A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches.

Background: Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Methods: Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. Results: We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. Conclusion: The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

Aging-related alterations in mechanistic target of rapamycin signaling promote platelet hyperreactivity and thrombosis.

BACKGROUND: Aging is an independent risk factor for the development of cardiovascular, thrombotic, and other chronic diseases. However, mechanisms of platelet hyperactivation in aging remain poorly understood. OBJECTIVES: Here, we examine whether and how aging alters intracellular signaling in platelets to support platelet hyperactivity and thrombosis. METHODS: Quantitative mass spectrometry with tandem mass tag labeling systematically measured protein phosphorylation in platelets from healthy aged (>65 years) and young human (<45 years) subjects. The role of platelet mechanistic target of rapamycin (mTOR) in aging-induced platelet hyperreactivity was assessed using pharmacologic mTOR inhibition and a platelet-specific mTOR-deficient mouse model (mTORplt-/-). RESULTS: Quantitative phosphoproteomics uncovered differential site-specific protein phosphorylation within mTOR, Rho GTPase, and MAPK pathways in platelets from aged donors. Western blot confirmed constitutive activation of the mTOR pathway in platelets from both aged humans and mice, which was associated with increased aggregation compared with that in young controls. Inhibition of mTOR with either Torin 1 in aged humans or genetic deletion in aged mice reversed platelet hyperreactivity. In a collagen-epinephrine pulmonary thrombosis model, aged wild-type (mTORplt+/+) mice succumbed significantly faster than young controls, while time to death of aged mTORplt-/- mice was similar to that of young mTORplt+/+ mice. Mechanistically, we noted increased Rac1 activation and levels of mitochondrial reactive oxygen species in resting platelets from aged mice, as well as increased p38 phosphorylation upstream of thromboxane generation following agonist stimulation. CONCLUSION: Aging-related changes in mTOR phosphorylation enhance Rac1 and p38 activation to enhance thromboxane generation, platelet hyperactivity, and thrombosis.

Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.