An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks
Abstract Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus molecular subtype (CMS) is a transcriptome...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-03618-z |
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| author | Tuomas Kaprio Jaana Hagström Jussi Kasurinen Ioannis Gkekas Sofia Edin Ines Beilmann-Lehtonen Karin Strigård Richard Palmqvist Ulf Gunnarson Camilla Böckelman Caj Haglund |
| author_facet | Tuomas Kaprio Jaana Hagström Jussi Kasurinen Ioannis Gkekas Sofia Edin Ines Beilmann-Lehtonen Karin Strigård Richard Palmqvist Ulf Gunnarson Camilla Böckelman Caj Haglund |
| author_sort | Tuomas Kaprio |
| collection | DOAJ |
| description | Abstract Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). Transcriptomics is too complex and expensive for clinical implementation; therefore, an immunohistochemical method is needed. The prognostic impact of the immunohistochemistry-based four CMS-like subtypes remains unclear. Due to the complexity and costs associated with transcriptomics, we developed an immunohistochemistry (IHC)-based method supported by convolutional neural networks (CNNs) to define subgroups that resemble CMS biological characteristics. Building on previous IHC-classifiers and incorporating β-catenin to refine differentiation between CMS2- and CMS3-like profiles, we categorized CRC tumors in a cohort of 538 patients. Classification was successful in 89.4% and 15.9% of tumors were classified as CMS1-like, 35.1% as CMS2-like, 38.7% as CMS3-like, and 11.7% as CMS4-like. CMS2-like patients exhibited the best overall survival (p = 0.018), including when local and metastasized disease were analyzed separately. Our method offers an accessible and clinically feasible CMS-inspired classification, although it does not serve as a replacement for transcriptomic CMS classification. |
| format | Article |
| id | doaj-art-2dfc9254345140dfa598ea67edddef26 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2dfc9254345140dfa598ea67edddef262025-08-20T03:22:07ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-03618-zAn immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networksTuomas Kaprio0Jaana Hagström1Jussi Kasurinen2Ioannis Gkekas3Sofia Edin4Ines Beilmann-Lehtonen5Karin Strigård6Richard Palmqvist7Ulf Gunnarson8Camilla Böckelman9Caj Haglund10Department of Surgery, University of Helsinki and Helsinki University HospitalDepartment of Pathology, University of Helsinki and Helsinki University HospitalDepartment of Pathology, University of Helsinki and Helsinki University HospitalDepartment of Surgical and Perioperative Sciences, Umeå UniversityDepartment of Medical Biosciences, Pathology, Umeå UniversityDepartment of Surgery, University of Helsinki and Helsinki University HospitalDepartment of Surgical and Perioperative Sciences, Umeå UniversityDepartment of Medical Biosciences, Pathology, Umeå UniversityDepartment of Surgical and Perioperative Sciences, Umeå UniversityDepartment of Surgery, University of Helsinki and Helsinki University HospitalDepartment of Surgery, University of Helsinki and Helsinki University HospitalAbstract Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). Transcriptomics is too complex and expensive for clinical implementation; therefore, an immunohistochemical method is needed. The prognostic impact of the immunohistochemistry-based four CMS-like subtypes remains unclear. Due to the complexity and costs associated with transcriptomics, we developed an immunohistochemistry (IHC)-based method supported by convolutional neural networks (CNNs) to define subgroups that resemble CMS biological characteristics. Building on previous IHC-classifiers and incorporating β-catenin to refine differentiation between CMS2- and CMS3-like profiles, we categorized CRC tumors in a cohort of 538 patients. Classification was successful in 89.4% and 15.9% of tumors were classified as CMS1-like, 35.1% as CMS2-like, 38.7% as CMS3-like, and 11.7% as CMS4-like. CMS2-like patients exhibited the best overall survival (p = 0.018), including when local and metastasized disease were analyzed separately. Our method offers an accessible and clinically feasible CMS-inspired classification, although it does not serve as a replacement for transcriptomic CMS classification.https://doi.org/10.1038/s41598-025-03618-zColorectal cancerImmunohistochemistryConsensus molecular subtypesPrognosisConvoluted neural network |
| spellingShingle | Tuomas Kaprio Jaana Hagström Jussi Kasurinen Ioannis Gkekas Sofia Edin Ines Beilmann-Lehtonen Karin Strigård Richard Palmqvist Ulf Gunnarson Camilla Böckelman Caj Haglund An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks Scientific Reports Colorectal cancer Immunohistochemistry Consensus molecular subtypes Prognosis Convoluted neural network |
| title | An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| title_full | An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| title_fullStr | An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| title_full_unstemmed | An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| title_short | An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| title_sort | immunohistochemistry based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks |
| topic | Colorectal cancer Immunohistochemistry Consensus molecular subtypes Prognosis Convoluted neural network |
| url | https://doi.org/10.1038/s41598-025-03618-z |
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