Divining responder populations from survival data. (12th March 2019)

• Record Type: Journal Article
• Authors: Rahman, R; Ventz, S; Fell, G; Vanderbeek, A M; Trippa, L; Alexander, B M
• Is Part Of: Annals of oncology. Volume 30:Number 6(2019)
• Page Start: 1005
• Page End: 1013
• Publication Date: 2019-03-12
• Subjects: biomarkers -- subgroup effects -- survival analysis -- trial simulations; Oncology -- Periodicals; 616.992
• DOI: 10.1093/annonc/mdz087
• Languages: English
• ISSNs: 0923-7534

Abstract

Abstract: Background: Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers. Materials and methods: We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses. Results: Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data. Conclusions: Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.