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.
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.
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DOI：
10.1016/s1470-2045(16)30560-5
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发表时间：
2017-01
期刊：
影响因子：
51.1
通讯作者：
Zhu, Yuxin
中科院分区：
文献类型：
--
作者：
Guinney, Justin;Wang, Tao;Laajala, Teemu D.;Winner, Kimberly Kanigel;Bare, J. Christopher;Neto, Elias Chaibub;Khan, Suleiman A.;Peddinti, Gopal;Airola, Antti;Pahikkala, Tapio;Mirtti, Tuomas;Yu, Thomas;Bot, Brian M.;Shen, Liji;Abdallah, Kald;Norman, Thea;Friend, Stephen;Stolovitzky, Gustavo;Soule, Howard;Sweeney, Christopher J.;Ryan, Charles J.;Scher, Howard I.;Sartor, Oliver;Xie, Yang;Aittokallio, Tero;Zhou, Fang Liz;Costello, James C.;Abdallah, Kald;Aittokallio, Tero;Airola, Antti;Anghel, Catalina;Azima, Helia;Baertsch, Robert;Ballester, Pedro J.;Bare, Chris;Bhandari, Vinayak;Bot, Brian M.;Dang, Cuong C.;Dunba, Maria Bekker-Nielsen;Buchardt, Ann-Sophie;Buturovic, Ljubomir;Cao, Da;Chalise, Prabhakar;Cho, Junwoo;Chu, Tzu-Ming;Coley, R. Yates;Conjeti, Sailesh;Correia, Sara;Costello, James C.;Dai, Ziwei;Dai, Junqiang;Dargatz, Philip;Delavarkhan, Sam;Deng, Detian;Dhanik, Ankur;Du, Yu;Elangovan, Aparna;Ellis, Shellie;Elo, Laura L.;Espiritu, Shadrielle M.;Fan, Fan;Farshi, Ashkan B.;Freitas, Ana;Fridley, Brooke;Friend, Stephen;Fuchs, Christiane;Gofer, Eyal;Peddinti, Gopalacharyulu;Graw, Stefan;Greiner, Russ;Guan, Yuanfang;Guinney, Justin;Guo, Jing;Gupta, Pankaj;Guyer, Anna I.;Han, Jiawei;Hansen, Niels R.;Chang, Billy H. W.;Hirvonen, Outi;Huang, Barbara;Huang, Chao;Hwang, Jinseub;Ibrahim, Joseph G.;Jayaswal, Vivek;Jeon, Jouhyun;Ji, Zhicheng;Juvvadi, Deekshith;Jyrkkio, Sirkku;Kanigel-Winner, Kimberly;Katouzian, Amin;Kazanov, Marat D.;Khan, Suleiman A.;Khayyer, Shahin;Kim, Dalho;Golinska, Agnieszka K.;Koestler, Devin;Kokowicz, Fernanda;Kondofersky, Ivan;Krautenbacher, Norbert;Krstajic, Damjan;Kumar, Luke;Kurz, Christoph;Kyan, Matthew;Laajala, Teemu D.;Laimighofer, Michael;Lee, Eunjee;Lesinski, Wojciech;Li, Miaozhu;Li, Ye;Lian, Qiuyu;Liang, Xiaotao;Lim, Minseong;Lin, Henry;Lin, Xihui;Lu, Jing;Mahmoudian, Mehrad;Manshaei, Roozbeh;Meier, Richard;Miljkovic, Dejan;Mirtti, Tuomas;Mnich, Krzysztof;Navab, Nassir;Neto, Elias C.;Newton, Yulia;Norman, Thea;Pahikkala, Tapio;Pal, Subhabrata;Park, Byeongju;Patel, Jaykumar;Pathak, Swetabh;Pattin, Alejandrina;Ankerst, Donna