Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State
Brandon Bumbaca1, Marc R Birtwistle2,3, James M Gallo1
1Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA.
Abstract:
Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.
Insights
Glioblastoma Multiforme (GBM) is hard to treat due to cell state changes. This study models GBM cell states to find new drug targets for better cancer therapy.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Glioblastoma Multiforme (GBM) presents significant therapeutic challenges, marked by poor survival rates.
- Intratumor heterogeneity and epigenetic plasticity, particularly cell-state transitions, impede effective drug treatments for GBM.
Approach:
- Utilized single-cell and bulk RNA sequencing data to classify GBM cells into four distinct states: neural progenitor-like (NPC-like), oligodendrocyte progenitor-like (OPC-like), astrocyte-like (AC-like), and mesenchymal-like (MES-like).
- Integrated phosphoproteomic data to construct cell-state-specific protein-protein interaction networks (PPINs).
- Developed a Boolean network from these PPINs for in silico protein knockout simulations to identify drivers of cell state transitions, followed by machine learning predictions on independent GBM data (GLASS Consortium).
Key Points:
- Identified key protein nodes and signaling pathways regulating cell state transitions in GBM.
- Generated hypotheses for causal mechanisms underlying these transitions, exemplified by TFAP2A promoting NPC-like to MES-like state shifts.
- Highlighted potential drug targets within these pathways for cell state-directed (CSD) therapy.
Conclusions:
- The study provides a computational framework to dissect GBM heterogeneity and plasticity.
- Identified specific molecular mechanisms and potential therapeutic targets for GBM treatment.
- Suggests that targeting cell state transitions could offer a novel therapeutic strategy for Glioblastoma Multiforme.
