentrations of DOX and TMZ exhibit four orders of magnitude difference. In addition, we used Light Sheet Fluorescence imaging to visualize the drug penetration and necrosis. We observed that DOX was able to accumulatively cause necrosis, while in the TMZ-treated spheroids slight growth-inhibiting effects were observed in a non-consistent dose-response relationship. We have indications regarding the option of a TMZ-DOX therapeutic scheme to disable proliferation and increase cytotoxicity. An in vitro drug screening tool was proposed, while we followingly suggest to extend these observations to the hybrid discrete-continuous model. Overall, in this PhD thesis, we conclude that future research should be based on primary cells and that common cell lines should only serve as landmarks between different groups. Computational models may serve as predictor tools not only for estimating cancer progress, but also for designing targeted biological experiments and allow a better understanding of the involved biological phenomena. Simulations of cancer progress should not anymore be based on theoretical values, especially if clinical translation is of interest.
Language English
Subject Glioblastoma
Predictive algorithms
Primary cell cultures
Translational research
Μεταφραστική έρευνα
Προβλεπτικοί αλγόριθμοι
Πρωτογενείς κυτταροκαλλιέργειες
Φυσιολογία καρκίνου εγκεφάλου
Issue date 2018-12-05
Collection   School/Department--School of Medicine--Department of Medicine--Doctoral theses
  Type of Work--Doctoral theses
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