Death of human tumor endothelial cells in vitro through a probable calcium-associated mechanism induced by bevacizumab and detected via a novel method

We isolated three dimensional cell clusters from fresh human solid tumors and also isolated human neoplastic and normal lymphatic cells. Cells were cultured for 96 hours with and without bevacizumab and other agents. At concentrations of bevacizumab which completely removed VEGF from the culture medium, dead microvascular cells were detected through Fast Green/H&E staining as previously described. These peculiar staining characteristics suggested the involvement of calcium, and this was confirmed through staining with Alizarin red S. Using Alizarin staining as a marker for endothelial cell death permitted the use of public domain image analysis software which resulted in a sensitive and specific system for identifying active pharmaceuticals which target the tumor microvasculature at the same time direct antitumor cell effects are determined. Our results suggest an important role for calcium in endothelial cell death mediated by bevacizumab and other agents and further suggest that agents promoting calcium influx may potentiate the activity of antiangiogenic agents

Endothelial Massive Calcium Accumulation Death (MCAD): Mechanism, Target, and Predictive Biomarker for Anti-Angiogenic Therapy

We cultured human umbilical vein endothelial cells with bevacizumab, with tyrosine kinase inhibitors known to be AA, and with traditional cytotoxic drugs. The images below show that, in the presence of physiological saline and non-favorable culture conditions, the vast majority of the endothelial cells undergo a "non-specific" type of cell death (NSCD), not associated with calcium accumulation, but with loss of cell membrane integrity, allowing uptake of the Fast Green dye, staining these dead dells a pale blue green. In the presence of known AA agents (e.g. bevacizumab, some TK inhibitors) a large percentage of the endothelial cells undergo death associated with massive calcium accumulation (MCAD), with these cells staining hyperchromatic, refractile, blue-black, precisely as reported in http://www.ncbi.nlm.nih.gov/pubmed/18793333 and http://meeting.ascopubs.org/cgi/content/abstract/
28/15_suppl/e13617 and http://tinyurl.com/weisenthal-breast-lapatinib. MCAD is strikingly demonstrated by Fast Green/Alizarin staining as reported in http://precedings.nature.com/documents/4499/version/1. Traditional cytotoxic drugs (e.g. cisplatin) produce only NSCD and inhibit MCAD. We propose that MCAD is a cell death mechanism unique to endothelial cells and provides a practical biomarker to predict for AA activity in clinical oncology and drug development, as well as a potential drug target

Bevacizumab-induced tumor calcifications can be elicited in glioblastoma microspheroid culture and represent massive calcium accumulation death (MCAD) of tumor endothelial cells

Bähr and colleagues reported that 22 of 36 glioblastoma patients treated with bevacizumab showed tumor calcifications on 8 week post therapy follow up with MRI. Early tumor calcification strongly predicted for response, time to progression, and overall survival. The authors didn’t understand the mechanism, but speculated that it was vascular in nature. At the 13th International Anti-Angiogenic Symposium (2011), we presented our discovery of the phenomenon of massive calcium accumulation death, wherein MCAD occurred in endothelial cells (tumor, circulating, and HUVEC), in response to VEGF depletion by bevacizumab and other putative anti-angiogenic agents, but not in response to non-specific cytotoxins. In subsequent work, we have documented marked MCAD to occur in primary microcluster cultures from 6 fresh human glioblastoma biopsies, following 96 hours of VEGF depletion in vitro by bevacizumab. The presence and degree of MCAD is strikingly dependent on the type of serum in the culture medium (RPMI-1640 + 25% serum) -- typically most striking in (very low VEGF) fetal calf serum, but inhibited (often) or enhanced (rarely) by 25% human serum from different patients or normal donors containing variable quantities of VEGF. There was not a linear relationship between VEGF concentration and MCAD inhibition (or enhancement), suggesting that other pro-angiogenic (or anti-angiogenic) serum factors may play a role. In epithelial metastatic tumors, circulating peripheral blood endothelial cells may be easily tested, using our methods, and the serum inhibition (or, rarely, enhancement) is faithfully reproduced on circulating endothelial cells, in comparison with the tumor cluster-associated endothelial cells. We propose MCAD as the mechanism of glioblastoma calcification following bevacizumab and further propose that testing tumor microclusters and/or circulating endothelial cells, in the presence of autologous serum, could be a useful predictive biomarker and research tool

Relative Sparsity for Medical Decision Problems

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (i.e., the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields ``relative sparsity," where, as a function of a tuning parameter, $\lambda$, we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (e.g., heart rate only). We propose a criterion for selecting $\lambda$, perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.Comment: 53 pages, 7 figures, 2 table

Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.Comment: In revisio