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Tumor Growth Rates Derived from Data for Patients in a Clinical Trial Correlate Strongly with Patient Survival: A Novel Strategy for Evaluation of Clinical Trial Data

^aMedical Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA; ^bDepartment of Biological Chemistry, Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel; ^cHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA; ^dHebrew University-Hadassah School of Public Health, Hebrew University, Ein Kerem Medical Centre, Jerusalem, Israel

Correspondence: Correspondence: Tito Fojo, M.D., Ph.D., Medical Oncology Branch, National Cancer Institute, Center for Cancer Research, National Institutes of Health, Building 10, Room 12N226, 9000 Rockville Pike, Bethesda, Maryland 20892, USA. Telephone: 301-402-1357; Fax: 301-402-1608; e-mail: tfojo{at}helix.nih.gov

Received March 27, 2008; accepted for publication August 13, 2008.

ABSTRACT

Purpose. The slow progress in developing new cancer therapies can be attributed in part to the long time spent in clinical development. To hasten development, new paradigms especially applicable to patients with metastatic disease are needed.

Patients and Methods. We present a new method to predict survival using tumor measurement data gathered while a patient with cancer is receiving therapy in a clinical trial. We developed a two-phase equation to estimate the concomitant rates of tumor regression (regression rate constant d) and tumor growth (growth rate constant g).

Results. We evaluated the model against serial levels of prostate-specific antigen (PSA) in 112 patients undergoing treatment for prostate cancer. Survival was strongly correlated with the log of the growth rate constant, log(g) (Pearson r = –0.72) but not with the log of the regression rate constants, log(d) (r = –0.218). Values of log(g) exhibited a bimodal distribution. Patients with log(g) values above the median had a mortality hazard of 5.14 (95% confidence interval, 3.10–8.52) when compared with those with log(g) values below the median. Mathematically, the minimum PSA value (nadir) and the time to this minimum are determined by the kinetic parameters d and g, and can be viewed as surrogates.

Conclusions. This mathematical model has applications to many tumor types and may aid in evaluating patient outcomes. Modeling tumor progression using data gathered while patients are on study, may help evaluate the ability of therapies to prolong survival and assist in drug development.

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