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.

Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value of immunotherapy

“…In addition, CMS extracted from whole slide images of tissue histology through advanced machine learning techniques (27) has emerged as a novel imaging biomarker in various tumor types, providing independent clinical values (15,16). In this study, we found that the multimodal integration of MNAI and CMS exceeds the individual biomarker in precision prognosis.…”

“…The patient Cellular Morphometric Subtype (CMS) was defined by AI-empowered technique based on whole slide images of tissue histolgogy, which has been detaily describled in (15). In this study, CMSs in TCGA-LGG and TCGA-BRCA cohorts were preestablished in our previous studies (15)(16)(17). And the multimodal integration and evaluation was based on multivariate CoxPH model (17).…”

“…CMS derived from whole slide images of tissue histology has demonstrated significant and independent prognosis value in various tumors (15,16). Therefore, we assessed whether the multimodal integration of MNAI and CMS further improves the predictive power of prognosis.…”

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

“…A complete and accurate pathological cancer classification is still crucial to deciding on the best treatment plan for patients. Recently, we developed a framework powered by artificial intelligence (AI) technique for identifying cellular morphometric biomarkers (CMBs) and cellular morphometric subtypes (CMSs) from the whole slide images (WSI) of Hematoxylin and Eosin (H&E)-stained tissue histology[ 10 , 11 ]. We demonstrated that CMSs were significantly associated with specific molecular alterations, immune microenvironment, and prognosis in lower-grade gliomas[ 10 ].…”

The Signals of Subtypes: How AI Creates Personalized Cancer Treatment (Dr. Hang Chang)

“…BSJ : To identify CMS' from mouse mammary tumors, you created an AI pipeline based on stacked predictive sparse decomposition (SPSD). 1 Could you brie y walk us through SPSD and why you chose it for your studies? HC : SPSD is an unsupervised machine learning pipeline that we created a long time ago for the mining of robust cellular morphometric biomarkers with speed and accuracy.…”