Comparison of overall survival prognostic power of contemporary prognostic scores in prevailing tumor indications
Main Article Content
Abstract
Background: Prognosis of overall survival is instrumental for patient management and can improve conduct of clinical trials and real-world data analysis. With the shift towards cancer immunotherapy, modeling of overall host fitness becomes increasingly important. Here, we compare the performance of contemporary prognostic scores constructed from routinely measured biomarkers.
Patients and methods: We used patient data from the Flatiron Health electronic-health record de-identified oncology database and from 16 clinical studies sponsored by Roche. A total of 64,233 patients were analyzed, covering the most prevailing solid tumor and hematology cancer types.
We compared the Royal Marsden Hospital score (solid tumors), international prognostic index (IPI) (blood tumors), the Eastern Cooperative Oncology Group (ECOG) performance status, and the ‘Real wOrld PROgnostic score (ROPRO)’. OS was modeled from the start of treatment using Kaplan-Meier analysis and Cox regression.
Results: All investigated scores proved to be prognostic, both in RWD and clinical trial data, and in all indications from the respectively intended range of application. The ROPRO uniformly outperformed other prognostic scores. Concordance indices / hazard ratios in the range of [0.64;0.73]/[2.80;4.50] were found for ROPRO, and in the range of [0.53;0.65]/[1.55; 3.10] for the remaining scores. In hematology trials, the IPI came close to the performance of ROPRO.
Conclusions: Strong and easy-to-apply prognostic scores for overall survival exist. The usage of all investigated scores can be recommended. With moderate extra effort, the implementation of ROPRO can create considerable improvement.
Article Details
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References
2. Meera N, Sandhu SS, Sharma AK. Prognostic and Predictive Biomarkers in Cancer. Current Cancer Drug Targets. 2014; 14(5): 477-504.
3. Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med. 2022. Online ahead of print.
4. Nieder C, Dalhaug A. A new prognostic score derived from phase I study participants with advanced solid tumours is also valid in patients with brain metastasis. Anticancer Res. 2010; 30(3):977-979.
5. International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med. 1993; 329(14): 987-994.
6. https://ecog-acrin.org/resources/ecog-performance-status/
7. https://www.mdcalc.com/calc/2320/follicular-lymphoma-international-prognostic-index-flipi
8. Al-Sawaf O, Can Zhang C, Maneesh Tandon M, Arijit Sinha A, Anna-Maria Fink AM, Sandra Robrecht S et al. Venetoclax plus obinutuzumab versus chlorambucil plus obinutuzumab for previously untreated chronic lymphocytic leukaemia (CLL14): follow-up results from a multicentre, open-label, randomised, phase 3 trial. The Lancet. 2020; 21(9):1188-1200.
9. Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. Available at: http://www.fda.gov/regulatory-information/search-fda-guidance-documents/enrichmentstrategies-clinical-trials-support-approval-human-drugs-and-biologicalproducts. Accessed August 6, 2020.
10. Becker T, Weberpals J, Jegg AM, So WV, Fischer A, Weisser M, Schmich F, Rüttinger D, Bauer-Mehren A. An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort. Ann Oncol. 2020;31(11):1561-1568.
11. https://flatiron.com
12. Enderlein G, Cox DR, Oakes D. Analysis of Survival Data. Biom J. 1987; 29(1):114–114.
13. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model, New York: Springer-Verlag, 2000.
14. Kalbfleisch, JD, Prentice, RL. The statistical analysis of failure time data, Second Edition, Wiley Series in Probability and Statistics, 2002.
15. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53(28):457–81.
16. Steck H, Krishnapuram B, Dehing-Oberije C et al. On Ranking in Survival Analysis: Bounds on the Concordance Index. In Platt JC, Koller D, Singer Y, Roweis ST (eds): Adv. Neural Inf. Process. Syst. 20, Curran Associates, Inc., 2008:1209–1216.
17. https://cran.r-project.org/web/packages/timeROC/timeROC.pdf
18. https://clinicaltrials.gov/ct2/show/NCT04158583
19. Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. Available at: http://www.fda.gov/regulatory-information/search-fda-guidance-documents/enrichmentstrategies-clinical-trials-support-approval-human-drugs-and-biologicalproducts.Accessed August 6, 2020.
