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Predicting Overall Survival and Progression-Free Survival Using Tumor Dynamics in Advanced Breast Cancer Patients

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ABSTRACT

Prediction of survival endpoints, e.g., overall survival (OS) and progression-free survival (PFS), based on early observations, i.e., tumor size, may facilitate early decision making in oncology drug development. In this paper, using data from six randomized trials for first- or second-line advanced breast cancer (ABC) treatments with various mechanisms of action, tumor size change from baseline at different observation time points was evaluated as a predictor for survival endpoints using different modeling approaches. The aim is to establish a predictive model where tumor size change from baseline can be used as a treatment independent predictive marker for PFS and OS in first- and second-line ABC. The results showed that tumor size change at single time point (TSP) or up to certain time points as a time-varying covariate (TSTVC) were significant predictors for OS and PFS in the survival models along with other covariates identified for each line of treatment. TSP and TSTVC models performed similarly for first-line treatments; TSTVC performed significantly better for second-line treatments. Eight weeks was selected as the recommended early evaluation time of tumor size change to predict OS and PFS in both first- and second-line treatment, while better prediction can be achieved for first-line OS by using 16 weeks tumor size change. The result of this study is treatment independent and can be used to predict the outcome of the clinical trials using early readout of tumor size change for the classes of drugs that have been evaluated in this study.

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ACKNOWLEDGEMENTS

This study was sponsored by Pfizer. Editorial support was provided by Complete Healthcare Communications, LLC (West Chester, PA), a CHC Group company, and was funded by Pfizer.

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Authors and Affiliations

  1. Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Hyeong-Seok Lim

  2. Clinical Pharmacology, Global Product Development, Pfizer, 10555 Science Center Dr., San Diego, California, 92121, USA

    Wan Sun, Kourosh Parivar & Diane Wang

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  1. Hyeong-Seok Lim
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  2. Wan Sun
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  4. Diane Wang
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Correspondence to Diane Wang.

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Conflict of Interest

K.P. and D.W. are employees of and shareholders of Pfizer Inc. W.S. was a former employee of Pfizer and a current shareholder of Pfizer Inc. H.-S.L. was a research fellow with Pfizer when this research was conducted.

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Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hyeong-Seok Lim participated in this study during an international fellowship at Pfizer.

Wan Sun was employed at Pfizer Inc at the time of study conduct and manuscript development.

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Figure S1

Diagnostic plots for tumor size model (Gompertz growth with exponential decay model): time course of observed (shown as circles) and predicted tumor size (shown as broken lines) up to 8 weeks (A), 16 weeks (C) and for all available data (E) of some randomly selected patients; comparison of observed and predicted tumor size up to 8 weeks (B), 16 weeks (D) and for all available data (F) of all included patients. Circle points represent individual data points; solid and broken lines represent the reference line (diagonal line) and linear regression line based on individual data points. (PNG 983 kb)

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Lim, HS., Sun, W., Parivar, K. et al. Predicting Overall Survival and Progression-Free Survival Using Tumor Dynamics in Advanced Breast Cancer Patients. AAPS J 21, 22 (2019). https://doi.org/10.1208/s12248-018-0290-x

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