1. Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.
- Author
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Parker JL, Kuzulugil SS, Pereverzev K, Mac S, Lopes G, Shah Z, Weerasinghe A, Rubinger D, Falconi A, Bener A, Caglayan B, Tangri R, and Mitsakakis N
- Subjects
- Breast Neoplasms chemistry, Breast Neoplasms drug therapy, Breast Neoplasms genetics, Carcinoma, Non-Small-Cell Lung chemistry, Carcinoma, Non-Small-Cell Lung drug therapy, Carcinoma, Non-Small-Cell Lung genetics, Clinical Trials, Phase I as Topic, Clinical Trials, Phase II as Topic, Clinical Trials, Phase III as Topic, Colorectal Neoplasms chemistry, Colorectal Neoplasms drug therapy, Colorectal Neoplasms genetics, Databases, Factual statistics & numerical data, Female, Genetic Markers, Humans, Lung Neoplasms chemistry, Lung Neoplasms drug therapy, Lung Neoplasms genetics, Male, Medical Oncology, Melanoma chemistry, Melanoma drug therapy, Melanoma genetics, Neoplasms chemistry, Neoplasms genetics, Risk, Skin Neoplasms chemistry, Skin Neoplasms drug therapy, Skin Neoplasms genetics, Stochastic Processes, Time Factors, Treatment Failure, Antineoplastic Agents therapeutic use, Biomarkers, Tumor classification, Biomarkers, Tumor genetics, Clinical Trials as Topic classification, Clinical Trials as Topic statistics & numerical data, Drug Approval methods, Drug Approval statistics & numerical data, Markov Chains, Neoplasms drug therapy
- Abstract
Purpose: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology., Methods: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states., Results: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers., Conclusion: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)
- Published
- 2021
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