Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer
Abstract Background Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune...
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Nature Portfolio
2024-10-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00604-w |
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| author | Sumanth Reddy Nakkireddy Inyeop Jang Minji Kim Linda X. Yin Michael Rivera Joaquin J. Garcia Kathleen R. Bartemes David M. Routman Eric. J. Moore Chadi N. Abdel-Halim Daniel J. Ma Kathryn M. Van Abel Tae Hyun Hwang |
| author_facet | Sumanth Reddy Nakkireddy Inyeop Jang Minji Kim Linda X. Yin Michael Rivera Joaquin J. Garcia Kathleen R. Bartemes David M. Routman Eric. J. Moore Chadi N. Abdel-Halim Daniel J. Ma Kathryn M. Van Abel Tae Hyun Hwang |
| author_sort | Sumanth Reddy Nakkireddy |
| collection | DOAJ |
| description | Abstract Background Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. Methods A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Results Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Conclusions Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC. |
| format | Article |
| id | doaj-art-3f1e8d33b96a4fab90e7deab9a657335 |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-3f1e8d33b96a4fab90e7deab9a6573352025-08-20T03:09:19ZengNature PortfolioCommunications Medicine2730-664X2024-10-014111010.1038/s43856-024-00604-wIntegrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancerSumanth Reddy Nakkireddy0Inyeop Jang1Minji Kim2Linda X. Yin3Michael Rivera4Joaquin J. Garcia5Kathleen R. Bartemes6David M. Routman7Eric. J. Moore8Chadi N. Abdel-Halim9Daniel J. Ma10Kathryn M. Van Abel11Tae Hyun Hwang12Department of Artificial Intelligence and Informatics, Mayo ClinicDepartment of Artificial Intelligence and Informatics, Mayo ClinicDepartment of Artificial Intelligence and Informatics, Mayo ClinicDepartment of Otolaryngology-Head and Neck Surgery, Mayo ClinicDepartment of Laboratory Medicine and Pathology, Mayo ClinicDepartment of Laboratory Medicine and Pathology, Mayo ClinicDepartment of Otolaryngology-Head and Neck Surgery, Mayo ClinicDepartment of Radiation Oncology, Mayo ClinicDepartment of Artificial Intelligence and Informatics, Mayo ClinicDepartment of Otolaryngology-Head and Neck Surgery, Mayo ClinicDepartment of Radiation Oncology, Mayo ClinicDepartment of Laboratory Medicine and Pathology, Mayo ClinicDepartment of Artificial Intelligence and Informatics, Mayo ClinicAbstract Background Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. Methods A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Results Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Conclusions Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC.https://doi.org/10.1038/s43856-024-00604-w |
| spellingShingle | Sumanth Reddy Nakkireddy Inyeop Jang Minji Kim Linda X. Yin Michael Rivera Joaquin J. Garcia Kathleen R. Bartemes David M. Routman Eric. J. Moore Chadi N. Abdel-Halim Daniel J. Ma Kathryn M. Van Abel Tae Hyun Hwang Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer Communications Medicine |
| title | Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer |
| title_full | Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer |
| title_fullStr | Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer |
| title_full_unstemmed | Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer |
| title_short | Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer |
| title_sort | integrative analysis of h e and ihc identifies prognostic immune subtypes in hpv related oropharyngeal cancer |
| url | https://doi.org/10.1038/s43856-024-00604-w |
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