Unmasking the immune microecology of ductal carcinoma in situ with deep learning.
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Embargo End Date
ICR Authors
Authors
Narayanan, PL
Raza, SEA
Hall, AH
Marks, JR
King, L
West, RB
Hernandez, L
Guppy, N
Dowsett, M
Gusterson, B
Maley, C
Hwang, ES
Yuan, Y
Raza, SEA
Hall, AH
Marks, JR
King, L
West, RB
Hernandez, L
Guppy, N
Dowsett, M
Gusterson, B
Maley, C
Hwang, ES
Yuan, Y
Document Type
Journal Article
Date
2021-03-01
Date Accepted
2020-10-21
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 individual 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 to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an 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.
Citation
NPJ breast cancer, 2021, 7 (1), pp. 19 - ?
Source Title
Publisher
NATURE PORTFOLIO
ISSN
2374-4677
eISSN
2374-4677
Collections
Research Team
Computational Pathology & Integrated Genomics
Computational Pathology & Integrated Genomics
Computational Pathology & Integrated Genomics