Tissue‐matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma
Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogenei...
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| Vydáno v: | International journal of cancer Ročník 156; číslo 8; s. 1634 - 1643 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
15.04.2025
Wiley Subscription Services, Inc |
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| ISSN: | 0020-7136, 1097-0215, 1097-0215 |
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| Abstract | Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi‐parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three‐dimensional (3D) printing was employed for tissue sampling, to match the multi‐parametric MRI data with tumor tissues, followed by flow cytometry analysis and next‐generation RNA‐sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL‐related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T‐cells, CD4+ T‐cells, CD8+ T‐cells, PD1 + CD8+ T‐cells, B‐cells, macrophages, and regulatory T‐cells (correlation coefficients ranged from −0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non‐inflamed subclasses, with the proportion of T‐cells, CD8+ T‐cells, and PD1 + CD8+ T‐cells significantly higher in the inflamed group compared to the non‐inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z‐score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721–0.910) and a cut‐off value of −0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z‐score was related to the gene expression of T‐cell activation, chemokine production, and cell adhesion. The tissue‐matched analysis of multi‐parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses.
What's new?
Increasing evidence suggests that the evaluation of tumor‐infiltrating lymphocytes both at baseline and throughout cancer therapy can be valuable for predicting and monitoring treatment responses. Tumor heterogeneity however complicates the assessment of tumor‐infiltrating lymphocytes. In this study, the authors used an animal model and three‐dimensional printing technology to achieve guided tumor spatial segmentation and regional evaluation of tumor‐infiltrating lymphocytes. They present a feasible, non‐invasive magnetic resonance imaging‐based model for evaluating tumor‐infiltrating lymphocytes in hepatocellular carcinoma that takes spatial heterogeneity into account and could potentially inform the immunotherapy response in patients. |
|---|---|
| AbstractList | Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three-dimensional (3D) printing was employed for tissue sampling, to match the multi-parametric MRI data with tumor tissues, followed by flow cytometry analysis and next-generation RNA-sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL-related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T-cells, CD4+ T-cells, CD8+ T-cells, PD1 + CD8+ T-cells, B-cells, macrophages, and regulatory T-cells (correlation coefficients ranged from -0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non-inflamed subclasses, with the proportion of T-cells, CD8+ T-cells, and PD1 + CD8+ T-cells significantly higher in the inflamed group compared to the non-inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z-score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721-0.910) and a cut-off value of -0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z-score was related to the gene expression of T-cell activation, chemokine production, and cell adhesion. The tissue-matched analysis of multi-parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses.Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three-dimensional (3D) printing was employed for tissue sampling, to match the multi-parametric MRI data with tumor tissues, followed by flow cytometry analysis and next-generation RNA-sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL-related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T-cells, CD4+ T-cells, CD8+ T-cells, PD1 + CD8+ T-cells, B-cells, macrophages, and regulatory T-cells (correlation coefficients ranged from -0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non-inflamed subclasses, with the proportion of T-cells, CD8+ T-cells, and PD1 + CD8+ T-cells significantly higher in the inflamed group compared to the non-inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z-score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721-0.910) and a cut-off value of -0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z-score was related to the gene expression of T-cell activation, chemokine production, and cell adhesion. The tissue-matched analysis of multi-parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses. Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi‐parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three‐dimensional (3D) printing was employed for tissue sampling, to match the multi‐parametric MRI data with tumor tissues, followed by flow cytometry analysis and next‐generation RNA‐sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL‐related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T‐cells, CD4+ T‐cells, CD8+ T‐cells, PD1 + CD8+ T‐cells, B‐cells, macrophages, and regulatory T‐cells (correlation coefficients ranged from −0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non‐inflamed subclasses, with the proportion of T‐cells, CD8+ T‐cells, and PD1 + CD8+ T‐cells significantly higher in the inflamed group compared to the non‐inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z‐score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721–0.910) and a cut‐off value of −0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z‐score was related to the gene expression of T‐cell activation, chemokine production, and cell adhesion. The tissue‐matched analysis of multi‐parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses. Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi‐parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three‐dimensional (3D) printing was employed for tissue sampling, to match the multi‐parametric MRI data with tumor tissues, followed by flow cytometry analysis and next‐generation RNA‐sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL‐related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T‐cells, CD4+ T‐cells, CD8+ T‐cells, PD1 + CD8+ T‐cells, B‐cells, macrophages, and regulatory T‐cells (correlation coefficients ranged from −0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non‐inflamed subclasses, with the proportion of T‐cells, CD8+ T‐cells, and PD1 + CD8+ T‐cells significantly higher in the inflamed group compared to the non‐inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z‐score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721–0.910) and a cut‐off value of −0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z‐score was related to the gene expression of T‐cell activation, chemokine production, and cell adhesion. The tissue‐matched analysis of multi‐parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses. What's new? Increasing evidence suggests that the evaluation of tumor‐infiltrating lymphocytes both at baseline and throughout cancer therapy can be valuable for predicting and monitoring treatment responses. Tumor heterogeneity however complicates the assessment of tumor‐infiltrating lymphocytes. In this study, the authors used an animal model and three‐dimensional printing technology to achieve guided tumor spatial segmentation and regional evaluation of tumor‐infiltrating lymphocytes. They present a feasible, non‐invasive magnetic resonance imaging‐based model for evaluating tumor‐infiltrating lymphocytes in hepatocellular carcinoma that takes spatial heterogeneity into account and could potentially inform the immunotherapy response in patients. Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three-dimensional (3D) printing was employed for tissue sampling, to match the multi-parametric MRI data with tumor tissues, followed by flow cytometry analysis and next-generation RNA-sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL-related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T-cells, CD4+ T-cells, CD8+ T-cells, PD1 + CD8+ T-cells, B-cells, macrophages, and regulatory T-cells (correlation coefficients ranged from -0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non-inflamed subclasses, with the proportion of T-cells, CD8+ T-cells, and PD1 + CD8+ T-cells significantly higher in the inflamed group compared to the non-inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z-score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721-0.910) and a cut-off value of -0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z-score was related to the gene expression of T-cell activation, chemokine production, and cell adhesion. The tissue-matched analysis of multi-parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses. Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi‐parametric MRI was performed on hepatocellular carcinoma (HCC) mice ( N = 28). Three‐dimensional (3D) printing was employed for tissue sampling, to match the multi‐parametric MRI data with tumor tissues, followed by flow cytometry analysis and next‐generation RNA‐sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL‐related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T‐cells, CD4+ T‐cells, CD8+ T‐cells, PD1 + CD8+ T‐cells, B‐cells, macrophages, and regulatory T‐cells (correlation coefficients ranged from −0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non‐inflamed subclasses, with the proportion of T‐cells, CD8+ T‐cells, and PD1 + CD8+ T‐cells significantly higher in the inflamed group compared to the non‐inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z‐score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721–0.910) and a cut‐off value of −0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z‐score was related to the gene expression of T‐cell activation, chemokine production, and cell adhesion. The tissue‐matched analysis of multi‐parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses. |
| Author | Song, Chenyu Wang, Huanjun Peng, Zhenpeng Huang, Mengqi Cai, Huasong Zhou, Xiaoqi Wang, Jifei Feng, Shi‐Ting Lin, Yingyu Wang, Meng Dong, Zhi |
| AuthorAffiliation | 1 Department of Radiology, The First Affiliated Hospital Sun Yat‐sen University Guangzhou China 2 Department of Radiology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China |
| AuthorAffiliation_xml | – name: 1 Department of Radiology, The First Affiliated Hospital Sun Yat‐sen University Guangzhou China – name: 2 Department of Radiology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China |
| Author_xml | – sequence: 1 givenname: Mengqi surname: Huang fullname: Huang, Mengqi organization: Huazhong University of Science and Technology – sequence: 2 givenname: Chenyu surname: Song fullname: Song, Chenyu organization: Sun Yat‐sen University – sequence: 3 givenname: Xiaoqi surname: Zhou fullname: Zhou, Xiaoqi organization: Sun Yat‐sen University – sequence: 4 givenname: Huanjun surname: Wang fullname: Wang, Huanjun organization: Sun Yat‐sen University – sequence: 5 givenname: Yingyu surname: Lin fullname: Lin, Yingyu organization: Sun Yat‐sen University – sequence: 6 givenname: Jifei surname: Wang fullname: Wang, Jifei organization: Sun Yat‐sen University – sequence: 7 givenname: Huasong surname: Cai fullname: Cai, Huasong organization: Sun Yat‐sen University – sequence: 8 givenname: Meng surname: Wang fullname: Wang, Meng organization: Sun Yat‐sen University – sequence: 9 givenname: Zhenpeng surname: Peng fullname: Peng, Zhenpeng organization: Sun Yat‐sen University – sequence: 10 givenname: Zhi surname: Dong fullname: Dong, Zhi email: dongzh7@mail.sysu.edu.cn organization: Sun Yat‐sen University – sequence: 11 givenname: Shi‐Ting orcidid: 0000-0002-0869-7290 surname: Feng fullname: Feng, Shi‐Ting email: fengsht@mail.sysu.edu.cn organization: Sun Yat‐sen University |
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| Keywords | magnetic resonance imaging tumor‐infiltrating lymphocytes hepatocellular carcinoma three‐dimensional printing |
| Language | English |
| License | Attribution-NonCommercial-NoDerivs 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
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| Notes | Mengqi Huang and Chenyu Song have contributed equally and considered as co‐first author. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| SubjectTerms | Animals Carcinoma, Hepatocellular - diagnostic imaging Carcinoma, Hepatocellular - immunology Carcinoma, Hepatocellular - pathology CD4 antigen CD8 antigen Cell activation Cell adhesion Chemokines Feasibility studies Flow cytometry Gene expression Hepatocellular carcinoma Humans Immunotherapy Inflammation Leukocytes Liver cancer Liver Neoplasms - diagnostic imaging Liver Neoplasms - immunology Liver Neoplasms - pathology Lymphocytes Lymphocytes, Tumor-Infiltrating - immunology Lymphocytes, Tumor-Infiltrating - pathology Macrophages Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Metastases Mice Multiparametric Magnetic Resonance Imaging - methods PD-1 protein RESEARCH ARTICLE Spatial heterogeneity three‐dimensional printing Tumor microenvironment Tumor Microenvironment - immunology Tumors tumor‐infiltrating lymphocytes |
| Title | Tissue‐matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma |
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