Cargando…

Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients

Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be p...

Descripción completa

Detalles Bibliográficos
Autores principales: Suwalska, Aleksandra, Zientek, Lukasz, Polanska, Joanna, Marczyk, Michal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317291/
https://www.ncbi.nlm.nih.gov/pubmed/35887610
http://dx.doi.org/10.3390/jpm12071113
_version_ 1784755019439931392
author Suwalska, Aleksandra
Zientek, Lukasz
Polanska, Joanna
Marczyk, Michal
author_facet Suwalska, Aleksandra
Zientek, Lukasz
Polanska, Joanna
Marczyk, Michal
author_sort Suwalska, Aleksandra
collection PubMed
description Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.
format Online
Article
Text
id pubmed-9317291
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93172912022-07-27 Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients Suwalska, Aleksandra Zientek, Lukasz Polanska, Joanna Marczyk, Michal J Pers Med Article Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance. MDPI 2022-07-07 /pmc/articles/PMC9317291/ /pubmed/35887610 http://dx.doi.org/10.3390/jpm12071113 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Suwalska, Aleksandra
Zientek, Lukasz
Polanska, Joanna
Marczyk, Michal
Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title_full Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title_fullStr Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title_full_unstemmed Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title_short Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients
title_sort quantifying spatial heterogeneity of tumor-infiltrating lymphocytes to predict survival of individual cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317291/
https://www.ncbi.nlm.nih.gov/pubmed/35887610
http://dx.doi.org/10.3390/jpm12071113
work_keys_str_mv AT suwalskaaleksandra quantifyingspatialheterogeneityoftumorinfiltratinglymphocytestopredictsurvivalofindividualcancerpatients
AT zienteklukasz quantifyingspatialheterogeneityoftumorinfiltratinglymphocytestopredictsurvivalofindividualcancerpatients
AT polanskajoanna quantifyingspatialheterogeneityoftumorinfiltratinglymphocytestopredictsurvivalofindividualcancerpatients
AT marczykmichal quantifyingspatialheterogeneityoftumorinfiltratinglymphocytestopredictsurvivalofindividualcancerpatients