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...

Markov Chain Monte Carlo (MCMC) method is a widely used algorithm design scheme with many applications. To make efficient use of this method, the key step is to prove that the Markov chain is rapid mixing. Canonical paths is one of the two main tools to prove rapid mixing. However, there are much fewer success examples comparing to coupling, the other main tool. The main reason is that there is no systematic approach or general recipe to design canonical paths. Building up on a previous exploration by McQuillan [18], we develop a general theory to design canonical paths for MCMC: We reduce the task of designing canonical paths to solving a set of linear equations, which can be automatically done even by a machine.Making use of this general approach, we obtain fully polynomial-time randomized approximation schemes (FPRAS) for counting the number of b-matching with b ≤ 7 and b-edge-cover with b ≤ 2. They are natural generalizations of matchings and edge covers for graphs. No polynomial time approximation was previously known for these problems.

Hepatic stellate cells (HSCs) are activated by inflammatory mediators to secrete extracellular matrix for collagen deposition, leading to liver fibrosis. Ferroptosis is iron- and lipid hydroperoxide-dependent programmed cell death, which has recently been targeted for inhibiting liver fibrogenic processes. Tripartite motif-containing protein 26 (TRIM26) is an E3 ubiquitin ligase that functions as a tumor suppressor in hepatocellular carcinoma, while little is known about its function in liver fibrosis. In the present study, the differential expression of TRIM26 in normal and fibrotic liver tissues was examined based on both online databases and specimens collected from patient cohort. The effects of TRIM26 on HSCs ferroptosis were examined in vitro through evaluating cell proliferation, lipid peroxidation, and expression of key ferroptosis-related factors. In vivo function of TRIM26 in liver fibrosis was examined based on CCl4-induced mice model. We found that TRIM26 was downregulated in fibrotic liver tissues. The overexpression of TRIM26 inhibited HSCs proliferation, promoted lipid peroxidation, manipulated ferroptosis-related factor expressions, and counteracted the effect of iron inhibitor deferoxamine. Moreover, TRIM26 physically interacted with solute carrier family-7 member-11 (SLC7A11), a critical protein for lipid reactive oxygen species (ROS) scavenging, and mediated its ubiquitination. In addition, TRIM26 overexpression induced HSCs ferroptosis and mitigated CCl4-induced liver fibrosis in mice. In conclusion, TRIM26 promotes HSCs ferroptosis to suppress liver fibrosis through mediating the ubiquitination of SLC7A11. The TRIM26-targeted SLC7A11 suppression can be a novel therapeutic strategy for liver fibrosis.

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