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Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. METHODS: We retrospectively extracted radiomics features from...

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Autores principales: Su, Guan-Hua, Xiao, Yi, Jiang, Lin, Zheng, Ren-Cheng, Wang, He, Chen, Yan, Gu, Ya-Jia, You, Chao, Shao, Zhi-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571493/
https://www.ncbi.nlm.nih.gov/pubmed/36243806
http://dx.doi.org/10.1186/s12967-022-03688-x
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author Su, Guan-Hua
Xiao, Yi
Jiang, Lin
Zheng, Ren-Cheng
Wang, He
Chen, Yan
Gu, Ya-Jia
You, Chao
Shao, Zhi-Ming
author_facet Su, Guan-Hua
Xiao, Yi
Jiang, Lin
Zheng, Ren-Cheng
Wang, He
Chen, Yan
Gu, Ya-Jia
You, Chao
Shao, Zhi-Ming
author_sort Su, Guan-Hua
collection PubMed
description BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. METHODS: We retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures. RESULTS: We selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638–0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8(+) T cells, follicular helper T cells and memory B cells, was found in high Rad-TILs tumors. CONCLUSIONS: Our study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03688-x.
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spelling pubmed-95714932022-10-17 Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer Su, Guan-Hua Xiao, Yi Jiang, Lin Zheng, Ren-Cheng Wang, He Chen, Yan Gu, Ya-Jia You, Chao Shao, Zhi-Ming J Transl Med Research BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. METHODS: We retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures. RESULTS: We selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638–0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8(+) T cells, follicular helper T cells and memory B cells, was found in high Rad-TILs tumors. CONCLUSIONS: Our study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03688-x. BioMed Central 2022-10-15 /pmc/articles/PMC9571493/ /pubmed/36243806 http://dx.doi.org/10.1186/s12967-022-03688-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Su, Guan-Hua
Xiao, Yi
Jiang, Lin
Zheng, Ren-Cheng
Wang, He
Chen, Yan
Gu, Ya-Jia
You, Chao
Shao, Zhi-Ming
Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title_full Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title_fullStr Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title_full_unstemmed Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title_short Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
title_sort radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571493/
https://www.ncbi.nlm.nih.gov/pubmed/36243806
http://dx.doi.org/10.1186/s12967-022-03688-x
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