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

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

Abstract Background Mara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect. Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons. Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation

Oxygen availability determines key regulators in soil organic carbon mineralisation in paddy soils

Rice paddy agro-ecosystems play an important role in global carbon (C) sequestration. Because of flooding management, paddy soil experience periodical changes in oxygen availability, which may make soil organic carbon (SOC) mineralisation unique as compared to upland or other wetland ecosystems. However, at present, information about the relevant mechanisms involved in paddy SOC mineralisation is limited and unclear. We selected three paddy soils with variable iron (Fe) contents, which were either fumigated with chloroform (to reduce microbial biomass) or remained un-fumigated. Soils were incubated for 78 days in one of three treatments: alternating nonflooded–flooded (NF: moist for 0–30 days (oxygen-abundant) and flooded for 31–78 days (oxygen-limited)), continuously flooded (CF: oxygen-limited), and continuously anaerobically flooded (AF: oxygen-depleted). Fumigation reduced the microbial biomass C by more than 70%. Except for the nonflooded period in the NF treatment, the SOC mineralisation rate, at the late stage of each treatment, was significantly lower in the fumigated than in the un-fumigated soil. A multiple regressions showed that a reduction in dissolved organic C contents over time contributed to the cumulative SOC mineralisation only during the nonflooded period in the NF treatment. Furthermore, the labile C pool size was smaller in the AF treatment relative to the other treatments. These imply that dissolved substrates in oxygen-depleted paddy soil were of greater recalcitrance, most likely due to thermodynamic reasons. SOC mineralisation correlated with changes in the redox potential and the Fe2+ contents in the CF and AF treatments only. This indicates that under oxygen-limited and -depleted conditions, Fe played a significant role as an electron acceptor during SOC mineralisation. Correlation and linear regression analyses also suggest that Fe influenced dissolved organic C contents, and hydrolase and oxidative activities. Our findings show that SOC bioavailability is a rate-limiting factor for SOC mineralisation, but only under oxygen-abundant conditions. However, under oxygen-limited or -depleted conditions, microbial biomass, the recalcitrance of organic C compounds, and the availability of electron acceptors are key regulators in determining the intensity and rate of SOC mineralisation

Vertical and horizontal shifts in the microbial community structure of paddy soil under long-term fertilization regimes

Knowledge remains limited on how the structure of microbial community in paddy soils changes in relation to different types of fertilizers with same amount of nutrients. Thus, here, soil samples were collected at 0–10, 10–20, 20–30, and 30–40 cm depths from a paddy field subjected to four long-term fertilization treatments (no fertilization, mineral fertilization, mineral fertilization combined with rice straw, and chicken manure) and analyzed for microbial biomass and community composition. In unfertilized soils, microbial biomass decreased from 0 to 40 cm (with actinomycetes < gram-positive (G+) bacteria < gram-negative (G&#x100000; ) bacteria < fungi). This ordering was retained after fertilization, but the decline with depth was less pronounced. Both mineral and mineral plus organic fertilization increased the biomass of G+ bacteria compared to G&#x100000; bacteria (22.7–56.2% increase) and actinomycetes (14.8–52.5% increase). Thus, over the long term, G+ bacteria benefited the most from mineral fertilizer than the other microbial groups. The partial replacement of mineral fertilizer with manure primarily enhanced the abundance of G+ bacteria at 0–30 cm soil depth, whereas replacement with straw enhanced the abundance of fungi at 10–20 cm soil depth. Our findings demonstrate that the structure of the microbial community is strongly impacted by long-term fertilization, independent of fertilizer type

Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach [version 1; referees: 2 approved, 1 approved with reservations]

In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods