ArticleCPT: pharmacometrics & systems pharmacology2020
Tumor Time-Course Predicts Overall Survival in Non-Small Cell Lung Cancer Patients Treated with Atezolizumab: Dependency on Follow-Up Time.
Article in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influence ofJCO precision oncology · 2024Pooled it
- Review of Existing Approaches and Potential Role of Pharmacometrics in the Interpretation of Prognostic Circulating Serum Biomarkers in Advanced NSCLC.Clinical and translational science · 2026Review
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.NPJ systems biology and applications · 2023Article
- Multistate model for pharmacometric analyses of overall survival in HER2-negative breast cancer patients treated with docetaxel.CPT: pharmacometrics & systems pharmacology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model-derived variables were explored as predictors of OS in 88 patients with non-small cell lung cancer treated with atezolizumab. To investigate the impact of follow-up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time-course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor-time course-OS relationship could be identified based on early phase I data.
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