Open AccessQuantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach
Author(s) -
Yoichiro Yamamoto,
Akira Saito,
Ayako Tateishi,
Hisashi Shimojo,
Hiroyuki Kanno,
Satoshi Tsuchiya,
Ken Ito,
Eric Cosatto,
Hans Peter Graf,
Rodrigo Rojas Moraleda,
Roland Eils,
Niels Grabe
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep46732
Subject(s) - myoepithelial cell , ductal carcinoma , paracrine signalling , pathology , breast cancer , hyperplasia , cell type , in situ , cancer , cell , medicine , biology , immunohistochemistry , chemistry , genetics , receptor , organic chemistry
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.