From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer

Hang Chang, Xu Yang, Jade Moore, Xiao Ping Liu, Kuang Yu Jen, Antoine M. Snijders, Lin Ma, William Chou, Roberto Corchado-Cobos, Natalia García-Sancha, Marina Mendiburu-Eliçabe, Jesus Pérez-Losada, Mary Helen Barcellos-Hoff, Jian Hua Mao

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number819565
JournalFrontiers in Oncology
Volume11
DOIs
StatePublished - Feb 11 2022
Externally publishedYes

Keywords

  • cellular morphometric biomarkers
  • cellular morphometric subtypes
  • human breast cancers
  • metastasis
  • mouse mammary tumor
  • overall survival (OS)
  • transfer learning

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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