Unmasking the immune microecology of ductal carcinoma in situ with deep learning

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 spat...

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Autores principales:	Priya Lakshmi Narayanan, Shan E. Ahmed Raza, Allison H. Hall, Jeffrey R. Marks, Lorraine King, Robert B. West, Lucia Hernandez, Naomi Guppy, Mitch Dowsett, Barry Gusterson, Carlo Maley, E. Shelley Hwang, Yinyin Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/933aafb5df734dabbcabf44349806765
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spelling	oai:doaj.org-article:933aafb5df734dabbcabf443498067652021-12-02T18:01:26ZUnmasking the immune microecology of ductal carcinoma in situ with deep learning10.1038/s41523-020-00205-52374-4677https://doaj.org/article/933aafb5df734dabbcabf443498067652021-03-01T00:00:00Zhttps://doi.org/10.1038/s41523-020-00205-5https://doaj.org/toc/2374-4677Abstract 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.Priya Lakshmi NarayananShan E. Ahmed RazaAllison H. HallJeffrey R. MarksLorraine KingRobert B. WestLucia HernandezNaomi GuppyMitch DowsettBarry GustersonCarlo MaleyE. Shelley HwangYinyin YuanNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Breast Cancer, Vol 7, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Priya Lakshmi Narayanan Shan E. Ahmed Raza Allison H. Hall Jeffrey R. Marks Lorraine King Robert B. West Lucia Hernandez Naomi Guppy Mitch Dowsett Barry Gusterson Carlo Maley E. Shelley Hwang Yinyin Yuan Unmasking the immune microecology of ductal carcinoma in situ with deep learning
description	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.
format	article
author	Priya Lakshmi Narayanan Shan E. Ahmed Raza Allison H. Hall Jeffrey R. Marks Lorraine King Robert B. West Lucia Hernandez Naomi Guppy Mitch Dowsett Barry Gusterson Carlo Maley E. Shelley Hwang Yinyin Yuan
author_facet	Priya Lakshmi Narayanan Shan E. Ahmed Raza Allison H. Hall Jeffrey R. Marks Lorraine King Robert B. West Lucia Hernandez Naomi Guppy Mitch Dowsett Barry Gusterson Carlo Maley E. Shelley Hwang Yinyin Yuan
author_sort	Priya Lakshmi Narayanan
title	Unmasking the immune microecology of ductal carcinoma in situ with deep learning
title_short	Unmasking the immune microecology of ductal carcinoma in situ with deep learning
title_full	Unmasking the immune microecology of ductal carcinoma in situ with deep learning
title_fullStr	Unmasking the immune microecology of ductal carcinoma in situ with deep learning
title_full_unstemmed	Unmasking the immune microecology of ductal carcinoma in situ with deep learning
title_sort	unmasking the immune microecology of ductal carcinoma in situ with deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/933aafb5df734dabbcabf44349806765
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Unmasking the immune microecology of ductal carcinoma in situ with deep learning

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