20. Ko JJ, Xie W, Kroeger N, Lee JL, Rini BI et al. The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-based study. Lancet Oncol. 2015; 16(3):293-300.
21. Forrest LM, McMillan DC, McArdle CS, Angerson WJ, Dunlop DJ. Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer. British Journal of Cancer. 2003. 89: 1028–1030.
22. Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med. 2022.
23. Liu D. Cancer biomarkers for targeted therapy. Biomark Res. 2019. 7(25).
24. Nair M, Sandhu SS, Sharma AK. Prognostic and Predictive Biomarkers in Cancer. Curr Cancer Drug Targets. 2014. 14(5):477-504
25. Reichert ZR,Morgan TM,Li G,Castellanos E, Snow T, Dall'Olio FG et al. Prognostic value of plasma circulating tumor DNA fraction across four common cancer types: a real-world outcomes study. Ann Oncol. 2022;S0923-7534(22):04186-2.
26. Teixeira MM, Borges FC, Ferreira PS, Rocha J, Sepodes B, Torre C. A review of patient-reported outcomes used for regulatory approval of oncology medicinal products in the European Union between 2017 and 2020. Front Med (Lausanne). 2022;9:968272.
27. Chau, N.G., Florescu, A., Chan, K.K. et al. Early mortality and overall survival in oncology phase I trial participants: can we improve patient selection? BMC Cancer. 2011. 11:426.
28. Ivy SP, Siu LL, Garrett-Mayer E, Rubinstein L. Approaches to phase 1 clinical trial design focused on safety, efficiency, and selected patient populations: a report from the clinical trial design task force of the national cancer institute investigational drug steering committee. Clin Cancer Res. 2010. 16(6):1726-36.
29. Jin S, Pazdur R, Sridhara R. Re-Evaluating Eligibility Criteria for Oncology Clinical Trials: Analysis of Investigational New Drug Applications in 2015. Clin Oncol. 2017. 35(33):3745-3752.
30. https://doi.org/10.1016/j.annonc.2021.12.015
31. https://doi.org/10.1016/j.annonc.2021.12.015
32. Carrigan G, Whipple S, Capra WB, et al. Using electronic health records to derive control arms. for early phase single-arm lung cancer trials: proof-of-concept in randomized controlled trials. Clin Pharmacol Ther. 2020. 107(2):369-377.
33. Thorlund K, Dron L, Park JJH, Mills EJ. Synthetic and external controls in clinical trials - a primer for researchers. Clin Epidemiol. 2020.12:457-467.
34. Ventz S, Lai A, Cloughesy TF, et al. Design and evaluation of an external control arm using prior clinical trials and real-world data. Clin Cancer Res. 2019. 25(16):4993-5001.
35. https://clinicaltrials.gov/ct2/show/NCT00567190
36. Baselga J, Cortes J, Kim SB, et al. Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med. 2012. 366:109-119
37. DOI: 10.1056/NEJMoa1413513
38. Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst. 2000. 92:205-21
39. Aykan NF, Özatlı T. Objective response rate assessment in oncology: Current situation and future expectations. World J Clin Oncol. 2020. 11(2):53-73.
40. Belin L, Tan A, De Rycke Y, Dechartres A. Progression-free survival as a surrogate for overall survival in oncology trials: a methodological systematic review. Br J Cancer. 2020. 122(11):1707-1714.
41. Goulart BH, Clark JW, Pien HH, Roberts TG, Finkelstein SN, Chabner BA. Trends in the use and role of biomarkers in phase I oncology trials. Clin Cancer Res. 2007. 13(22 Pt 1):6719-26.
42. Alonso A, Bigirumurame T, Burzykowski T, Buyse M, Molenberghs G, Muchene L, Perualila NJ, Shkedy Z, Van der Elst W. Applied surrogate endpoint evaluation methods with SAS and R. CRC Press. 2016.
43. Burzykowski T, Molenberghs G, Buyse M. The Evaluation of Surrogate Endpoints. 2005. Springer.
44. Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000. 1(1):49-67.
45. Rizopoulos, Dimitris. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. 2012. CRC press.
46. Ganna A, Ingelsson E. 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study. Lancet. 2015;386(9993):533e540.