Prostate cancer cell-platelet bidirectional signaling promotes calcium mobilization, invasion and apoptotic resistance via distinct receptor-ligand pairs

Platelets play a crucial role in cancer and thrombosis. However, the receptor-ligand repertoire mediating prostate cancer (PCa) cell-platelet interactions and ensuing consequences have not been fully elucidated. Microvilli emanating from the plasma membrane of PCa cell lines (RC77 T/E, MDA PCa 2b) directly contacted individual platelets and platelet aggregates. PCa cell-platelet interactions were associated with calcium mobilization in platelets, and translocation of P-selectin and integrin αβ onto the platelet surface. PCa cell-platelet interactions reciprocally promoted PCa cell invasion and apoptotic resistance, and these events were insensitive to androgen receptor blockade by bicalutamide. PCa cells were exceedingly sensitive to activation by platelets in vitro, occurring at a PCa cell:platelet coculture ratio as low as 1:10 (whereas PCa patient blood contains 1:2,000,000 per ml). Conditioned medium from cocultures stimulated PCa cell invasion but not apoptotic resistance nor platelet aggregation. Candidate transmembrane signaling proteins responsible for PCa cell-platelet oncogenic events were identified by RNA-Seq and broadly divided into 4 major categories: (1) integrin-ligand, (2) EPH receptor-ephrin, (3) immune checkpoint receptor-ligand, and (4) miscellaneous receptor-ligand interactions. Based on antibody neutralization and small molecule inhibitor assays, PCa cell-stimulated calcium mobilization in platelets was found to be mediated by a fibronectin1 (FN1)-αβ signaling axis. Platelet-stimulated PCa cell invasion was facilitated by a CD55-adhesion G protein coupled receptor E5 (ADGRE5) axis, with contribution from platelet cytokines CCL3L1 and IL32. Platelet-stimulated PCa cell apoptotic resistance relied on ephrin-EPH receptor and lysophosphatidic acid (LPA)-LPA receptor (LPAR) signaling. Of participating signaling partners, FN1 and LPAR3 overexpression was observed in PCa specimens compared to normal prostate, while high expression of CCR1 (CCL3L1 receptor), EPHA1 and LPAR5 in PCa was associated with poor patient survival. These findings emphasize that non-overlapping receptor-ligand pairs participate in oncogenesis and thrombosis, highlighting the complexity of any contemplated clinical intervention strategy

Differences in the Platelet mRNA Landscape Portend Racial Disparities in Platelet Function and Suggest Novel Therapeutic Targets.

The African American (AA) population displays a 1.6 to 3-fold higher incidence of thrombosis and stroke mortality compared to European Americans (EA). Current anti-platelet therapies target the ADP-mediated signaling pathway, which displays significant pharmacogenetic variation for platelet reactivity. The focus of this study was to define underlying population differences in platelet function in an effort to identify novel molecular targets for future anti-platelet therapy. We performed deep coverage RNA-Seq to compare gene expression levels in platelets derived from a cohort of healthy volunteers defined by ancestry determination. We identified >13,000 expressed platelet genes of which 480 were significantly differentially expressed genes (DEGs) between AAs and EAs. DEGs encoding proteins known or predicted to modulate platelet aggregation, morphology or platelet count were up-regulated in AA platelets. Numerous G-protein coupled receptors (GPCRs), ion channels, and pro-inflammatory cytokines not previously associated with platelet function were likewise differentially expressed. Many of the signaling proteins represent potential pharmacologic targets of intervention. Notably, we confirmed the differential expression of cytokines IL32 and PROK2 in an independent cohort by qRT-PCR, and provide functional validation of the opposing actions of these two cytokines on collagen-induced AA platelet aggregation. Using GTEx whole blood data, we identified 516 eQTLs with Fst values >0.25, suggesting that population-differentiated alleles may contribute to differences in gene expression. This study identifies gene expression differences at the population level that may affect platelet function and serve as potential biomarkers to identify CVD risk. Additionally, our analysis uncovers candidate novel druggable targets for future anti-platelet therapies

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial

Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. Conclusions: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE