Single-Cell Heterogeneity of EGFR Pathway Is Linked to Unique Signatures of Drug Response and Malignancy in Patient Derived Glioblastoma Stem Cells.

Purpose: In glioblastoma, the therapeutically intractable and resistant phenotypes can be derived from glioma stem cells, which often have different underlying mechanisms from non-stem glioma cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR pathways have been shown as common drivers of glioblastoma. Revealing the inter and intra-cellular heterogeneity within glioma stem cell populations in relations to signaling patterns through these pathways may be key to precision diagnostic and therapeutic targeting of these cells.

Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma.

Genomic profiling often fails to predict therapeutic outcomes in cancer. This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses.

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma.

A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Pre- and post-contrast T-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans.