Change in tumor size by RECIST correlates linearly with overall survival in phase I oncology studies

Abstract

Purpose: RECIST is used to quantify tumor changes during exposure to anticancer agents. Responses are categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Clinical trials dictate a patient's management options based on the category into which his or her response falls. However, the association between response and survival is not well studied in the early trial setting.

Patients and methods: To study the correlation between response as quantified by RECIST and overall survival (OS, the gold-standard survival outcome), we analyzed 570 participants of 24 phase I trials conducted between October 2004 and May 2009, of whom 468 had quantifiable changes in tumor size. Analyses of Kaplan-Meier estimates of OS by response and null Martingale residuals of Cox models were the primary outcome measures. All analyses are landmark analyses.

Results: Kaplan-Meier analyses revealed strong associations between change in tumor size by RECIST and survival (P = 4.5 × 10(-6) to < 1 × 10(-8)). The relationship was found to be near-linear (R(2) = 0.75 to 0.92) and confirmed by the residual analyses. No clear inflection points were found to exist in the relationship between tumor size changes and survival.

Conclusion: RECIST quantification of response correlates with survival, validating RECIST's use in phase I trials. However, the lack of apparent boundary values in the relationship between change in tumor size and OS demonstrates the arbitrary nature of the CR/PR/SD/PD categories and questions emphasis placed on this categorization scheme. Describing tumor responses as a continuous variable may be more informative than reporting categoric responses when evaluating novel anticancer therapies.

Fig 1.

(A), (C), and (E) Kaplan-Meier overall survival (OS) based on tumor response using landmark analyses. Patient cohorts are separated by 15% changes in best tumor response (except at the two extremes where cohorts are enlarged due to small patient numbers). (B), (D), and (F) For cohorts that reached a median OS, each cohort's median is plotted against its mean change in tumor burden. Linearity is assessed by using least-squares fit and calculating the correlation coefficient R². Landmark in A-B, 1.9 months; C-D, 4 months; E-F, 8.2 months. PFS, progression-free survival.

Fig 2.

Null Martingale residual analyses of overall survival (OS) using indicated landmark time points. Squares represent individual patients. Blue line (loess line) represents best fit by local regression as described in Patients and Methods. PFS, progression-free survival.

Fig 3.

(A) Kaplan-Meier analysis of progression-free survival (PFS) based on tumor response using landmark analysis. Cohorts are separated by 15% changes in best tumor response (except at the extreme representing the greatest shrinkage, where cohort is enlarged because of small patient numbers). (B) Each cohort's median PFS is plotted against its mean change in tumor burden. Linearity is assessed by least-squares fit and calculating the correlation coefficient R². (C) Null Martingale residual analysis. Squares represent individual patients. Blue line (loess line) represents best fit by local regression as described in Patients and Methods. In (A) and (B), the green line is labeled +15% to 30% to coincide with Figure 1 (although no patients with change > 20% are included owing to all having progressive disease by the 1.9-month landmark).

Fig 4.

Waterfall plot of best response by RECIST. Four hundred sixty-eight patients had quantifiable changes that are illustrated in the figure. The remaining 102 patients had new lesions at first restaging and are therefore unquantifiable and not shown in the figure.

Fig 5.

Kaplan-Meier analysis of overall survival (OS) for patients with new lesions on first restaging using the landmark analysis. Gold curves represent patients with measurable lesions and are identical to the curves in Figs 1A, 1C, and 1E. Blue curves represent survival rates for patients with best response of new lesion(s). For patients with new lesions, (A) OS, 3.9; n = 93; (B) OS, 3.2 months; n = 64; (C) OS, 4.9 months; n = 26. PFS, progression-free survival.

Fig A1.

(A), (C), and (E) Kaplan-Meier overall survival (OS) rates based on tumor response using landmark analyses. Cohorts are separated such that there are equal numbers per cohort. (B), (D), and (F) For cohorts that reached a median OS, each cohort's median is plotted against its mean change in tumor burden. Linearity is assessed by least-squares fit and calculating the correlation coefficient R². Landmark in A-B, 1.9 months; C-D, 4 months; E-F, 8.2 months. PFS, progression-free survival.

Fig A2.

(A) Kaplan-Meier analysis of progression-free survival (PFS) based on tumor response using the landmark analysis. Patient cohorts are separated such that there are equal numbers per cohort (except at the extreme representing the greatest shrinkage, where the cohort is enlarged owing to small patient numbers). (B) Each cohort's median PFS is plotted against its mean change in tumor burden. Linearity is assessed by using least-squares fit and calculating the correlation coefficient R².