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Development and validation of a multivariable prognostic model in de novo metastatic castrate sensitive prostate cancer

Prostate Cancer and Prostatic Diseases volume 26, pages 119–125 (2023)Cite this article

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Abstract

Background

Metastatic castrate sensitive prostate cancer (mCSPC) is a heterogeneous disease state with variable prognosis. Although several life-prolonging systemic agents are available, there is no robust multivariable model to predict prognosis and improve risk stratification in mCSPC. The objective of this study was to build and validate a multivariable prognostic model to predict overall survival (OS) in mCSPC.

Methods

We used data from LATITUDE, a phase III randomized controlled trial in which men with de novo mCSPC were randomly allocated to either ADT plus abiraterone or ADT with placebo. Patients with non-missing data (n = 1,058) were randomly split in a 70:30 ratio to training (n = 743) and testing (n = 315) sets. Elastic net regression was used for variable selection. A multivariable Cox regression model for OS was then fitted using the selected variables. The predictive accuracy of the model was assessed on the testing set using the time-dependent area under curve (tAUC) with bootstrapped confidence intervals [CI] primarily for OS and secondarily for radiographic progression-free survival (rPFS).

Results

The 11 prognostic variables in the final model were performance status, number of skeletal metastases, Gleason score, presence of liver metastasis, worst pain score, albumin, lactate dehydrogenase, prostate-specific antigen, hemoglobin, and treatment regimen. The tAUC for predicting OS at 2- and 3-years was 0.74 (95% CI, 0.67–0.80) and 0.72 (95% CI, 0.65–0.77), respectively. The tAUC for rPFS at 2- and 3-years was 0.72 (95% CI, 0.65–0.77) and 0.77 (95% CI, 0.70–0.82), respectively.

Conclusions

A prognostic model for men with de novo mCSPC was developed and validated in an independent testing set. Our model had high accuracy for predicting OS and rPFS. The model includes commonly used clinical and laboratory parameters and can guide risk stratification of these patients for participation in future trials.

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Fig. 1: Calibration plots for overall survival in the testing dataset.
Fig. 2: Prognostic model predicting overall survival probability.
Fig. 3: Kaplan–Meier survival curves by the two- and three-risk groups in the testing set.

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Data availability

This study, carried out under YODA Project # 2021–4565, used data obtained from the Yale University Open Data Access Project, which has an agreement with JANSSEN RESEARCH & DEVELOPMENT, L.L.C. The interpretation and reporting of research using this data are solely the responsibility of the authors and does not necessarily represent the official views of the Yale University Open Data Access Project or JANSSEN RESEARCH & DEVELOPMENT, L.L.C.

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Funding

The original LATITUDE study was funded by Janssen Oncology. However, the employees of the funder had no role in study design, data collection, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all data in the study and final responsibility for the decision to submit for publication.

Author information

Author notes

  1. These authors contributed equally: Soumyajit Roy, Yilun Sun, Cristopher J. D. Wallis, Scott C. Morgan, Daniel E. Spratt, Fred Saad, Shawn Malone.

Authors and Affiliations

  1. Department of Radiation Oncology, Rush University Medical Center, Chicago, IL, USA

    Soumyajit Roy

  2. Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Yilun Sun

  3. Department of Urology, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada

    Cristopher J. D. Wallis

  4. Division of Radiation Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON, Canada

    Scott C. Morgan, Scott Grimes, Julia Malone & Shawn Malone

  5. Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA

    Amar U. Kishan

  6. Institute of Computational Biology, Bengaluru, India

    Dibya Mukherjee

  7. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA

    Daniel E. Spratt

  8. Department of Surgery, Université de Montréal, Montreal, QC, Canada

    Fred Saad

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Contributions

Conception and design: SR and SM. Administrative support: SM, CW, SR, DM, JM, and DS. Provision of study material or patients: SM, SM, SR, CW, and FS. Collection and assembly of data: SM, SR, YS, SM, FS, AUK, DM, and SG. Data analysis and interpretation: SR, YS. Manuscript writing: All authors. Final approval of manuscript: All authors. Accountable for all aspects of the work: All authors.

Corresponding authors

Correspondence to Soumyajit Roy or Shawn Malone.

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Competing interests

Outside of this work, Dr. Malone has received honoraria from Astellas, Bayer, Janssen, and Sanofi; and travel and accommodations support from TerSera and Sanofi. Outside of this work, Dr. Morgan reports personal fees from Astellas, Bayer, Janssen, and TerSera. Dr. Roy reports a research grant from Abbvie-Canadian Association of Radiation Oncology. Dr. Wallis reports other from Janssen Oncology, other from SESEN Bio, outside the submitted work. Honoraria. Dr. Kishan reports personal fees and research support from ViewRay, Inc., and personal fees from Varian Medical Systems, Inc., and Intelligent Automation, Inc., outside the submitted work. Dr. Saad reports grants, personal fees, and non-financial support from Janssen, during the conduct of the study; grants, personal fees, and non-financial support from Astellas, grants, personal fees, and non-financial support from Bayer, outside the submitted work. Dr. Spratt reports personal fees from Blue Earth, personal fees from Janssen, personal fees from AstraZeneca, outside the submitted work. The other coauthors have no competing financial interests.

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Roy, S., Sun, Y., Wallis, C.J.D. et al. Development and validation of a multivariable prognostic model in de novo metastatic castrate sensitive prostate cancer. Prostate Cancer Prostatic Dis 26, 119–125 (2023). https://doi.org/10.1038/s41391-022-00560-3

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  • DOI: https://doi.org/10.1038/s41391-022-00560-3

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