Animal models in preclinical metastatic breast cancer immunotherapy research: A systematic review and meta-analysis of efficacy outcomes - Yalda Mirzaei; Martina Hüffel; Sarah McCann; Alexandra Bannach-Brown; René H. Tolba; Julia Steitz (PLoS ONE, 2025)
Breast cancer, particularly metastatic breast cancer (MBC), presents aggressive clinical challenges with limited treatment success. Immunotherapy has emerged as a promising approach, however, discrepancies between preclinical animal models and human cancers complicate translation to clinical outcomes. This systematic review and meta-analysis evaluated the effect of immunotherapy on primary and metastatic tumor regression in animal models of MBC and assessed the models’ appropriateness and reproducibility to improve future preclinical study design. Following a preregistered protocol in PROSPERO (CRD42021207033), we conducted searches in MEDLINE, Embase, and Web of Science databases, yielding 2255 studies for title/abstract screening and 108 studies included after full-text screening. All included studies used mouse models, assessing primary outcomes through tumor volume or weight and metastatic outcomes via nodule count or bioluminescence. Only 14% of studies fully reported experimental animal characteristics, and 43% provided detailed experimental procedures. Of 105 articles (293 comparisons) included in the meta-analysis, pooled effect sizes indicated significant reductions in both primary and metastatic tumors. However, high heterogeneity across studies and wide prediction intervals suggested substantial variability in model responses to immunotherapy. Univariable and multivariable meta-regressions failed to significantly explain this heterogeneity, suggesting additional factors may influence outcomes. Trim-and-fill and Egger’s regression tests indicated funnel plot asymmetry, implying potential publication bias and small study effects. While our analysis demonstrated positive effects of immunotherapy on MBC and highlighted variability in animal tumor models, addressing model-related heterogeneity and enhancing methodological transparency are essential to improve reproducibility and clinical translatability.