Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
The tumour microenvironment has been shown to be a valuable source of prognostic information for different cancer types. This holds in particular for triple negative breast cancer (TNBC), a breast cancer subtype for which currently no prognostic biomarkers are established. Although different methods...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | Breast |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0960977621000217 |
| _version_ | 1818427222780805120 |
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| author | Maschenka CA. Balkenhol Francesco Ciompi Żaneta Świderska-Chadaj Rob van de Loo Milad Intezar Irene Otte-Höller Daan Geijs Johannes Lotz Nick Weiss Thomas de Bel Geert Litjens Peter Bult Jeroen AWM. van der Laak |
| author_facet | Maschenka CA. Balkenhol Francesco Ciompi Żaneta Świderska-Chadaj Rob van de Loo Milad Intezar Irene Otte-Höller Daan Geijs Johannes Lotz Nick Weiss Thomas de Bel Geert Litjens Peter Bult Jeroen AWM. van der Laak |
| author_sort | Maschenka CA. Balkenhol |
| collection | DOAJ |
| description | The tumour microenvironment has been shown to be a valuable source of prognostic information for different cancer types. This holds in particular for triple negative breast cancer (TNBC), a breast cancer subtype for which currently no prognostic biomarkers are established. Although different methods to assess tumour infiltrating lymphocytes (TILs) have been published, it remains unclear which method (marker, region) yields the most optimal prognostic information. In addition, to date, no objective TILs assessment methods are available.For this proof of concept study, a subset of our previously described TNBC cohort (n = 94) was stained for CD3, CD8 and FOXP3 using multiplex immunohistochemistry and subsequently imaged by a multispectral imaging system. Advanced whole-slide image analysis algorithms, including convolutional neural networks (CNN) were used to register unmixed multispectral images and corresponding H&E sections, to segment the different tissue compartments (tumour, stroma) and to detect all individual positive lymphocytes. Densities of positive lymphocytes were analysed in different regions within the tumour and its neighbouring environment and correlated to relapse free survival (RFS) and overall survival (OS).We found that for all TILs markers the presence of a high density of positive cells correlated with an improved survival. None of the TILs markers was superior to the others. The results of TILs assessment in the various regions did not show marked differences between each other.The negative correlation between TILs and survival in our cohort are in line with previous studies. Our results provide directions for optimizing TILs assessment methodology. |
| first_indexed | 2024-12-14T14:42:18Z |
| format | Article |
| id | doaj.art-e80a20b3b17545aa82c38ce136bbd8b2 |
| institution | Directory Open Access Journal |
| issn | 1532-3080 |
| language | English |
| last_indexed | 2024-12-14T14:42:18Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Breast |
| spelling | doaj.art-e80a20b3b17545aa82c38ce136bbd8b22022-12-21T22:57:24ZengElsevierBreast1532-30802021-04-01567887Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognosticsMaschenka CA. Balkenhol0Francesco Ciompi1Żaneta Świderska-Chadaj2Rob van de Loo3Milad Intezar4Irene Otte-Höller5Daan Geijs6Johannes Lotz7Nick Weiss8Thomas de Bel9Geert Litjens10Peter Bult11Jeroen AWM. van der Laak12Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the Netherlands; Corresponding author. Radboud University Medical Center, Department of Pathology, PO Box 9100, 6500 HB, Nijmegen, the Netherlands.Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the Netherlands; Warsaw University of Technology, Faculty of Electrical Engineering, Warsaw, PolandRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsFraunhofer Institute for Image Computing MEVIS, Lübeck, GermanyFraunhofer Institute for Image Computing MEVIS, Lübeck, GermanyRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the NetherlandsRadboud University Medical Center, Radboud Institute for Health Sciences, Department of Pathology, Nijmegen, the Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, SwedenThe tumour microenvironment has been shown to be a valuable source of prognostic information for different cancer types. This holds in particular for triple negative breast cancer (TNBC), a breast cancer subtype for which currently no prognostic biomarkers are established. Although different methods to assess tumour infiltrating lymphocytes (TILs) have been published, it remains unclear which method (marker, region) yields the most optimal prognostic information. In addition, to date, no objective TILs assessment methods are available.For this proof of concept study, a subset of our previously described TNBC cohort (n = 94) was stained for CD3, CD8 and FOXP3 using multiplex immunohistochemistry and subsequently imaged by a multispectral imaging system. Advanced whole-slide image analysis algorithms, including convolutional neural networks (CNN) were used to register unmixed multispectral images and corresponding H&E sections, to segment the different tissue compartments (tumour, stroma) and to detect all individual positive lymphocytes. Densities of positive lymphocytes were analysed in different regions within the tumour and its neighbouring environment and correlated to relapse free survival (RFS) and overall survival (OS).We found that for all TILs markers the presence of a high density of positive cells correlated with an improved survival. None of the TILs markers was superior to the others. The results of TILs assessment in the various regions did not show marked differences between each other.The negative correlation between TILs and survival in our cohort are in line with previous studies. Our results provide directions for optimizing TILs assessment methodology.http://www.sciencedirect.com/science/article/pii/S0960977621000217Triple negative breast cancerTumour infiltrating lymphocytesArtificial intelligenceMultispectral imagingPrognosis |
| spellingShingle | Maschenka CA. Balkenhol Francesco Ciompi Żaneta Świderska-Chadaj Rob van de Loo Milad Intezar Irene Otte-Höller Daan Geijs Johannes Lotz Nick Weiss Thomas de Bel Geert Litjens Peter Bult Jeroen AWM. van der Laak Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics Breast Triple negative breast cancer Tumour infiltrating lymphocytes Artificial intelligence Multispectral imaging Prognosis |
| title | Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| title_full | Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| title_fullStr | Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| title_full_unstemmed | Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| title_short | Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| title_sort | optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics |
| topic | Triple negative breast cancer Tumour infiltrating lymphocytes Artificial intelligence Multispectral imaging Prognosis |
| url | http://www.sciencedirect.com/science/article/pii/S0960977621000217 |
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