Annual Report 2024

Division of Pathology

Shumpei Ishikawa, Motohiro Kojima, Hiroto Katoh, Shingo Sakashita, Naoya Sakamoto, Kana Morikyu, Takahiro Iimura, Miki Kusumoto

Introduction

 The Division of Pathology is advancing research to uncover the molecular mechanisms of cancer and to develop innovative diagnostics and therapies, utilizing state-of-the-art pathology-based approaches.

The Team and What We Do

• Research Activities: With the goal of "Creating Next-Generation Pathology through the Integration of Innovation," we advanced research and development aimed at establishing standardized pathological diagnosis while fostering a new paradigm of pathology through the creative fusion of technological innovation and pathological approaches. The research framework consisted of five components: A. Establishing standardized diagnostic pathology through the integration of technological innovation and pathological creativity; B. Biophysical profiling and pathological analysis of tissues; C. Standardization of pathological imaging for AI; D. Development of novel experimental models such as organoids; and E. Cross-organ analysis of tumor microenvironments in sarcomatoid and related cancers. Each theme was developed individually while maintaining close collaboration across projects, collectively contributing to the realization of next-generation pathology.
• Clinical Practice: Each Division Head of Division of Pathology concurrently served in the Hospital, engaging in diagnostic pathology services (approximately 14,000 biopsy specimens, 3,500 surgical specimens, 5,000 cytology samples, and 5 autopsy cases annually). Beyond diagnostic duties, this division played an integral role in managing the biobank and maintaining ISO15189 and CAP accreditation. Clinical-pathological conferences (CPCs) were held regularly with each clinical department, along with resident conferences, to promote resident education and invigorate research activities. We also participated in the Expert Panel for the cancer gene panel testing, contributing to the advancement of precision cancer genomics.

Research Activities

A. Establishing Standardized Diagnostic Pathology through the Integration of Technological Innovation and Pathological Creativity: We contributed to the development of standardized pathological diagnostics by proposing objective, cross-organ classification systems and staging criteria. We examined serosal elastic lamina invasion as a staging parameter of cancers, aiming to objectively identify high-risk cases. We advanced quantitative histopathologic evaluations-such as the "area of residual tumor" index and immunohistochemical scoring-and published these findings.

B. Biophysical Profiling and Pathological Analysis of Tissues: We continued analyses linking tumor stiffness, histopathologic architecture, and immune profiles, focusing on how preoperative therapy alters these physical and immunological properties. In collaboration with the University of Tokyo and the University of Cyprus, we also investigated intra-tumoral pressure, revealing that it is influenced not only by cancer cell properties but also by stromal components.

C. Standardization of Pathological Imaging for AI: We published multiple AI-assisted models integrating machine learning with expert pathologist knowledge: A predictive model for lymph node recurrence in cT1-2N0 tongue cancer, a deep learning-based model identifying histologic differences between invasive and non-invasive regions in early esophageal cancer, and an algorithm quantifying intracellular mitochondria in FFPE specimens. We also collaborated with Sakura Finetek Japan to study the impact of slide thickness and staining variability on AI and to advance the automated slide preparation systems. In collaboration with Matsunami Glass, we developed standardized glass slides for reproducible H&E staining.

D.  Development of Novel Experimental Models such as Organoids: We established human gastric cancer organoids and investigated mechanisms of drug resistance. Transcriptomic analyses comparing gastric cancer and 5-FU/oxaliplatin-resistant organoids revealed upregulation of PI3 in resistant clones. Subsequent in vitro and clinical analyses confirmed the link between PI3 expression and chemoresistance, leading to a publication.

E. Cross-organ Analysis of Tumor Microenvironments in Sarcomatoid and Related Cancers: Using spatial transcriptomics, we obtained high-resolution gene expression maps from cancer specimens and integrated them with pathologists' morphological insights. This approach enabled the identification of novel molecular mechanisms underlying tumor progression and microenvironmental remodeling across organ systems.

Education

 Our group provided education to residents, senior residents, pathology trainees, etc., at the Hospital East and published four English-language papers, for which the Division of Pathology served as the corresponding authors. These papers included those which were used as doctoral dissertations. Division Head Katoh was newly appointed as a collaborating faculty member at the Graduate School of Frontier Sciences, The University of Tokyo, and began to accept master's and doctoral students.

Future Prospects

 In collaboration with the Department of Pathology and Clinical Laboratories at the Hospital East, we aim to extract characteristic pathological features of cancer using a wide range of advanced technologies. By quantitatively integrating biologically meaningful spatial morphological and genomic information from these analyses, we seek to establish a foundation for the development of new diagnostic and therapeutic strategies. We are also advancing research and development on standardized specimen processing, image processing, and AI technologies to enable feature extraction based on digital pathology. Furthermore, by leveraging artificial intelligence and spatial transcriptomics, we are constructing experimental models guided by distinctive histomorphological patterns to elucidate cancer pathogenesis and identify novel points for diagnostic and therapeutic intervention.

