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.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nanjing Medical University (China)
- Univ. of California, San Francisco, CA (United States)
- Univ. of California, Davis, CA (United States)
- Universidad de Salamanca (Spain); Consejo Superior de Investigaciones Cientificas (CSIC), Salamanca (Spain)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- National Cancer Institute (NCI); USDOD; National Institutes of Health (NIH)
- OSTI Identifier:
- 1891477
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Oncology
- Additional Journal Information:
- Journal Volume: 11; Journal ID: ISSN 2234-943X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; mouse mammary tumor; metastasis; human breast cancers; transfer learning; cellular morphometric biomarkers; cellular morphometric subtypes; overall survival (OS)
Citation Formats
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, and Mao, Jian-Hua. From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer. United States: N. p., 2022.
Web. doi:10.3389/fonc.2021.819565.
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. From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer. United States. https://doi.org/10.3389/fonc.2021.819565
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, and Mao, Jian-Hua. Fri .
"From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer". United States. https://doi.org/10.3389/fonc.2021.819565. https://www.osti.gov/servlets/purl/1891477.
@article{osti_1891477,
title = {From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer},
author = {Chang, Hang and Yang, Xu and Moore, Jade and Liu, Xiao-Ping and Jen, Kuang-Yu and Snijders, Antoine M. and Ma, Lin and Chou, William and Corchado-Cobos, Roberto and García-Sancha, Natalia and Mendiburu-Eliçabe, Marina and Pérez-Losada, Jesus and Barcellos-Hoff, Mary Helen and Mao, Jian-Hua},
abstractNote = {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.},
doi = {10.3389/fonc.2021.819565},
journal = {Frontiers in Oncology},
number = ,
volume = 11,
place = {United States},
year = {Fri Feb 11 00:00:00 EST 2022},
month = {Fri Feb 11 00:00:00 EST 2022}
}
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