A handheld and biocompatible mass spectrometry system provides nondestructive molecular diagnosis of cancer tissue samples.

Mechanisms by which blood cells sense shear stress are poorly characterized. In platelets, glycoprotein (GP)Ib–IX receptor complex has been long suggested to be a shear sensor and receptor. Recently, a relatively unstable and mechanosensitive domain in the GPIbα subunit of GPIb–IX was identified. Here we show that binding of its ligand, von Willebrand factor, under physiological shear stress induces unfolding of this mechanosensory domain (MSD) on the platelet surface. The unfolded MSD, particularly the juxtamembrane ‘Trigger’ sequence therein, leads to intracellular signalling and rapid platelet clearance. These results illustrate the initial molecular event underlying platelet shear sensing and provide a mechanism linking GPIb–IX to platelet clearance. Our results have implications on the mechanism of platelet activation, and on the pathophysiology of von Willebrand disease and related thrombocytopenic disorders. The mechanosensation via receptor unfolding may be applicable for many other cell adhesion receptors.

Key Points: Anti-GPIbα antibodies exert a pulling force on platelet GPIbα by crosslinking platelets under shear flow. A mechanical feature of an anti-GPIbα antibody, rather than affinity or epitope, determines ability to induce Fc-independent clearance.

Objective—: The platelet storage lesion accelerates platelet clearance after transfusion, but the underlying molecular mechanism remains elusive. Although inhibiting sheddase activity hampers clearance of platelets with storage lesion, the target platelet protein responsible for ectodomain shedding–induced clearance is not definitively identified. Monoclonal antibody 5G6 was developed recently to bind specifically human platelet receptor glycoprotein (GP)Ibα and inhibit its shedding but not shedding of other receptors. Here, the role of GPIbα shedding in platelet clearance after transfusion was addressed. Approach and Results—: Both human leukoreduced apheresis-derived platelets and transgenic mouse platelets expressing human GPIbα were stored at room temperature in the presence and absence of 5G6 Fab fragment. At various time points, aliquots of stored platelets were analyzed and compared. 5G6 Fab inhibited GPIbα shedding in both platelets during storage and preserved higher level of GPIbα on the platelet surface. Compared with age-matched control platelets, 5G6 Fab–stored platelets exhibited similar levels of platelet activation, degranulation, and agonist-induced aggregation. 5G6 Fab–stored human GPIbα platelets exhibited significantly higher post-transfusion recovery and in vivo hemostatic function in recipient mice than control platelets. Consistently, 5G6 Fab–stored, 8-day-old human platelets produced similar improvement in post-transfusion recovery in immunodeficient mice and in ex vivo thrombus formation over collagen under shear flow. Conclusions—: Specific inhibition of GPIbα shedding in the stored platelets improves post-transfusion platelet recovery and hemostatic function, providing clear evidence for GPIbα shedding as a cause of platelet clearance. These results suggest that specific inhibition of GPIbα shedding may be used to optimize platelet storage conditions.

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two “tumor imaging phenotypes” (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.