List of papers published in 2024

Journal

1. Oda S, Kuno H, Fujita T, Hiyama T, Kotani D, Kadota T, Sakashita S, Kobayashi T. Clinical usefulness of four-dimensional dynamic ventilation CT for borderline resectable locally advanced esophageal cancer. Japanese journal of radiology, 43:434-444, 2025

2. Jubashi A, Nakayama I, Koganemaru S, Sakamoto N, Oda S, Matsubara Y, Miyashita Y, Sato S, Ushiyama S, Kobayashi A, Okazaki U, Okemoto D, Yamamoto K, Mishima S, Kotani D, Kawazoe A, Hashimoto T, Nakamura Y, Kuboki Y, Bando H, Kojima T, Yoshino T, Miyaaki H, Nakao K, Shitara K. Prognostic and predictive factors for the efficacy and safety of trastuzumab deruxtecan in HER2-positive gastric or gastroesophageal junction cancer. Gastric cancer, 28:63-73, 2025

3. Tomi Y, Kinoshita T, Yura M, Sakamoto N, Fujita T, Tokunaga M, Kinugasa Y. Accuracy of the preoperative estimation of esophageal invasion length of adenocarcinoma of the esophagogastric junction and its discrepancy with the pathological measurement. Surgery today, 55:768-777, 2025

4. Oi H, Taki T, Kuroe T, Sakamoto N, Sakashita S, Kojima M, Sugiyama E, Umemura S, Sakai T, Izumi H, Zenke Y, Matsumoto S, Yoh K, Ishii M, Tsuboi M, Goto K, Ishii G. NETosis in pulmonary pleomorphic carcinoma. Cancer science, 116:524-532, 2025

5. Kajiyama D, Fujiwara N, Shigeno T, Sato K, Yamaguchi M, Sakashita S, Daiko H, Fujita T. Impact of Lymphatic and Venous Invasion Patterns on Postoperative Prognosis and Distant Metastasis in Esophageal Squamous Cell Carcinoma After Preoperative Chemotherapy. Annals of surgical oncology, 32:860-871, 2025

6. Sugawara K, Sakashita S, Motoi N. ASO Author Reflections: Clinical Relevance of Mitochondrial Status in Patients with Esophageal Squamous Cell Carcinoma. Annals of surgical oncology, 32:1987-1988, 2025

7. Sugawara K, Sakashita S, Fukuda T, Murakami C, Oka D, Amori G, Ishibashi K, Kobayashi Y, Kanda H, Motoi N. Survival Impacts of Mitochondrial Status in Esophageal Squamous Cell Carcinoma Patients. Annals of surgical oncology, 32:1963-1972, 2025

8. Miura R, Ono A, Nakahara H, Shirane Y, Yamaoka K, Fujii Y, Uchikawa S, Fujino H, Murakami E, Kawaoka T, Miki D, Tsuge M, Kishi T, Ohishi W, Sakamoto N, Arihiro K, Hayes CN, Oka S. Serum IL-6 concentration is a useful biomarker to predict the efficacy of atezolizumab plus bevacizumab in patients with hepatocellular carcinoma. Journal of gastroenterology, 60:328-339, 2025

9. Urabe A, Adachi M, Sakamoto N, Kojima M, Ishikawa S, Ishii G, Yano T, Sakashita S. Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer. Cancer science, 116:824-834, 2025

10. Nagata H, Kinoshita T, Sakashita S, Kojima M, Taki T, Kuwata T, Yura M, Shitara K, Ishii G, Sakamoto N. Area of Residual Tumor Measurement After Preoperative Chemotherapy as an Objective and Quantitative Method for Predicting the Prognosis of Gastric Cancer: A Single-Center Retrospective Study. World journal of surgery, 49:717-726, 2025

11. Sakai SA, Nomura R, Nagasawa S, Chi S, Suzuki A, Suzuki Y, Imai M, Nakamura Y, Yoshino T, Ishikawa S, Tsuchihara K, Kageyama SI, Yamashita R. SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with single-cell spatial omics data. PLoS computational biology, 21:e1012854, 2025

12. Harada K, Sakamoto N, Kitaoka T, Nakamura Y, Kondo R, Morisue R, Hashimoto H, Yamamoto Y, Ukai S, Maruyama R, Sakashita S, Kojima M, Tanabe K, Ohdan H, Shitara K, Kinoshita T, Ishii G, Yasui W, Ochiai A, Ishikawa S. PI3 expression predicts recurrence after chemotherapy with DNA-damaging drugs in gastric cancer. The Journal of pathology, 265:472-485, 2025

13. Asamori T, Katoh H, Takata M, Komura D, Kakiuchi M, Hashimoto I, Sakurai M, Yamamoto A, Tsutsumi T, Asakage T, Ota Y, Ishikawa S. Molecular mimicry-driven autoimmunity in chronic rhinosinusitis with nasal polyps. The Journal of allergy and clinical immunology, 155:1521-1535, 2025

