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Unmasking the immune microecology of ductal carcinoma in situ with deep learning
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Narayanan, Priya Lakshami, Raza, Shan-e-Ahmed, Hall, Allison H., Marks, Jeffrey R. , King, Lorraine , West, Robert B. , Hernandez, Lucia , Guppy, Naomi , Dowsett, Mitch , Gusterson, Barry , Maley, Carlo , Hwang, E. Shelley and Yuan, Yinyin (2021) Unmasking the immune microecology of ductal carcinoma in situ with deep learning. npj Breast Cancer , 7 . 19. doi:10.1038/s41523-020-00205-5
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Official URL: https://doi.org/10.1038/s41523-020-00205-5
Abstract
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood.
To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of single DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared with adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts present in adjacent DCIS cases.
Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable automated morphological and microenvironmental analysis of
DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
| Item Type: | Journal Article | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QR Microbiology R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Computer Science | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Breast -- Cancer, Microbial ecology, Tumors, Lymphocytes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Journal or Publication Title: | npj Breast Cancer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Publisher: | Springer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ISSN: | 2374-4677 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Official Date: | 2021 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Volume: | 7 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Article Number: | 19 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.1038/s41523-020-00205-5 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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