Development and external validation of a computational approach for accelerated read out of randomized clinical trials in metastatic prostate cancer.
Year: 2026
Type: conference-abstract
Abstract: 1502 Background: While overall survival (OS) is the gold standard endpoint for clinical trials in metastatic prostate cancer (mPC), it requires years of follow-up. We sought to develop and externally validate a computational model for accelerated OS readout based on short term (4 months) PSA kinetic data. Methods: Longitudinal PSA data were obtained from 8 completed phase 3 mPC trials. Using data from the first 4 months on trial, 18 different PSA kinetic variables were developed, including 50% and 90% decline in PSA, PSA=0.1, and PSA=0.2 at 1, 2, 3, and 4 months, and slope and base of the exponential fit of PSA. TITAN, COU-AA-301, and ACIS comprised the training cohort. Data from those trials was used to model OS using a relaxed LASSO approach. The resulting model was applied to 5 validation trials (see Table 1) to simulate 1,000 potential outcomes for each trial (after Harden and Kropko). For the CHAARTED trial, a model using only PSA-kinetic data was used as most baseline variables were missing. The distribution of 1,000 simulated Hazard Ratios (HR) for each trial was compared against the reported HR. Results: Overall, > 100,000 PSA values were used with a total > 7,500 eligible study pts, with ~ 70,000 PSA values and ~5,000 patients comprised the validation cohort. The model identified baseline PSA at treatment, PSA50 at 4 months, and the PSA slope as the most informative PSA predictors of OS. Table 1 below compares the median predicted HR and 95%-interpercentile range from the 1,000 simulations with the reported HR for each validation trial. The model correctly predicted the OS point estimate and 95%-confidence interval using the first 4 months of PSA data in every validation trial. Conclusions: A computational simulation approach to predict the OS outcome of phase 3 mPC trials based on PSA kinetics from the first 4 months on trial was developed. This model correctly predicted OS outcomes in 5 completed validation phase 3 trials. This included phase 3 trials with both positive and negative outcomes for OS as well as in both hormone sensitive and resistant mPC, and involving AR signaling inhibitors, chemotherapy and a PARP inhibitor. This model is being further validated with additional completed phase 3 trials, and once prospectively validated, has the potential to significantly shorten the follow up required for OS readout from phase 3 trials in mPC. Predicted vs. reported HR of each trial. Validation Trial Predicted HR (median [95 th interpercentile range]) Actual HR [95%-CI] LATITUDE 0.64 [0.54 – 0.74] 0.66 [0.56 – 0.78] COU-AA-302 0.69 [0.60 – 0.80] 0.81 [0.70 – 0.93] MAGNITUDE 1.06 [0.88 – 1.26] 0.97 [0.79 –1.19]* CALGB 90401 0.89 [0.78 – 1.01] 0.91 [0.78 – 1.05] CHAARTED (Unselected) 0.79 [0.61 – 0.98] 0.72 [0.59 – 0.89] CHAARTED (High Volume) 0.72 [0.53 – 0.92] 0.63 [0.50 – 0.79] CHAARTED (Low Volume) 0.95 [0.61 – 1.52] 1.04 [0.70 – 1.55] *Observed HR used as overall OS HR not reported in manuscript.
Source: Journal of Clinical Oncology
Authors: Ali Sabbagh, David Quigley, Nicholas Lillis, Li Zhang, Jean Feng, Isabel D. Friesner, Adina Bailey, Rahul Raj Aggarwal, Meera Reddy Chappidi, Hari Singhal, Ke Zhang, Joel Greshock, Jinhui Li, Margaret K. Yu, Christopher Sweeney, William Kevin Kelly, Michael A. Carducci, Yu-Hui Chen, Eric J. Small, Julian C. Hong
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: Prostate Cancer Foundation
Award: G4357581765
OpenAlex ID: W7162487381
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