Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Background: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that the serum white blood cell (WBC) count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of WBC count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum WBC counts. Conclusions: These findings suggest that in a subset of glioblastoma patients the incorporation of WBC count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow the distillation of complex clinical data sets to uncover novel and meaningful clinical relationships.

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. Conclusion: These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.

[10]-Gingerol-Loaded Nanoemulsion and its Biological Effects on Triple-Negative Breast Cancer Cells.

The apoptotic, cytotoxic, and cytostatic activities for [10]-gingerol in triple-negative breast cancer cells (TNBCs) were already reported. However, despite these important antitumor activities, the compound has the disadvantage to have a hydrophobic characteristic, hindering in vivo administration. To surpass this issue, in this study we have created a [10]-gingerol-loaded nanoemulsion (10GNE) in order to increase the stability and solubility of the compound. The nanoemulsion was characterized and tested for its cytotoxic, cytostatic, and apoptotic effects on a panel of murine and human TNBC cell lines, as well as non-tumor cells, and compared with a [10]-gingerol-free nanoemulsion (NE) and with [10]-gingerol itself. Except for the murine 4T1.13 cell line, the IC50 of the free 10G molecule, after 72 h of incubation, was higher in all cell lines tested, both murine and human, demonstrating therefore the efficacy of the 10GNE regarding cytotoxicity. In murine tumor cells, 60 µM 10GNE was able to arrest cell cycle at sub-G0 phase and induce apoptosis, leading to 48% and 78% of total cell death in 4T1.13 and 4T1Br4 murine tumor cells, respectively. This represents an improvement compared to 10G-free molecule that only induced 74% of total apoptosis at 100 µM in 4T1Br4 cells. Taken together, our results show that nanoformulation preserved the [10]-gingerol cytotoxic and cytostatic properties and improved its apoptotic function on murine TNBC cell lines. These data open new perspectives to a more suitable drug-delivery approach for [10]-gingerol for TNBC treatment that should be further demonstrated using in vivo assays.