Abstract
PurposeImaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data.MethodsIndividual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data.ResultsWithin the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response.ConclusionsThis analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms.
Highlights
Assessment of a new treatment in oncology involves recording changes in tumour burden, measured via imaging, and is expressed as tumour size metrics, within a patient and overElectronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.time
In addition to performing a model-based analysis, we assessed whether differences in dynamics between study arms could be visualised from the raw data
Our approach involved the use of a first-order autoregressive (AR) model [9], Yn+1 = αYn, where Yn is the size of an individual lesion at visit n, and α represents the relative change in individual lesion size between current and previous visits
Summary
Drug effect on the target lesions is recorded quantitatively over time, through the sum-of-longest-diameters (SLD) metric, while effects on non-target lesions are recorded qualitatively. The information on drug effect on target and nontarget lesions together with whether a new lesion occurs is used to place patients into one of four response categories: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). It is this PD category which is of interest when considering resistance, as is how the depth of response, CR/PR/SD, relates to time to PD.
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