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Chen C, Feng YS, Wang Z, Gupta M, Xu XS, Yan X. Organ-specific tumor dynamics predict survival of patients with metastatic colorectal cancer. Eur J Cancer 2024; 207:114147. [PMID: 38834016 DOI: 10.1016/j.ejca.2024.114147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND We aim to compare the prognostic value of organ-specific dynamics with the sum of the longest diameter (SLD) dynamics in patients with metastatic colorectal cancer (mCRC). METHODS All datasets are accessible in Project Data Sphere, an open-access platform. The tumor growth inhibition models developed based on organ-level SLD and SLD were used to estimate the organ-specific tumor growth rates (KGs) and SLD KG. The early tumor shrinkage (ETS) from baseline to the first measurement after treatment was also evaluated. The relationship between organ-specific dynamics, SLD dynamics, and survival outcomes (overall survival, OS; progression-free survival, PFS) was quantified using Kaplan-Meier analysis and Cox regression. RESULTS This study included 3687 patients from 6 phase III mCRC trials. The liver emerged as the most frequent metastatic site (2901, 78.7 %), with variable KGs across different organs in individual patients (liver 0.0243 > lung 0.0202 > lymph node 0.0127 > other 0.0118 [week-1]). Notably, the dynamics for different organs did not equally contribute to predicting survival outcomes. In liver metastasis cases, liver KG proved to be a superior prognostic indicator for OS and surpasses the predictive performance of SLD, (C-index, liver KG 0.610 vs SLD KG 0.606). A similar result can be found for PFS. Moreover, liver ETS also outperforms SLD ETS in predicting survival. Cox regression analysis confirmed liver KG is the most significant variable in survival prediction. CONCLUSIONS In mCRC patients with liver metastasis, liver dynamics is the primary prognostic indicator for both PFS and OS. In future drug development for mCRC, greater emphasis should be directed towards understanding the dynamics of liver metastasis development.
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Affiliation(s)
- Chengcong Chen
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Special Administrative Region of China
| | - Yan Summer Feng
- Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA
| | - Ziyi Wang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Special Administrative Region of China
| | - Manish Gupta
- Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA
| | - Xu Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA.
| | - Xiaoyu Yan
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Special Administrative Region of China.
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Marchand M, Gonçalves A, Mercier F, Chanu P, Jin JY, Guedj J, Bruno R. Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches. CPT Pharmacometrics Syst Pharmacol 2024; 13:1017-1028. [PMID: 38629452 PMCID: PMC11179698 DOI: 10.1002/psp4.13137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/05/2024] [Accepted: 03/15/2024] [Indexed: 06/17/2024] Open
Abstract
Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.
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Affiliation(s)
| | | | | | | | - Jin Y. Jin
- Clinical PharmacologyGenentechSouth San FranciscoCaliforniaUSA
| | | | - René Bruno
- Clinical PharmacologyGenentech‐RocheMarseilleFrance
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3
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Kerioui M, Beaulieu M, Desmée S, Bertrand J, Mercier F, Jin JY, Bruno R, Guedj J. Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer. Biometrics 2023; 79:3752-3763. [PMID: 37498050 DOI: 10.1111/biom.13912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 07/10/2023] [Indexed: 07/28/2023]
Abstract
In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.
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Affiliation(s)
- Marion Kerioui
- Université Paris Cité, INSERM, IAME F-75018, Paris, France
- Université de Tours, Université de Nantes, INSERM SPHERE, UMR 1246, Tours, France
- Institut Roche, Boulogne-Billancourt, France
- Clinical Pharmacology, Genentech/Roche, Paris, France
| | | | - Solène Desmée
- Université de Tours, Université de Nantes, INSERM SPHERE, UMR 1246, Tours, France
| | - Julie Bertrand
- Université Paris Cité, INSERM, IAME F-75018, Paris, France
| | | | - Jin Y Jin
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - René Bruno
- Clinical Pharmacology, Genentech/Roche, Marseille, France
| | - Jérémie Guedj
- Université Paris Cité, INSERM, IAME F-75018, Paris, France
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4
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Bruno R, Chanu P, Kågedal M, Mercier F, Yoshida K, Guedj J, Li C, Beyer U, Jin JY. Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models. Br J Cancer 2023; 129:1383-1388. [PMID: 36765177 PMCID: PMC10628227 DOI: 10.1038/s41416-023-02190-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/12/2023] Open
Abstract
Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.
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Affiliation(s)
- René Bruno
- Clinical Pharmacology, Genentech-Roche, Marseille, France.
| | - Pascal Chanu
- Clinical Pharmacology, Genentech-Roche, Lyon, France
| | - Matts Kågedal
- Clinical Pharmacology, Genentech-Roche, Solna, Sweden
| | | | - Kenta Yoshida
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
| | | | - Chunze Li
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
| | | | - Jin Y Jin
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
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Courlet P, Abler D, Guidi M, Girard P, Amato F, Vietti Violi N, Dietz M, Guignard N, Wicky A, Latifyan S, De Micheli R, Jreige M, Dromain C, Csajka C, Prior JO, Venkatakrishnan K, Michielin O, Cuendet MA, Terranova N. Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy. CPT Pharmacometrics Syst Pharmacol 2023; 12:1170-1181. [PMID: 37328961 PMCID: PMC10431051 DOI: 10.1002/psp4.12983] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 06/18/2023] Open
Abstract
The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.
