Glioblastoma and Blood Microenvironment Predictive Model for Life Expectancy of Patients.

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 (IC(50)). 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 IC(50) 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: IC(50) 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 < 0.01). Cross k-means validation on three clusters (n = 10) of the model showed that its average accuracy (sensitivity and specificity) for cluster 1 was 74.98%; for cluster 2, it was 66.7%; and for cluster 3, it was 60.0%. At the same time, the differences between clusters 1, 2, and 3 were not significant. The results of the Sobel test show that there are no interactions between the components of the model, and each component is an independent factor influencing the event (life expectancy, survival) of GBM patients. Conclusions: A simple predictive model for GBM patients' life expectancy has been developed using statistical analysis methods.