On the relationship between tumour growth rate and survival in non-small cell lung cancer.

Hitesh B Mistry

doi:10.7717/peerj.4111

Abstract

A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.

Highlights

A key question being posed in early oncology drug development at the end of phase II is: given past imaging information, what is the overall survival (OS) outcome in a phase III study likely to be? This question has been primarily addressed by analysing efficacy data using RECIST criteria (Response Evaluation Criteria In Solid Tumours) (Therasse et al, 2000; Eisenhauer et al, 2009)
Data from the control arm of three randomised phase III studies in NSCLC were collected: Erlotinib (Scagliotti et al, 2012), Docetaxel (Ramlau et al, 2012) and Paclitaxel/Carboplatin (Socinski et al, 2012). (Note, these studies were chosen as they were the only ones available at the time within ProjectDataSphere.) Both the Erlotinib and Docetaxel studies were conducted within a patient population who were pre-treated with doublet chemotherapy whereas the Paclitaxel/Carboplatin study was conducted in therapy naïve patients
I found they did not correlate to overall survival minus progression free survival, reverse alignment. These results show that growth rate (GR) and to tumour re-growth (TTG) provide no information on the survival prognosis of patients post progression

Summary

Introduction

A key question being posed in early oncology drug development at the end of phase II (and increasingly at the end of phase I expansion studies) is: given past imaging information, what is the overall survival (OS) outcome in a phase III study likely to be? This question has been primarily addressed by analysing efficacy data using RECIST criteria (Response Evaluation Criteria In Solid Tumours) (Therasse et al, 2000; Eisenhauer et al, 2009). The drug’s effect on target lesions are reported via the Sum of Longest Diameters (SLD) marker which is a continuous variable whereas drug effect on non-target lesions is reported via qualitative descriptions: increase, decrease or no-change. Both are recorded over time as is the occurrence of a new lesion. An important continuous metric of interest is progression free survival (PFS) time: the time from treatment initiation to PD or death This time-point is of importance for two reasons; firstly it marks the end of imaging data collection and secondly, it signals the end of the current treatment and the beginning of the next. The debate moves onto how best to analyse the imaging data collected up until disease progression

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