Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow‐Up

Abstract

We characterized the association between tumor size kinetics and survival in patients with advanced urothelial carcinoma treated with atezolizumab (anti‐programmed death‐ligand 1, Tecentriq) using a joint model. The model, developed on data from 309 patients of a phase II clinical trial, identified the time‐to‐tumor growth and the instantaneous changes in tumor size as the best on‐treatment predictors of survival. On the validation dataset containing data from 457 patients from a phase III study, the model predicted individual survival probability using 3‐month or 6‐month tumor size follow‐up data with an area under the receptor‐occupancy curve between 0.75 and 0.84, as compared with values comprised between 0.62 and 0.75 when the model included only information available at treatment initiation. Including tumor size kinetics in a relevant statistical framework improves the prediction of survival probability during immunotherapy treatment and may be useful to identify most‐at‐risk patients in “real‐time.”

Tumor Time‐Course Predicts Overall Survival in Non‐Small Cell Lung Cancer Patients Treated with Atezolizumab: Dependency on Follow‐Up Time

“…and absolute TS time‐course and percent change in TS over time (Zheng et al .) predicted OS in patients treated with checkpoint inhibitors . It is also reasonable to expect that these types of analyses will gain further interest and impact in the future…”

“…Our results are comparable to those previously reported in patients treated with checkpoint inhibitors, with respect to relationships between OS and TS-related metrics, where time to tumor growth and current rate of tumor size changes (Tardivon et al), tumor growth rate (Claret et al) and absolute TS time-course and percent change in TS over time (Zheng et al) predicted OS in patients treated with checkpoint inhibitors. [5][6][7] It is also reasonable to expect that these types of analyses will gain further interest and impact in the future. 30 IL-18 and AUC were identified as predictors of TS in the earlier PK-IL18-TS analysis, where a high IL-18 response on day 21 after start of atezolizumab treatment was related to a sustained tumor growth inhibition.…”

“…Population modeling has previously demonstrated usefulness in drug development by identifying relationships between predictors, such as tumor size (TS) dynamics, treatment related and patient characteristics, and OS . The experience of using such extensive modeling framework in cancer immunotherapy is, however, limited and inclusion of longitudinal biomarker data (other than TS) has not been reported.…”

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

“…Although this can be tackled using more complex models (e.g., with second-order kinetics), 121 biphasic zero/first-order kinetics models have also been successfully adapted to the ICI context. 81,114,[122][123][124] TGI modeling for immunotherapy. To address the heterogeneity of response patterns to ICI, mixture models-whereby some parameters are considered to depend on a latent variable defining multiple classes-have been considered, for instance, for pembrolizumab 81 and ipilimumab, 114 in advanced melanoma.…”

“…This joint modeling approach has recently flourished in oncology to analyze tumor size (or biomarker) kinetics and overall or disease-free survival. 124,127,128 Tardivon et al developed the first model of this kind for cancer immunotherapy (atezolizumab in urothelial carcinoma). 124 Using a similar model as Eq.…”