Assessing the Increased Variability in Individual Lesion Kinetics During Immunotherapy: Does It Exist, and Does It Matter?

Abstract

PURPOSE Several studies have raised the hypothesis that immunotherapy may exacerbate the variability in individual lesions, increasing the risk of observing divergent kinetic profiles within the same patient. This questions the use of the sum of the longest diameter to follow the response to immunotherapy. Here, we aimed to study this hypothesis by developing a model that estimates the different sources of variability in lesion kinetics, and we used this model to evaluate the impact of this variability on survival. METHODS We relied on a semimechanistic model to follow the nonlinear kinetics of lesions and their impact on the risk of death, adjusted on organ location. The model incorporated two levels of random effects to characterize both between- and within-patient variability in response to treatment. The model was estimated on 900 patients from a phase III randomized trial evaluating programmed death-ligand 1 checkpoint inhibitor atezolizumab versus chemotherapy in patients with second-line metastatic urothelial carcinoma (IMvigor211). RESULTS The within-patient variability in the four parameters that characterize individual lesion kinetics represented between 12% and 78% of the total variability during chemotherapy. Similar results were obtained during atezolizumab, except for the durability of the treatment effects, for which the within-patient variability was markedly larger than during chemotherapy (40% v 12%, respectively). Accordingly, the occurrence of divergent profile consistently increased over time in patients treated with atezolizumab and was equal to about 20% after 1 year of treatment. Finally, we show that accounting for the within-patient variability provided a better prediction of most at-risk patients than a model relying solely on the sum of the longest diameter. CONCLUSION Within-patient variability provides valuable information for the assessment of treatment efficacy and the detection of at-risk patients.

Comparison of two‐stage and joint TGI‐OS modeling using data from six atezolizumab clinical studies in patients with metastatic non‐small cell lung cancer

“…Although none of these metrics provided a better fit to the data than log(K G ) (see Table S4), these metrics could nonetheless be useful to understand in greater detail other effects of treatment, in particular on the between-lesion variability. 5,15 To conclude, our study supports that the modeling approach used both two-stage and joint models provide to link tumor size data and OS may not play an essential role in largely similar results and accurately predicting the outcome of clinical trials in NSCLC. In addition, the two-stage approach is the easiest to implement and a computationally much faster viable alternative.…”

“…An advantage of the joint model is the capability to investigate for additional metrics, such as the time‐continuous tumor size, TS(t). Although none of these metrics provided a better fit to the data than log( K G ) (see Table S4), these metrics could nonetheless be useful to understand in greater detail other effects of treatment, in particular on the between‐lesion variability 5,15 …”

“…Although none of these metrics provided a better fit to the data than log( K G ) (see Table S4 ), these metrics could nonetheless be useful to understand in greater detail other effects of treatment, in particular on the between‐lesion variability. 5 , 15 …”

“…[12][13][14] However, this comes at a certain cost, with higher computational times due to the numerical complexity of maximizing the likelihood while fitting both processes simultaneously. Although joint modeling has been successfully used to elucidate the association between tumor size and OS and enabled individualized medicine by providing an accurate prediction of individual survival, 5,7,15 the two-stage approach remains a more practical and actionable approach during drug development to analyze clinical trials and conclude on treatment efficacy. 8,9,16 For that purpose, we here used a large database of six clinical studies evaluating atezolizumab (an immunotherapy) in monotherapy or in combination with chemotherapies in 3699 patients with non-small cell lung cancer (NSCLC) to evaluate the differences of both approaches in terms of parameter estimates, magnitude of covariate effects, and ability to predict OS.…”

“… 12 , 13 , 14 However, this comes at a certain cost, with higher computational times due to the numerical complexity of maximizing the likelihood while fitting both processes simultaneously. Although joint modeling has been successfully used to elucidate the association between tumor size and OS and enabled individualized medicine by providing an accurate prediction of individual survival, 5 , 7 , 15 the two‐stage approach remains a more practical and actionable approach during drug development to analyze clinical trials and conclude on treatment efficacy. 8 , 9 , 16 …”