P.;Peng, Jian;Petersen, Anne H.;Philip, Robin;Piccolo, Stephen R.;Poelsterl, Sebastian;Polewko-Klim, Aneta;Rao, Karthik;Ren, Xiang;Rocha, Miguel;Rudnicki, Witold R.;Ryan, Charles J.;Ryu, Hyunnam;Sartor, Oliver;Scherb, Hagen;Sehgal, Raghav;Seyednasrollah, Fatemeh;Shang, Jingbo;Shao, Bin;Shen, Liji;Sher, Howard;Shiga, Motoki;Sokolov, Artem;Soellner, Julia F.;Song, Lei;Soule, Howard;Stolovitzky, Gustavo;Stuart, Josh;Sun, Ren;Sweeney, Christopher J.;Tahmasebi, Nazanin;Tan, Kar-Tong;Tomaziu, Lisbeth;Usset, Joseph;Vang, Yeeleng S.;Vega, Roberto;Vieira, Vitor;Wang, David;Wang, Difei;Wang, Junmei;Wang, Lichao;Wang, Sheng;Wang, Tao;Wang, Yue;Wolfinger, Russ;Wong, Chris;Wu, Zhenke;Xiao, Jinfeng;Xie, Xiaohui;Xie, Yang;Xin, Doris;Yang, Hojin;Yu, Nancy;Yu, Thomas;Yu, Xiang;Zahedi, Sulmaz;Zanin, Massimiliano;Zhang, Chihao;Zhang, Jingwen;Zhang, Shihua;Zhang, Yanchun;Zhou, Fang Liz;Zhu, Hongtu;Zhu, Shanfeng;Zhu, Yuxin
转移性去势抵抗性前列腺癌预后模型的改进有可能增强临床试验设计并指导治疗策略。与Project Data Sphere合作，这是一项非营利计划，允许癌症临床试验数据与研究人员广泛共享，我们设计了一个开放数据，众包，梦想（逆向工程评估和方法对话）不仅要确定一个更好的预测转移性去势患者生存的预后模型，耐药前列腺癌，但也吸引了国际数据科学家社区来研究这种疾病。从Project Data Sphere获得了4项一线转移性去势抵抗性前列腺癌3期临床试验的对照组数据，包括ASCENT 2试验中接受多西他赛和泼尼松治疗的476例患者，MAINSAIL试验中接受多西他赛、泼尼松和安慰剂治疗的526例患者，VENICE试验中接受多西他赛、泼尼松或泼尼松龙和安慰剂治疗的598例患者，在ENTHUSE 33试验中，470名患者接受多西他赛和安慰剂治疗。集中管理了由150多个临床变量组成的数据集，包括人口统计学、实验室检查值、病史、病变部位和既往治疗。来自ASCENT 2、MAINSAIL和VENICE的数据被公开发布，作为训练数据用于预测感兴趣的结果，即总生存率。还发布了ENTHUSE 33的临床数据，但结果变量（总生存期和事件状态）的数据对挑战参与者隐藏，以便ENTHUSE 33可用于独立验证。使用积分时间依赖性曲线下面积（iAUC）评价方法。使用基于8个临床变量和惩罚考克斯比例风险模型的参考模型比较方法性能。使用第五项试验（ENTHUSE M1）的数据进行了进一步验证，其中266例转移性去势抵抗性前列腺癌患者单独接受安慰剂治疗。开发了50种独立的方法来预测总生存期，并通过DREAM挑战进行了评估。表现最好的是基于惩罚考克斯回归模型（ePCR）的集合，该模型唯一确定了与免疫生物标志物和肝肾功能标志物的预测相互作用效应。总体而言，ePCR优于所有其他方法（iAUC 0.791;贝叶斯因子>5），并超过参考模型（iAUC 0.743;贝叶斯因子>20）。ePCR模型和参考模型均将ENTHUSE 33试验中的患者分为高风险组和低风险组，总生存期存在显著差异（ePCR：风险比3.32，95% CI 2.39 - 4.62，p<0.0001;参考模型：2.56，1.85 - 3.53，p<0.0001）。新模型在ENTHUSE M1队列中得到进一步验证，具有相似的高性能（iAUC 0·768）。所有方法的荟萃分析证实了先前确定的预测性临床变量，并显示天冬氨酸氨基转移酶是一种重要的预后生物标志物，尽管先前报道不足。新的预后因素进行了划定，并由独立的国际团队开发的50种方法的评估建立了一个基准的方法在未来的发展。这项工作的结果表明，数据共享与众包挑战相结合，是开发晚期前列腺癌新预后模型的强大框架。赛诺菲美国服务部，项目数据领域。
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. 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. 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 predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. 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. Sanofi US Services, Project Data Sphere.