AUTHOR=Chang Hang , Yang Xu , Moore Jade , Liu Xiao-Ping , Jen Kuang-Yu , Snijders Antoine M. , Ma Lin , Chou William , Corchado-Cobos Roberto , García-Sancha Natalia , Mendiburu-Eliçabe Marina , Pérez-Losada Jesus , Barcellos-Hoff Mary Helen , Mao Jian-Hua TITLE=From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer JOURNAL=Frontiers in Oncology VOLUME=11 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.819565 DOI=10.3389/fonc.2021.819565 ISSN=2234-943X ABSTRACT=

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.