Development and external validation of a computational approach for accelerated read out of randomized clinical trials in metastatic prostate cancer.

A Ali Sabbagh (Memorial Sloan Kettering Cancer Center, New York, NY) D David Quigley (Department of Physics, University of Warwick 2 , Gibbet Hill Road, Coventry CV4 7AL,) N Nicholas Lillis (University of California, San Francisco, San Francisco, CA) L Li Zhang J Jean Feng (UCSF, San Francisco, California, United States) I Isabel D. Friesner (University of Colorado Health Science Center, Aurora, CO) A Adina Bailey (University of California, San Francisco, San Francisco, CA) R Rahul Raj Aggarwal (Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA) M Meera Reddy Chappidi (University of Washington, Seattle, WA) H Hari Singhal (Janssen Research and Development, San Francisco, CA) K Ke Zhang J Joel Greshock (Johnson & Johnson Research and Development, Cambridge, MA) J Jinhui Li (School of Environment, Tsinghua University, Beijing, China.) M Margaret K. Yu (ARTBIO, INC., Cambridge, MA) C Christopher Sweeney (South Australian Immunogenomics Cancer Institute, Adelaide University, Adelaide, SA, Australia) W William Kevin Kelly (Thomas Jefferson University Hospital, Philadelphia, PA) M Michael A. Carducci (Johns Hopkins, Baltimore, MD) Y Yu-Hui Chen (Dana-Farber Cancer Institute, Boston, MA) E Eric J. Small J Julian C. Hong (University of California, San Francisco, San Francisco, CA)

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.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 1502-1502
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (20)

A

Ali Sabbagh

Memorial Sloan Kettering Cancer Center, New York, NY

D

David Quigley

Department of Physics, University of Warwick 2 , Gibbet Hill Road, Coventry CV4 7AL,

N

Nicholas Lillis

University of California, San Francisco, San Francisco, CA

L

Li Zhang

J

Jean Feng

UCSF, San Francisco, California, United States

I

Isabel D. Friesner

University of Colorado Health Science Center, Aurora, CO

A

Adina Bailey

University of California, San Francisco, San Francisco, CA

R

Rahul Raj Aggarwal

Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA

M

Meera Reddy Chappidi

University of Washington, Seattle, WA

H

Hari Singhal

Janssen Research and Development, San Francisco, CA

K

Ke Zhang

J

Joel Greshock

Johnson & Johnson Research and Development, Cambridge, MA

J

Jinhui Li

School of Environment, Tsinghua University, Beijing, China.

M

Margaret K. Yu

ARTBIO, INC., Cambridge, MA

C

Christopher Sweeney

South Australian Immunogenomics Cancer Institute, Adelaide University, Adelaide, SA, Australia

W

William Kevin Kelly

Thomas Jefferson University Hospital, Philadelphia, PA

M

Michael A. Carducci

Johns Hopkins, Baltimore, MD

Y

Yu-Hui Chen

Dana-Farber Cancer Institute, Boston, MA

E

Eric J. Small

J

Julian C. Hong

University of California, San Francisco, San Francisco, CA