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

Journal: Frontiers in oncology
Published Date: Feb 11, 2022

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 -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 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.

Authors

  • Hang Chang
    Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
  • Xu Yang
    Department of Food Science and Technology, The Ohio State University, Columbus, OH, United States.
  • Jade Moore
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • Xiao-Ping Liu
    Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
  • Kuang-Yu Jen
    Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Davis, CA, United States.
  • Antoine M Snijders
    Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
  • Lin Ma
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • William Chou
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • Roberto Corchado-Cobos
    Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain.
  • Natalia García-Sancha
    Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain.
  • Marina Mendiburu-Eliçabe
    Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain.
  • Jesus Pérez-Losada
    Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/Consejo Superior de Investigaciones Científicas (CSIC), Salamanca, Spain.
  • Mary Helen Barcellos-Hoff
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • Jian-Hua Mao
    Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.

Keywords

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