Glioblastoma and Blood Microenvironment Predictive Model for Life Expectancy of Patients
Alexander N. Chernov,
Sofia S. Skliar,
Mikalai M. Yatskou,
Victor V. Skakun,
Sarng S. Pyurveev,
Ekaterina G. Batotsyrenova,
Sergey N. Zheregelya,
Guodong Liu,
Vadim A. Kashuro,
Dmitry O. Ivanov,
Sergey D. Ivanov
Affiliations
Alexander N. Chernov
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Sofia S. Skliar
Laboratory of Neurooncology of Polenov Neurosurgical Institute, Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia
Mikalai M. Yatskou
Department of System Analysis and Computer Modeling, Belarussian State University, 220030 Minsk, Belarus
Victor V. Skakun
Department of System Analysis and Computer Modeling, Belarussian State University, 220030 Minsk, Belarus
Sarng S. Pyurveev
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Ekaterina G. Batotsyrenova
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Sergey N. Zheregelya
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Guodong Liu
Department of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Vadim A. Kashuro
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Dmitry O. Ivanov
Biological Chemistry Department, Federal State Budgetary Educational Institution of Higher Education Saint Petersburg State Pediatric Medical University of the Ministry of Health of Russia, 194100 Saint Petersburg, Russia
Sergey D. Ivanov
Federal State Budgetary Institution “National Medical Research Center of Oncology named after N.N. Petrov” of the Ministry of Health of the Russian Federation, 197758 Saint Petersburg, Russia
Background: Glioblastoma multiforme (GBM) is a very malignant brain tumor. GBM exhibits cellular and molecular heterogeneity that can be exploited to improve patient outcomes by individually tailoring chemotherapy regimens. Objective: Our objective was to develop a predictive model of the life expectancy of GBM patients using data on tumor cells’ sensitivity to chemotherapy drugs, as well as the levels of blood cells and proteins forming the tumor microenvironment. Methods: The investigation included 31 GBM patients from the Almazov Medical Research Centre (Saint Petersburg, Russia). The cytotoxic effects of chemotherapy drugs on GBM cells were studied by an MTT test using a 50% inhibitory concentration (IC50). We analyzed the data with life expectancy by a one-way ANOVA, principal component analysis (PCA), ROC, and Kaplan–Meier survival tests using GraphPad Prism and Statistica 10 software. Results: We determined in vitro the IC50 of six chemotherapy drugs for GBM and 32 clinical and biochemical blood indicators for these patients. This model includes an assessment of only three parameters: IC50 of tumor cells to carboplatin (CARB) higher than 4.115 μg/mL, as well as levels of band neutrophils (NEUT-B) below 2.5% and total protein (TP) above 64.5 g/L in the blood analysis, which allows predicting with 83.3% probability (sensitivity) the life expectancy of patients for 15 months or more. In opposite, a change in these parameters—CARB above 4115 μg/mL, NEUT-B below 2.5%, and TP above 64.5 g/L—predict with 83.3% probability (specificity) no survival rate of GBM patients for more than 15 months. The relative risk for CARB was 6.41 (95 CI: 4.37–8.47, p = 0.01); for NEUT-B, the RR was 0.40 (95 CI: 0.26–0.87, p = 0.09); and for TP, it was 2.88 (95 CI: 1.57–4.19, p = 0.09). Overall, the model predicted the risk of developing a positive event (an outcome with a life expectancy more than 10 months) eight times (95 CI 6.34–9.66, p Conclusions: A simple predictive model for GBM patients’ life expectancy has been developed using statistical analysis methods.
