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

Predicting tumor dynamics in treated patients from patient-derived-xenograft mouse models: a translational model-based approach

This study presents a translational modeling framework designed to predict tumor size dynamics in cancer patients undergoing anticancer treatment, using data from patient-derived xenograft (PDX) mice. In the first step, a population tumor growth inhibition (TGI) model to estimate the distribution of exponential tumor growth rates and anticancer drug potency in PDX mice was built. Then, model parameters were allometrically scaled from mice to humans to inform a TGI model predicting tumor size dynamics in cancer patients. Longitudinal tumor dynamics predicted by the PDX-informed TGI model were expressed in terms of tumor progression events to allow validation against literature time-to-progression (TTP) data. The proposed approach was tested on two case studies: gemcitabine treatment for pancreatic cancer and sorafenib treatment for hepatocellular cancer. The framework successfully predicted median tumor size dynamics, closely aligned with clinical TTP curves for gemcitabine-pancreatic cancer case study. While predictions for extreme tumor size percentiles highlighted potential avenues for refinement, such as incorporating resistance mechanisms, the overall accuracy underscored the goodness of the approach. For the sorafenib-hepatocellular cancer case study, the framework provided plausible tumor size predictions, with TTP curves closely aligned with clinical observations, despite the limited availability of clinical data prevented a full validation. Overall, the translational modeling framework showed potential for predicting tumor dynamics in cancer patients, with results suggesting its applicability as a valid tool to support early decision-making in oncology.

Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics

Objective

Assess the contribution of early tumor growth dynamics modeling to predict clinical outcomes in non-small cell lung cancer patients receiving immunotherapy, alongside standard RECIST 1.1 criteria.

Methods

Our retrospective studies used data from 861 patients with advanced NSCLC enrolled in three randomized Phase III trials evaluating immunotherapy plus chemotherapy were analyzed. Tumor size measurements up to two follow-up time points were used to fit a novel Gompertz model and estimate growth rate (GR) and kinetic parameters representing depth of response (A), speed of response (B), and long-term modulation (M). Correlations between these early tumor growth parameters and clinical outcomes such as progression-free survival (PFS) and time to response (TTR) were assessed. Descriptive and discriminative analyses were performed to delineate tumor growth dynamics across various response profiles based on RECIST 1.1 criteria.

Results

The novel Gompertz model accurately described early tumor growth kinetics in 861 non-small cell lung cancer patients treated with immunotherapy. Lower growth rate (GR) and model parameter M were associated with longer progression-free survival (PFS) (HR=0.897 and 7.47x10^-7, respectively). Higher GR and parameter A correlated with shorter time to response (HR=0.575 and 0.696, respectively). Responders had significantly lower A (p=1.51e-53) and higher GR (p=0.4e-12) than non-responders. Non-durable stable disease patients had higher GR (p=0.0001) and parameter B (p=0.0002) compared to late responders. Early tumor growth parameters showed potential for predicting long-term outcomes and treatment response patterns.

Impact of tumour size measurement inter-operator variability on model-based drug effect evaluation

Purpose

During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model.

Methods

One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre. A visual analysis was performed to identify trends within the profiles over time. Linear regression and relative error ratios were used to explore the inter-operator variability of raw TS measurements and model-based estimates.

Results

While correlation between patient-level estimates of drug effect was poor (r² = 0.28), variability between the study-level estimates was much less affected (9%).

Conclusions

The global evaluation of drug effect using modelling approaches might not be affected by inter-operator variability. However, the exploration of covariates for drug effect and the characterisation of an exposure–tumour shrinkage relationship seems limited by the high measurement variability that translates to a poor correlation of individual drug effect estimates. This might be addressed by the use of more precise computer-aided measurement methods.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04049-5) contains supplementary material, which is available to authorized users.