14. Uehara Y, Izumi H, Taki T, Sakai T, Udagawa H, Sugiyama E, Umemura S, Zenke Y, Matsumoto S, Yoh K, Kubota S, Aokage K, Sakamoto N, Sakashita S, Kojima M, Nagamine M, Hosomi Y, Tsuboi M, Goto K, Ishii G. Solid Predominant Histology and High Podoplanin Expression in Cancer-Associated Fibroblast Predict Primary Resistance to Osimertinib in EGFR-Mutated Lung Adenocarcinoma. JTO clinical and research reports, 6:100779, 2025

15. Hashimoto T, Nakamura Y, Mishima S, Nakayama I, Kotani D, Kawazoe A, Kuboki Y, Bando H, Kojima T, Iida N, Shibuki T, Imai M, Fujisawa T, Nagamine M, Sakamoto N, Kuwata T, Yoshino T, Shitara K. Whole-transcriptome sequencing in advanced gastric or gastroesophageal cancer: A deep dive into its clinical potential. Cancer science, 115:1622-1633, 2024

16. Nagasaki Y, Taki T, Nomura K, Tane K, Miyoshi T, Samejima J, Aokage K, Ohtani-Kim SJ, Kojima M, Sakashita S, Sakamoto N, Ishikawa S, Suzuki K, Tsuboi M, Ishii G. Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer. Journal of the National Cancer Institute, 116:1158-1168, 2024

17. Sakashita M, Motoi N, Yamamoto G, Gambe E, Suzuki M, Yoshida Y, Watanabe SI, Takazawa Y, Aoki K, Ochiai A, Sakashita S. An algorithm-based technique for counting mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections. Journal of cancer research and clinical oncology, 150:172, 2024

18. Ochi M, Komura D, Onoyama T, Shinbo K, Endo H, Odaka H, Kakiuchi M, Katoh H, Ushiku T, Ishikawa S. Registered multi-device/staining histology image dataset for domain-agnostic machine learning models. Scientific data, 11:330, 2024

19. Miyasaka Y, Hiyama T, Kuno H, Shinozaki T, Tomioka T, Sakashita S, Kobayashi T. Imaging of salivary gland cancers derived from a sublingual gland herniated into the submandibular space: a report of three cases. Neuroradiology, 66:931-935, 2024

20. Ishikawa S. Artificial Intelligence Enhances NSCLC Care by Predicting Treatment Outcomes, Validating Neoadjuvant Therapies, and Improving Precision. Journal of thoracic oncology, 19:666-667, 2024

21. Goto E, Taki T, Nomura K, Miyakami Y, Miyoshi T, Tane K, Samejima J, Aokage K, Nagamine M, Sakashita S, Sakamoto N, Kojima M, Suzuki K, Tsuboi M, Ishii G. Clinicopathological differences between EGFR mutated and EGFR wild-type lung adenocarcinoma with papillary predominant pattern. Lung cancer (Amsterdam, Netherlands), 192:107830, 2024

22. Habu T, Kumagai S, Bando H, Fujisawa T, Mishima S, Kotani D, Nakamura M, Hojo H, Sakashita S, Kinoshita T, Yano T, Mitsunaga S, Nishikawa H, Koyama S, Kojima T. Definitive chemoradiotherapy induces T-cell-inflamed tumor microenvironment in unresectable locally advanced esophageal squamous cell carcinoma. Journal of gastroenterology, 59:798-811, 2024

23. Hoshi Y, Enokida T, Tamura S, Nakashima T, Okano S, Fujisawa T, Sato M, Wada A, Tanaka H, Takeshita N, Tanaka N, Onaga R, Kishida T, Uryu H, Sakashita S, Asakage T, Tahara M. Efficacy of anti-PD-1 monotherapy for recurrent or metastatic olfactory neuroblastoma. Frontiers in oncology, 14:1379013, 2024

24. Kaminuma Y, Nakai T, Aokage K, Taki T, Miyoshi T, Tane K, Samejima J, Miyazaki S, Sakamoto N, Sakashita S, Kojima M, Watanabe R, Tsuboi M, Ishii G. Prognostic significance of micronest in cancer stroma in resected lung squamous cell carcinoma. Human pathology, 150:20-28, 2024

25. Kubota S, Taki T, Miyoshi T, Tane K, Samejima J, Aokage K, Wakabayashi M, Nomura K, Nagamine M, Kojima M, Sakashita S, Sakamoto N, Tsuboi M, Ishii G. Prognostic value of the international association for the study of lung cancer grading system and its association with the tumor microenvironment in stage I EGFR-muted lung adenocarcinoma. European journal of cancer (Oxford, England : 1990), 207:114184, 2024

26. Hashimoto T, Nakamura Y, Fujisawa T, Imai M, Shibuki T, Iida N, Ozaki H, Nonomura N, Morizane C, Iwata H, Okano S, Yamagami W, Yamazaki N, Kadowaki S, Taniguchi H, Ueno M, Boku S, Oki E, Komatsu Y, Yuki S, Makiyama A, Otsuka T, Hara H, Okano N, Nishina T, Sakamoto Y, Miki I, Kobayashi S, Yuda J, Kageyama SI, Nagamine M, Sakashita S, Sakamoto N, Yamashita R, