From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 11, 2022
- Source ID
- 10.3389/fonc.2021.819565
Entities
People
- Antoine M Snijders
- Hang Chang
- Jade Moore
- Jesus Pérez-losada
- Jian-Hua Mao
- Kuang-yu Jen
- Lin Ma
- Marina Mendiburu-eliçabe
- Mary Helen Barcellos-hoff
- Natalia García-sancha
- Roberto Corchado-cobos
- William Chou
- Xiao-ping Liu
- Xu Yang
Organizations
- National Cancer Institute
- United States Department of Defense