How to take into account the within-patient variability in tumor dynamics, when some lesions respond to treatment and some not ? Does it matter, and is it exacerbated by immunotherapy ? What impact for patient' follow-up ?
Find out our new modeling framework to address these fundamental questions, developed by Marion Kerioui, in collaboration with Rene Bruno, Francois Mercier (Roche/Genentech), Julie Bertrand, Solène Desmée (INSERM) and Christophe Le Tourneau (Institut Curie)! Congrats Marion !
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.