Sample-size calculation for preclinical dose–response experiments using heterogeneous tumour models. (May 2021)

• Record Type: Journal Article
• Authors: Ciecior, Willy; Ebert, Nadja; Borgeaud, Nathalie; Thames, Howard D.; Baumann, Michael; Krause, Mechthild; Löck, Steffen
• Is Part Of: Radiotherapy and oncology. Volume 158(2021)
• Page Start: 262
• Page End: 267
• Publication Date: 2021-05
• Subjects: Sample size -- Tumour models -- Tumour-control assay -- Pre-clinical research -- Dose-response curves -- Dose-modifying factor; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.02.032
• Languages: English
• ISSNs: 0167-8140

Abstract

Graphical abstract: Highlights: Presentation of sample-size calculation for comparing dose–response curves. Application to simple experiment with one tumour model. Application to complex experiment with heterogeneous tumour models. 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.