Open AccessComputational analysis of morphological and molecular features in gastric cancer tissues
Author(s) -
Yasuda Yoko,
Tokunaga Kazuaki,
Koga Tomoaki,
Sakamoto Chiyomi,
Goldberg Ilya G.,
Saitoh Noriko,
Nakao Mitsuyoshi
Publication year - 2020
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.2885
Subject(s) - immunohistochemistry , h&e stain , cancer , pathological , pathology , morphological analysis , biology , staining , computational biology , cancer cell , medicine , artificial intelligence , computer science , genetics
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)‐stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E‐stained images by expression levels of cancer‐associated nuclear ATF7IP/MCAF1 and membranous PD‐L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer‐associated proteins, suggesting the usefulness of biomolecular‐based morphological classification.