A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non–small cell lung cancer based on early tumor kinetics

Laurent Claret, Jin Y. Jin, Charles Ferte, Helen Winter, Sandhya Girish, Mark Stroh, Pei He, Marcus Ballinger, Alan Sandler, Amita Joshi, Achim Rittmeyer, David R Gandara, Jean Charles Soria, Rene Bruno

Research output: Contribution to journalArticle

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Abstract

Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non–small cell lung cancer. Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63–0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.

Original languageEnglish (US)
Pages (from-to)3292-3298
Number of pages7
JournalClinical Cancer Research
Volume24
Issue number14
DOIs
StatePublished - Jul 15 2018

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Non-Small Cell Lung Carcinoma
Drug Therapy
Survival
docetaxel
Neoplasms
Growth
Immunotherapy
MPDL3280A
Disease-Free Survival
Albumins
Clinical Trials

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non–small cell lung cancer based on early tumor kinetics. / Claret, Laurent; Jin, Jin Y.; Ferte, Charles; Winter, Helen; Girish, Sandhya; Stroh, Mark; He, Pei; Ballinger, Marcus; Sandler, Alan; Joshi, Amita; Rittmeyer, Achim; Gandara, David R; Soria, Jean Charles; Bruno, Rene.

In: Clinical Cancer Research, Vol. 24, No. 14, 15.07.2018, p. 3292-3298.

Research output: Contribution to journalArticle

Claret, L, Jin, JY, Ferte, C, Winter, H, Girish, S, Stroh, M, He, P, Ballinger, M, Sandler, A, Joshi, A, Rittmeyer, A, Gandara, DR, Soria, JC & Bruno, R 2018, 'A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non–small cell lung cancer based on early tumor kinetics', Clinical Cancer Research, vol. 24, no. 14, pp. 3292-3298. https://doi.org/10.1158/1078-0432.CCR-17-3662
Claret, Laurent ; Jin, Jin Y. ; Ferte, Charles ; Winter, Helen ; Girish, Sandhya ; Stroh, Mark ; He, Pei ; Ballinger, Marcus ; Sandler, Alan ; Joshi, Amita ; Rittmeyer, Achim ; Gandara, David R ; Soria, Jean Charles ; Bruno, Rene. / A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non–small cell lung cancer based on early tumor kinetics. In: Clinical Cancer Research. 2018 ; Vol. 24, No. 14. pp. 3292-3298.
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abstract = "Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non–small cell lung cancer. Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95{\%} prediction interval), 0.73 (0.63–0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97{\%} chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50{\%} of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.",
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T1 - A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non–small cell lung cancer based on early tumor kinetics

AU - Claret, Laurent

AU - Jin, Jin Y.

AU - Ferte, Charles

AU - Winter, Helen

AU - Girish, Sandhya

AU - Stroh, Mark

AU - He, Pei

AU - Ballinger, Marcus

AU - Sandler, Alan

AU - Joshi, Amita

AU - Rittmeyer, Achim

AU - Gandara, David R

AU - Soria, Jean Charles

AU - Bruno, Rene

PY - 2018/7/15

Y1 - 2018/7/15

N2 - Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non–small cell lung cancer. Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63–0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.

AB - Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non–small cell lung cancer. Patients and Methods: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63–0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies.

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