Purpose: Preclinical studies have demonstrated that postirradiation tumor revascularization is dependent on a stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4)-driven process in which myeloid cells are recruited from bone marrow. Blocking this axis results in survival improvement in preclinical models of solid tumors, including glioblastoma (GBM). We conducted a phase I/II study to determine the safety and efficacy of Macrophage Exclusion after Radiation Therapy (MERT) using the reversible CXCR4 inhibitor plerixafor in patients with newly diagnosed glioblastoma.Patients and Methods: We enrolled nine patients in the phase I study and an additional 20 patients in phase II using a modified toxicity probability interval (mTPI) design. Plerixafor was continuously infused intravenously via a peripherally inserted central catheter (PICC) line for 4 consecutive weeks beginning at day 35 of conventional treatment with concurrent chemoradiation. Blood serum samples were obtained for pharmacokinetic analysis. Additional studies included relative cerebral blood volume (rCBV) analysis using MRI and histopathology analysis of recurrent tumors.Results: Plerixafor was well tolerated with no drugattributable grade 3 toxicities observed. At the maximum dose of 400 mg/kg/day, biomarker analysis found suprathreshold plerixafor serum levels and an increase in plasma SDF-1 levels. Median overall survival was 21.3 months [95% confidence interval (CI), 15.9-NA] with a progression-free survival of 14.5 months (95% CI, 11.9-NA). MRI and histopathology support the mechanism of action to inhibit postirradiation tumor revascularization.Conclusions: Infusion of the CXCR4 inhibitor plerixafor was well tolerated as an adjunct to standard chemoirradiation in patients with newly diagnosed GBM and improves local control of tumor recurrences.

Key Points Question Can machine-learning approaches achieve an objective pulmonary embolism risk score by analyzing temporal patient data to accurately inform computed tomographic imaging decisions? Findings In this multi-institutional diagnostic study of 3214 patients, a machine learning model was designed to achieve an accurate patient-specific risk score for pulmonary embolism diagnosis. The model was successfully evaluated in both multi-institutional inpatient and outpatient settings. Meaning Machine learning algorithms using retrospective temporal patient data appear to be a valuable and feasible tool for accurate computation of patient-specific risk score to better inform clinical decision-making for computed tomographic pulmonary embolism imaging.

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.

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