Summary 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<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...
Two-dimensional (2-D) gel electrophoresis has been used to map proteins from various cell types in an effort to eventually link such maps to the sequencing of the entire human genome. While this analysis indicates the cellular disposition and expression of proteins, another application of 2-D gels, the analysis of phosphoproteins, can provide much information as to the assembly and "wiring" of the signal transduction circuits within cells which appear to be enervated by phosphate exchange. The preparation and separation of 32P-labeled proteins is described, as well as various analytical methods, including: the variety of gel systems available for specialist types of analyses, comparing 33P- and 32P-labeling of proteins, imaging techniques, phosphoamino analysis, phosphopeptide separation, identifying the amino acid groups that are phosphorylated, and the identification of phosphoproteins on 2-D gels by immunoprecipitation, corunning of purified proteins, comparative mapping and microsequencing, and by Western blotting. Examples (in brackets) are given of applications in which 2-D phosphogels can be applied, which offer advantages over other techniques. These include: (i) identifying in vivo substrates for kinases (protein kinase C activated by phorbol myristate acetate), (ii) investigating cytokine signaling pathways (tumor necrosis factor and interleukin-1), (iii) investigating the effects of drugs on signaling pathways (okadaic acid, menadione and cyclooxygenase inhibitors), (iv) characterization of specific phosphoproteins (heat-shock protein Hsp27 and stathmin), (v) comparing normal and transformed cells (MRC-5 human lung fibroblasts and their SV-40-transformed counterparts, MRC-5 SV1 cells), (vi) purifying phosphoproteins, (vii) investigating the relationship of protein phosphorylation to stages in the cell cycle (stathmin), (viii) investigating protein/protein interactions, (ix) mapping in vitro kinase substrates (protein kinase C, protein kinase A, and mitogen activated protein kinase activated protein kinase 2), and (x) locating and identifying cellular phosphatases (Hsp27 phosphatase). It is possible that the mapping of phosphoproteins can be linked to other 2-D gel databases and that information derived from these can be used in the future to better understand the signaling mechanisms of normal and cancerous cells.
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