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Affiliation(s)
- Perrine Courlet
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Daniel Abler
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Institute of Informatics, School of Management, University of Applied Sciences Western Switzerland (HES‐SO)SierreSwitzerland
| | - Monia Guidi
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- Service of Clinical PharmacologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Pascal Girard
- Merck Institute of Pharmacometrics, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany)LausanneSwitzerland
| | - Federico Amato
- Swiss Data Science Centre, École Polytechnique Fédérale de Lausanne (EPFL) and Eidgenössische Technische Hochschule Zurich (ETH)ZurichSwitzerland
| | - Naik Vietti Violi
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Matthieu Dietz
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Nicolas Guignard
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Alexandre Wicky
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Sofiya Latifyan
- Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Rita De Micheli
- Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Mario Jreige
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Clarisse Dromain
- Department of Radiology and Interventional RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Chantal Csajka
- Centre for Research and Innovation in Clinical Pharmaceutical SciencesLausanne University Hospital and University of LausanneLausanneSwitzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of GenevaUniversity of LausanneGenevaSwitzerland
- School of Pharmaceutical SciencesUniversity of GenevaGenevaSwitzerland
| | - John O. Prior
- Nuclear Medicine and Molecular Imaging DepartmentLausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Olivier Michielin
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Michel A. Cuendet
- Precision Oncology Center, Department of OncologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- Swiss Institute of Bioinformatics, University of LausanneLausanneSwitzerland
- Department of Physiology and Biophysics, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Nadia Terranova
- Merck Institute of Pharmacometrics, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany)LausanneSwitzerland
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Bruno R, Marchand M, Yoshida K, Chan P, Li H, Zou W, Mercier F, Chanu P, Wu B, Lee A, Li C, Jin JY, Maitland ML, Reck M, Socinski MA. Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150. Clin Cancer Res 2023; 29:1047-1055. [PMID: 36595566 PMCID: PMC10023325 DOI: 10.1158/1078-0432.ccr-22-2323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/17/2022] [Accepted: 12/21/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy. PATIENTS AND METHODS Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design. RESULTS For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated. CONCLUSIONS Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.
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Affiliation(s)
- René Bruno
- Clinical Pharmacology, Genentech-Roche, Marseille, France
| | | | - Kenta Yoshida
- Clinical Pharmacology, Genentech, South San Francisco, California
| | - Phyllis Chan
- Clinical Pharmacology, Genentech, South San Francisco, California
| | - Haocheng Li
- Product Development, Roche/Genentech, Mississauga, Ontario, Canada
| | - Wei Zou
- Product Development, Genentech, South San Francisco, California
| | | | - Pascal Chanu
- Clinical Pharmacology, Genentech-Roche, Lyon, France
| | - Benjamin Wu
- Clinical Pharmacology, Genentech, South San Francisco, California
| | - Anthony Lee
- Product Development, Genentech, South San Francisco, California
| | - Chunze Li
- Clinical Pharmacology, Genentech, South San Francisco, California
| | - Jin Y Jin
- Clinical Pharmacology, Genentech, South San Francisco, California
| | - Michael L Maitland
- Inova Schar Cancer Institute, Fairfax, Virginia
- University of Virginia Cancer Center, Charlottesville, Virginia
| | - Martin Reck
- Lung Clinic Grosshansdorf, Airway Research Center North, German Center of Lung Research, Grosshansdorf, Germany
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Pritchard JR, Lee MJ, Peyton SR. Materials-driven approaches to understand extrinsic drug resistance in cancer. SOFT MATTER 2022; 18:3465-3472. [PMID: 35445686 PMCID: PMC9380814 DOI: 10.1039/d2sm00071g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Metastatic cancer has a poor prognosis, because it is broadly disseminated and associated with both intrinsic and acquired drug resistance. Critical unmet needs in effectively killing drug resistant cancer cells include overcoming the drug desensitization characteristics of some metastatic cancers/lesions, and tailoring therapeutic regimens to both the tumor microenvironment and the genetic profiles of the resident cancer cells. Bioengineers and materials scientists are developing technologies to determine how metastatic sites exclude therapies, and how extracellular factors (including cells, proteins, metabolites, extracellular matrix, and abiotic factors) at metastatic sites significantly affect drug pharmacodynamics. Two looming challenges are determining which feature, or combination of features, from the tumor microenvironment drive drug resistance, and what the relative impact is of extracellular signals vs. intrinsic cell genetics in determining drug response. Sophisticated systems biology tools that can de-convolve a crowded network of signals and responses, as well as controllable microenvironments capable of providing discrete and tunable extracellular cues can help us begin to interrogate the high dimensional interactions governing drug resistance in patients.
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Affiliation(s)
- Justin R Pritchard
- Department of Biomedical Engineering, Pennsylvania State University, State College PA, USA
| | - Michael J Lee
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Shelly R Peyton
- Department of Chemical Engineering, University of Massachusetts Amherst, 240 Thatcher Way, Life Sciences Laboratory N531, Amherst, MA 01003, USA.
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