Willy Ciecior1, Nadja Ebert2, Nathalie Borgeaud3, Howard D Thames4, Michael Baumann5, Mechthild Krause6, Steffen Löck7. 1. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany. 2. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany. 3. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany. 4. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, United States. 5. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Ruprecht-Karls-University, Heidelberg, Germany. 6. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, Germany. 7. OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany. Electronic address: steffen.loeck@oncoray.de.
Abstract
BACKGROUND: In preclinical radio-oncological research, local tumour control is considered the most relevant endpoint as it reflects the inactivation of cancer stem cells. Preclinical tumour-control assays may compare dose-response curves between different radiotherapy strategies, e.g., assessing additional targeted drugs and immunotherapeutic interventions, or between different radiation modalities. To mimic the biological heterogeneity of human tumour populations and to accommodate for approaches of personalized oncology, preclinical studies are increasingly performed combining larger panels of tumour models. For designing the study protocols and to obtain reliable results, prospective sample-size planning has to be developed that accounts for such heterogeneous cohorts. METHODS: A Monte-Carlo-based method was developed to estimate the sample size of a comparative 1:1 two-arm prospective tumour-control assay. Based on repeated logistic regression analysis, pre-defined dose levels, assumptions on the dose-response curves of the included tumour models and on the dose-modifying factors (DMF), the power is calculated for a given number of animals per dose group. RESULTS: Two applications are presented: (i) For a simple tumour-control assay with the head and neck squamous cell carcinoma (HNSCC) model FaDu, 10 animals would be required for each of 7 dose levels per arm to reveal a DMF of 1.25 with a power of 0.82 without drop out (total: 140 animals). (ii) In a more complex experiment combining six different lung tumour models to a heterogeneous population, 21 animals would be required for each of 11 dose levels per arm to reveal a DMF of 1.25 with a power of 0.81 without drop out (total: 462 animals). Analyzing the heterogeneous cohort reduces the required animal number by more than 50% compared to six individual tumour-control assays. CONCLUSION: An approach for estimating the required animal number for comparative tumour-control assays in a heterogeneous population is presented, allowing also the inclusion of different treatments as a personalized approach in the experimental arm. The software is publicly available and can be applied to plan comparisons of sigmoidal dose-response curves based on logistic regression.
BACKGROUND: In preclinical radio-oncological research, local tumour control is considered the most relevant endpoint as it reflects the inactivation of cancer stem cells. Preclinical tumour-control assays may compare dose-response curves between different radiotherapy strategies, e.g., assessing additional targeted drugs and immunotherapeutic interventions, or between different radiation modalities. To mimic the biological heterogeneity of human tumour populations and to accommodate for approaches of personalized oncology, preclinical studies are increasingly performed combining larger panels of tumour models. For designing the study protocols and to obtain reliable results, prospective sample-size planning has to be developed that accounts for such heterogeneous cohorts. METHODS: A Monte-Carlo-based method was developed to estimate the sample size of a comparative 1:1 two-arm prospective tumour-control assay. Based on repeated logistic regression analysis, pre-defined dose levels, assumptions on the dose-response curves of the included tumour models and on the dose-modifying factors (DMF), the power is calculated for a given number of animals per dose group. RESULTS: Two applications are presented: (i) For a simple tumour-control assay with the head and neck squamous cell carcinoma (HNSCC) model FaDu, 10 animals would be required for each of 7 dose levels per arm to reveal a DMF of 1.25 with a power of 0.82 without drop out (total: 140 animals). (ii) In a more complex experiment combining six different lung tumour models to a heterogeneous population, 21 animals would be required for each of 11 dose levels per arm to reveal a DMF of 1.25 with a power of 0.81 without drop out (total: 462 animals). Analyzing the heterogeneous cohort reduces the required animal number by more than 50% compared to six individual tumour-control assays. CONCLUSION: An approach for estimating the required animal number for comparative tumour-control assays in a heterogeneous population is presented, allowing also the inclusion of different treatments as a personalized approach in the experimental arm. The software is publicly available and can be applied to plan comparisons of sigmoidal dose-response curves based on logistic regression.
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