Julia Wilkerson1, Kald Abdallah2, Charles Hugh-Jones3, Greg Curt2, Mace Rothenberg4, Ronit Simantov4, Martin Murphy5, Joseph Morrell5, Joel Beetsch6, Daniel J Sargent7, Howard I Scher8, Peter Lebowitz9, Richard Simon10, Wilfred D Stein11, Susan E Bates12, Tito Fojo13. 1. Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. 2. AstraZeneca, Gaithersburg, MD, USA. 3. Sanofi US, Bridgewater, NJ, USA. 4. Pfizer Inc, New York, NY, USA. 5. Project Data Sphere LLC, Cary, NC, USA. 6. Celgene Corporation, Summit, NJ, USA. 7. Mayo Medical Center, Rochester, MN, USA. 8. Memorial Sloan Kettering Cancer Center, New York, NY, USA. 9. Janssen, the Pharmaceutical Companies of Johnson & Johnson, New Brunswick, NJ, USA. 10. Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. 11. Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel. 12. Division of Medical Oncology, Department of Medicine, Columbia University/New York Presbyterian Hospital, Manhattan, NY, USA; James J Peters VA Medical Center, Bronx, NY, USA. 13. Division of Medical Oncology, Department of Medicine, Columbia University/New York Presbyterian Hospital, Manhattan, NY, USA; James J Peters VA Medical Center, Bronx, NY, USA. Electronic address: atf2116@cumc.columbia.edu.
Abstract
BACKGROUND: We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. METHODS: In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. FINDINGS: Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. INTERPRETATION: The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. FUNDING: None.
BACKGROUND: We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. METHODS: In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. FINDINGS: Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. INTERPRETATION: The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. FUNDING: None.
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