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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

Artykuł
Czasopismo : LANCET ONCOLOGY   Tom: 18, Zeszyt: 1, Strony: 132-142
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 Anghel , Helia Azima , Robert Baertsch , Pedro J Ballester , 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 , 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 Jayaswal , Jouhyun Jeon , Zhicheng Ji , Deekshith Juvvadi , Sirkku Jyrkkiö , Amin Katouzian , Marat D Kazanov , Shahin Khayyer , Dalho Kim , Agnieszka Golińska [2] , Devin Koestler , Fernanda Kokowicz , Ivan Kondofersky , Norbert Krautenbacher , Damjan Krstajic , Luke Kumar , Christoph Kurz , Matthew Kyan , Michael Laimighofer , Eunjee Lee , Wojciech Lesiński [2] , 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 [3] , Nassir Navab , 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 [2] , Karthik Rao , Xiang Ren , Miguel Rocha , Witold Rudnicki [1] , [2] , 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 , Xie Xiaohui , 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
2017-01 angielski
Link do publicznie dostępnego pełnego tekstu
Identyfikatory
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Cechy publikacji
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  • Oryginalny artykuł naukowy
  • Zrecenzowana naukowo
Dyscypliny naukowe
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Biologia medyczna , Informatyka – dziedzina nauk technicznych , Nauki o zdrowiu
Abstrakty ( angielski )
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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 predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. 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. Funding Sanofi US Services, Project Data Sphere.
Pełny tekst
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  1. Rodzaj tekstu: Ostateczna wersja wydawcy
  2. Licencja: Dozwolony użytek osobisty
  3. Pliki
    1. Dream_Prostate_Cancer.pdf, 719 kB Pobierz plik
    2. Dream_Prostate_Cancer_appendix.pdf, 1 679 kB Pobierz plik
Bibliografia
-
  1. 1 A Jemal, F Bray, MM Center, J Ferlay, E Ward, D Forman. Global cancer statistics. CA Cancer J Clin, 61 (2011), pp. 69–90
  2. T Tanimoto, A Hori, M Kami. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med, 363 (2010), p. 1966
  3. A Berruti, A Pia, M Terzolo. Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med, 365 (2011), p. 766
  4. CJ Ryan, MR Smith, JS de Bono, et al. Abiraterone in metastatic prostate cancer without previous chemotherapy. N Engl J Med, 368 (2013), pp. 138–148
  5. HI Scher, K Fizazi, F Saad, et al. Increased survival with enzalutamide in prostate cancer after chemotherapy. Increased survival with enzalutamide in prostate cancer after chemotherapy
  6. JS de Bono, S Oudard, M Ozguroglu, for the TROPIC investigators, et al. Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet, 376 (2010), pp. 1147–1154
  7. C Parker, S Nilsson, D Heinrich, et al. Alpha emitter radium-223 and survival in metastatic prostate cancer. N Engl J Med, 369 (2013), pp. 213–223
  8. PW Kantoff, CS Higano, ND Shore, et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med, 363 (2010), pp. 411–422
  9. TM Beer, AJ Armstrong, DE Rathkopf, et al. Enzalutamide in metastatic prostate cancer before chemotherapy. N Engl J Med, 371 (2014), pp. 424–433
  10. AJ Armstrong, ES Garrett-Mayer, Y-CO Yang, R de Wit, IF Tannock, M Eisenberger. A contemporary prognostic nomogram for men with hormone-refractory metastatic prostate cancer: a TAX327 study analysis. Clin Cancer Res, 13 (2007), pp. 6396–6403.
