Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer
Abstract With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune microenvironment is critical. Tertiary lymphoid structures (TLSs) are considered favorable prognostic factors for EC, but the significance of thei...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Cancer Immunology, Immunotherapy |
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| Online Access: | https://doi.org/10.1007/s00262-024-03929-6 |
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| author | Haruka Suzuki Kohei Hamada Junzo Hamanishi Akihiko Ueda Ryusuke Murakami Mana Taki Rin Mizuno Koichi Watanabe Hanako Sato Yuko Hosoe Hiroaki Ito Koji Yamanoi Hiroyuki Yoshitomi Nobuyuki Kakiuchi Ken Yamaguchi Noriomi Matsumura Seishi Ogawa Hideki Ueno Masaki Mandai |
| author_facet | Haruka Suzuki Kohei Hamada Junzo Hamanishi Akihiko Ueda Ryusuke Murakami Mana Taki Rin Mizuno Koichi Watanabe Hanako Sato Yuko Hosoe Hiroaki Ito Koji Yamanoi Hiroyuki Yoshitomi Nobuyuki Kakiuchi Ken Yamaguchi Noriomi Matsumura Seishi Ogawa Hideki Ueno Masaki Mandai |
| author_sort | Haruka Suzuki |
| collection | DOAJ |
| description | Abstract With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune microenvironment is critical. Tertiary lymphoid structures (TLSs) are considered favorable prognostic factors for EC, but the significance of their spatial distribution remains unclear. B cell receptor repertoire analysis performed using six TLS samples located at various distances from the tumor showed that TLSs in distal areas had more shared B cell clones with tumor-infiltrating lymphocytes. To comprehensively investigate the distribution of TLSs, we developed an artificial intelligence model to detect TLSs and determine their spatial locations in whole-slide images. Our model effectively quantified TLSs, and TLSs were detected in 69% of the patients with EC. We identified them as proximal or distal to the tumor margin and demonstrated that patients with distal TLSs (dTLSs) had significantly prolonged overall survival and progression-free survival (PFS) across multiple cohorts [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.36–0.88; p = 0.01 for overall survival; HR, 0.58; 95% CI, 0.40–0.84; p = 0.004 for PFS]. When analyzed by molecular subtype, patients with dTLSs in the copy-number-high EC subtype had significantly longer PFS (HR, 0.51; 95% CI, 0.29–0.91; p = 0.02). Moreover, patients with dTLSs had a higher response rate to immune checkpoint inhibitors (87.5 vs. 41.7%) and a trend toward improved PFS. Our findings indicate that the functions and prognostic implications of TLSs may vary with their locations, and dTLSs may serve as prognostic factors and predictors of treatment efficacy. This may facilitate personalized therapy for patients with EC. |
| format | Article |
| id | doaj-art-60cc06b30b6c456d882fcaf5374a337d |
| institution | Kabale University |
| issn | 1432-0851 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Cancer Immunology, Immunotherapy |
| spelling | doaj-art-60cc06b30b6c456d882fcaf5374a337d2025-02-02T12:26:22ZengSpringerCancer Immunology, Immunotherapy1432-08512025-02-0174311410.1007/s00262-024-03929-6Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancerHaruka Suzuki0Kohei Hamada1Junzo Hamanishi2Akihiko Ueda3Ryusuke Murakami4Mana Taki5Rin Mizuno6Koichi Watanabe7Hanako Sato8Yuko Hosoe9Hiroaki Ito10Koji Yamanoi11Hiroyuki Yoshitomi12Nobuyuki Kakiuchi13Ken Yamaguchi14Noriomi Matsumura15Seishi Ogawa16Hideki Ueno17Masaki Mandai18Department of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Obstetrics and Gynecology, Kindai UniversityDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Pathology, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Immunology, Kyoto University Graduate School of MedicineDepartment of Pathology and Tumor Biology, Kyoto UniversityDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineDepartment of Obstetrics and Gynecology, Kindai UniversityDepartment of Pathology and Tumor Biology, Kyoto UniversityDepartment of Immunology, Kyoto University Graduate School of MedicineDepartment of Gynecology and Obstetrics, Kyoto University Graduate School of MedicineAbstract With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune microenvironment is critical. Tertiary lymphoid structures (TLSs) are considered favorable prognostic factors for EC, but the significance of their spatial distribution remains unclear. B cell receptor repertoire analysis performed using six TLS samples located at various distances from the tumor showed that TLSs in distal areas had more shared B cell clones with tumor-infiltrating lymphocytes. To comprehensively investigate the distribution of TLSs, we developed an artificial intelligence model to detect TLSs and determine their spatial locations in whole-slide images. Our model effectively quantified TLSs, and TLSs were detected in 69% of the patients with EC. We identified them as proximal or distal to the tumor margin and demonstrated that patients with distal TLSs (dTLSs) had significantly prolonged overall survival and progression-free survival (PFS) across multiple cohorts [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.36–0.88; p = 0.01 for overall survival; HR, 0.58; 95% CI, 0.40–0.84; p = 0.004 for PFS]. When analyzed by molecular subtype, patients with dTLSs in the copy-number-high EC subtype had significantly longer PFS (HR, 0.51; 95% CI, 0.29–0.91; p = 0.02). Moreover, patients with dTLSs had a higher response rate to immune checkpoint inhibitors (87.5 vs. 41.7%) and a trend toward improved PFS. Our findings indicate that the functions and prognostic implications of TLSs may vary with their locations, and dTLSs may serve as prognostic factors and predictors of treatment efficacy. This may facilitate personalized therapy for patients with EC.https://doi.org/10.1007/s00262-024-03929-6Endometrial cancerTertiary lymphoid structureImmune checkpoint inhibitorsArtificial intelligenceB cell receptor repertoire |
| spellingShingle | Haruka Suzuki Kohei Hamada Junzo Hamanishi Akihiko Ueda Ryusuke Murakami Mana Taki Rin Mizuno Koichi Watanabe Hanako Sato Yuko Hosoe Hiroaki Ito Koji Yamanoi Hiroyuki Yoshitomi Nobuyuki Kakiuchi Ken Yamaguchi Noriomi Matsumura Seishi Ogawa Hideki Ueno Masaki Mandai Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer Cancer Immunology, Immunotherapy Endometrial cancer Tertiary lymphoid structure Immune checkpoint inhibitors Artificial intelligence B cell receptor repertoire |
| title | Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| title_full | Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| title_fullStr | Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| title_full_unstemmed | Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| title_short | Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| title_sort | artificial intelligence based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer |
| topic | Endometrial cancer Tertiary lymphoid structure Immune checkpoint inhibitors Artificial intelligence B cell receptor repertoire |
| url | https://doi.org/10.1007/s00262-024-03929-6 |
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