Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Closely related oxidized phospholipids differentially modulate the physicochemical properties of lipid particles

Oxidation of glycerophospholipids results in the formation of large variety of oxidized phospholipid products that differs significantly in their chemical compositions and molecular structures. Biological activities of these oxidized products also differ considerably. Here we report the comparisons of the physicochemical properties of non-oxidized phospholipid particle containing two closely related tOx-PLs: 1-palmitoyl-2-(5-keto-6-octendioyl)-sn-glycero-3- phosphocholine (KOdiA-PC) and 1-palmitoyl-2-(9-keto-10-dodecendioyl)-sn-glycero- 3-phosphocholine (KDdiA-PC). DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) was used as a model membrane non-oxidized phospholipid. Physicochemical properties of the lipid particles were characterized by using fluorescence spectroscopy, native polyacrylamide gel and agarose gel electrophoresis. Our result shows that the presence of closely related tOx-PLs, which differ only in the chemical composition of the oxidized fatty acyl chains at the sn-2 position, exerts considerably different effect on the physicochemical properties of non-oxidized phospholipid particles containing them

Self-assembled nanoscale upconversion POP composite for hypoxia relieving and enhanced chemotherapy in hepatocellular carcinoma

Uncontrolled proliferations and altered metabolism of cancer cells result an imbalance of nutrients as well as oxygen supply and persuade hypoxia. This hypoxia in turn activates the transcription gene HIF-1α which eventually upregulates the efflux transporter P-gp and induce MDR. Thus, hypoxia leads to resistance towards conventional therapy methods. Therefore, the fabrication of a nanoscale porous system enriched with upconversion nanoparticle to target the cancer cells, evade hypoxia and enhanced anticancer therapy is the key goal of this chapter. Herein, the upconversion nanoparticles are embedded with the nanoscale POP and further conjugated with targeting moiety and also with catalase molecule. The nanoscale POP embedded with UCNPs are generated in room temperature. Targeting ligand lactobionic acid is attached after polymer coating which effectively targets liver cancer cells. Then catalase is grafted effectively produces oxygen. The endogenously generated oxygen alleviates the hypoxia of the liver cancer cells. The drug and catalase-loaded composite exhibits more cytotoxicity in case of hypoxic liver cells than the normal cells by overcoming hypoxia and downregulating the hypoxia inducible factors

MD Simulation Studies for Selective Phytochemicals as Potential Inhibitors against Major Biological Targets of Diabetic Nephropathy

Diabetes is emerging as an epidemic and is becoming a public health concern worldwide. Diabetic nephropathy is one of the serious complications of diabetes, and about 40% of individuals with diabetes develop diabetic nephropathy. The consistent feature of diabetes and its associated nephropathy is hyperglycemia, and in some cases, hyperamylinemia. Currently, the treatment includes the use of medication for blood pressure control, sugar control, and cholesterol control, and in the later stage requires dialysis and kidney transplantation, making the management of this complication very difficult. Bioactive compounds, herbal medicines, and extracts are extensively used in the treatment and prevention of several diseases, and some are reported to be efficacious in diabetes too. Therefore, in this study, we tried to identify the therapeutic potential of phytochemicals used in in silico docking and molecular dynamic simulation studies using a library of 5284 phytochemicals against the two potential targets of type 2 diabetes-associated nephropathy. We identified two phytochemicals (i.e., gentisic acid and michelalbine) that target human amylin peptide and dipeptidyl peptidase-4, respectively, with good binding affinity. These phytochemicals can be further evaluated using in vitro and in vivo studies for their anti-hyperglycemia and anti-hyperamylinemia effects