  11. S Halabi, C-Y Lin, WK Kelly, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol, 32 (2014), pp. 671–677
  12. S Okser, T Pahikkala, A Airola, T Salakoski, S Ripatti, T Aittokallio. Regularized machine learning in the genetic prediction of complex traits. PLoS Genet, 10 (2014), p. e1004754
  13. E Papaemmanuil, M Gerstung, L Bullinger, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med, 374 (2016), pp. 2209–2221
  14. DL Longo, JM Drazen. Data sharing. N Engl J Med, 374 (2016), pp. 276–277.
  15. HI Scher, X Jia, K Chi, et al. Randomized, open-label phase III trial of docetaxel plus high-dose calcitriol versus docetaxel plus prednisone for patients with castration-resistant prostate cancer. J Clin Oncol, 29 (2011), pp. 2191–2198
  16. DP Petrylak, NJ Vogelzang, N Budnik, et al. Docetaxel and prednisone with or without lenalidomide in chemotherapy-naive patients with metastatic castration-resistant prostate cancer (MAINSAIL): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet Oncol, 16 (2015), pp. 417–425
  17. IF Tannock, K Fizazi, S Ivanov, on behalf of the VENICE investigators, et al. Aflibercept versus placebo in combination with docetaxel and prednisone for treatment of men with metastatic castration-resistant prostate cancer (VENICE): a phase 3, double-blind randomised trial. Lancet Oncol, 14 (2013), pp. 760–768
  18. K Fizazi, KS Fizazi, CS Higano, et al. Phase III, randomized, placebo-controlled study of docetaxel in combination with zibotentan in patients with metastatic castration-resistant prostate cancer. J Clin Oncol, 31 (2013), pp. 1740–1747
  19. JB Nelson, K Fizazi, K Miller, et al. Phase III study of the efficacy and safety of zibotentan (ZD4054) in patients with bone metastatic castration-resistant prostate cancer (CRPC). Proc Am Soc Clin Oncol, 29 (2011) abstr 117.
  20. H Hung, C-T Chiang. Estimation methods for time-dependent AUC models with survival data. Can J Stat, 38 (2010), pp. 8–26
  21. Project Data Sphere. Prostate cancer DREAM challenge. https://www.projectdatasphere.org/projectdatasphere/html/pcdc (accessed Oct 21, 2016).
  22. JC Costello, G Stolovitzky. Seeking the wisdom of crowds through challenge-based competitions in biomedical research. Clin Pharmacol Ther, 93 (2013), pp. 396–398
  23. E Bender. Challenges: crowdsourced solutions. Nature, 533 (2016), pp. S62–S64
  24. Bansal, Mukesh, et al. A community computational challenge to predict the activity of pairs of compounds. Nature biotechnology 32.12 (2014): 1213-1222.
  25. JC Costello, LM Heiser, E Georgii, et al. A community effort to assess and improve drug sensitivity prediction algorithms . Nat Biotechnol, 32 (2014), pp. 1202–1212
  26. AA Margolin, E Bilal, E Huang, et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci Transl Med, 5 (2013), p. 181re1
  27. AD Ewing, KE Houlahan, Y Hu, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods, 12 (2015), pp. 623–630
  28. J Saez-Rodriguez, JC Costello, SH Friend, et al. Crowdsourcing biomedical research: leveraging communities as innovation engines. Nat Rev Genet, 17 (2016), pp. 470–486
  29. L Merson, O Gaye, PJ Guerin. N Engl J Med, 374 (2016), pp. 2414–2415. Avoiding data dumpsters: toward equitable and useful data sharing
  30. K Fizazi, M Carducci, M Smith, et al. Denosumab versus zoledronic acid for treatment of bone metastases in men with castration-resistant prostate cancer: a randomised, double-blind study Lancet, 377 (2011), pp. 813–822
  31. S Halabi, EJ Small, PW Kantoff, et al. Prognostic model for predicting survival in men with hormone-refractory metastatic prostate cancer J Clin Oncol, 21 (2003), pp. 1232–1237
  32. O Smaletz, HI Scher, EJ Small, et al. Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration J Clin Oncol, 20 (2002), pp. 3972–3982
  33. BE Bierer, R Li, M Barnes, I Sim. A global, neutral platform for sharing trial data. N Engl J Med, 374 (2016), pp. 2411–2413
  34. B Lewis, O Sartor. The changing landscape of metastatic prostate cancer. Am J Hematol, 11 (2015), pp. 11–20
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