Justin Guinney, Tao Wang, Teemu D Laajala, Kimberly Kanigel Winner, J Christopher Bare, Elias Chaibub Neto, Suleiman A Khan, Gopal Peddinti, Antti Airola, Tapio Pahikkala, Tuomas Mirtti, Thomas Yu, Brian M Bot, Liji Shen, Kald Abdallah, Thea Norman, Stephen Friend, Gustavo Stolovitzky, Howard Soule, Christopher J Sweeney, Charles J Ryan, Howard I Scher, Oliver Sartor, Yang Xie, Tero Aittokallio, Fang Liz Zhou, James C Costello, Catalina Anghe, Helia Azima, Robert Baertsch, Pedro J Ballester, Chris Bare, Vinayak Bhandari, Cuong C Dang, Maria Bekker-Nielsen Dunbar, Ann-Sophie Buchardt, Ljubomir Buturovic, Da Cao, Prabhakar Chalise, Junwoo Cho, Tzu-Ming Chu, R Yates Coley, Sailesh Conjeti, Sara Correia, Ziwei Dai, Junqiang Dai, Philip Dargatz, Sam Delavarkhan, Detian Deng, Ankur Dhanik, Yu Du, Aparna Elangovan, Shellie Ellis, Laura L Elo, Shadrielle M Espiritu, Fan Fan, Ashkan B Farshi, Ana Freitas, Brooke Fridley, Christiane Fuchs, Eyal Gofer, Gopalacharyulu Peddinti, Stefan Graw, Russ Greiner, Yuanfang Guan, Jing Guo, Pankaj Gupta, Anna I Guyer, Jiawei Han, Niels R Hansen, Billy HW Chang, Outi Hirvonen, Barbara Huang, Chao Huang, Jinseub Hwang, Joseph G Ibrahim, Vivek Jayaswa, Jouhyun Jeon, Zhicheng Ji, Deekshith Juvvadi, Sirkku Jyrkkiö, Kimberly Kanigel-Winner, Amin Katouzian, Marat D Kazanov, Shahin Khayyer, Dalho Kim, Agnieszka K Golinska, Devin Koestler, Fernanda Kokowicz, Ivan Kondofersky, Norbert Krautenbacher, Damjan Krstajic, Luke Kumar, Christoph Kurz, Matthew Kyan, Michael Laimighofer, Eunjee Lee, Wojciech Lesinski, Miaozhu Li, Ye Li, Qiuyu Lian, Xiaotao Liang, Minseong Lim, Henry Lin, Xihui Lin, Jing Lu, Mehrad Mahmoudian, Roozbeh Manshaei, Richard Meier, Dejan Miljkovic, Krzysztof Mnich, Nassir Navab, Elias C Neto, Yulia Newton, Subhabrata Pal, Byeongju Park, Jaykumar Patel, Swetabh Pathak, Alejandrina Pattin, Donna P Ankerst, Jian Peng, Anne H Petersen, Robin Philip, Stephen R Piccolo, Sebastian Pölsterl, Aneta Polewko-Klim, Karthik Rao, Xiang Ren, Miguel Rocha, Witold R. Rudnicki, Hyunnam Ryu, Hagen Scherb, Raghav Sehgal, Fatemeh Seyednasrollah, Jingbo Shang, Bin Shao, Howard Sher, Motoki Shiga, Artem Sokolov, Julia F Söllner, Lei Song, Josh Stuart, Ren Sun, Nazanin Tahmasebi, Kar-Tong Tan, Lisbeth Tomaziu, Joseph Usset, Yeeleng S Vang, Roberto Vega, Vitor Vieira, David Wang, Difei Wang, Junmei Wang, Lichao Wang, Sheng Wang, Yue Wang, Russ Wolfinger, Chris Wong, Zhenke Wu, Jinfeng Xiao, Xiaohui Xie, Doris Xin, Hojin Yang, Nancy Yu, Xiang Yu, Sulmaz Zahedi, Massimiliano Zanin, Chihao Zhang, Jingwen Zhang, Shihua Zhang, Yanchun Zhang, Hongtu Zhu, Shanfeng Zhu, Yuxin Zhu, Universidade do Minho, Institute for Molecular Medicine Finland, University of Helsinki, Department of Pathology, Medicum, Clinicum, HUSLAB, Tero Aittokallio / Principal Investigator, and Bioinformatics
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 interestnamely, 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 trialENTHUSE M1in 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 2·394·62, p, European Union within the ERC grant LatentCauses supported the work of C.F and I.K. German Research Foundation (DFG) within the Collaborative Research Centre 1243, subproject A17 awarded to C.F. German Federal Ministry of Education and Research (BMBF) through the Research Consortium e:AtheroMED (Systems medicine of myocardial infarction and stroke) under the auspices of the e:Med Programme (grant # 01ZX1313C) supported the work of D.P.A., P.D., C.F., C.K., I.K., N.K., M.L., H.S. and J.F.S. at the Institute of Computational Biology. NIH Grants RR025747-01, MH086633 and 1UL1TR001111, and NSF Grants SES-1357666, DMS-14-07655 and BCS0826844 supported the work of C.H., J.I., E.L., Y.W., H.Y., H.Z. and J.Z. NSFC Grant Nos. 61332013, 61572139 supported the work of X.L, Y.L, Y.Z., and S.Z. National Natural Science Foundation of China grants [Nos. 61422309, 61379092] was awarded to S.Z. The Patrick C. Walsh Prostate Research Fund and the Johns Hopkins Individualized Health Initiative supported the work of R.Y.C., D.D., Y.D., Z.J., K.R., Z.W. and Y.Z. FCT Ph.D. Grant SFRH/BD/80925/2011 was awarded to S.C. Clinical Persona Inc., East Palo Alto, CA supported the work of L.B. and D.K. The Finnish Cultural Foundation and the Drug Research Doctoral Programme (DRDP) at the University of Turku supported T.D.L. The National Research Foundation Singapore and the Singapore Ministry of Education, under its Research Centres of Excellence initiative, supported the work of J.G. and K.T. A grant from the Russian Science Foundation 14-24-00155 was awarded to M.D.K. A*MIDEX grant (no. ANR-11-IDEX-0001-02) was awarded to P.J.B. NSERC supported the work of R.G. The Israeli Centers of Research Excellence (I-CORE) program (Center No. 4/11) supported the work of E.G. Academy of Finland (grants 292611, 269862, 272437, 279163, 295504), National Cancer Institute (16X064), and Cancer Society of Finland supported the work of T.A. Academy of Finland (grant 268531) supported the work of T.M