A joint model of tumor size dynamics and survival with new lesions in EGFR mutation-positive non-small cell lung cancer patients with treatment of gefitinib or carboplatin and paclitaxel.

e20697 Background: Tumor dynamics have been serving as significant predictors in diagnosis, staging, prognosis and treatment of patients with non-small cell lung cancer (NSCLC). The purpose of this study is to propose a joint model that links the longitudinal tumor burden to progression free survival (PFS) with the appearance of new lesion in a population of NSCLC patients with gefitinib treatment or Carboplatin/Paclitaxel. Methods: The model was extended to the estimation of new lesion based on a previously developed tumor size and survival joint model. The derivative of the tumor loads and the probability of new lesions served as biomarkers in the survival submodel. Parameters were estimated from the posterior distribution in a Bayesian framework and numerical study was realized with R and STAN. A total of 434 NSCLC patients with EGFR mutation positive treated with gefitinib or chemotherapy (carboplatin+paclitaxel) from IPASS (‘NCT00322452’) were used to construct the model. Predictions were performed on the IFUM study (‘NCT01203917’) with 102 EGFR mutation positive NSCLC patients. Results: The model performed well in PFS prediction in both within-sample and out-of-sample estimations. Further improvement of model specifications is necessary since the tumor load developing rate and appearance of new-lesion negatively impacted survival predictions. About 90% accuracy was realized by the joint model when recapitulating the outcomes from the response evaluation criteria in solid tumors (RECIST). The appearance of new lesion contributed less than tumor size in accommodating drug effect when comparing progression-specific hazards. Conclusions: This Bayesian joint model well recapitulated the outcomes from the RECIST with sequentially updated tumor size that linked to survival predictions. New insights of relative predictive values were provided by the joint model regarding the components of RESICT. </jats:p

Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics

PURPOSE: Imaging 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.METHODS: Individual 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.RESULTS: Within 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.CONCLUSIONS: This 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.</p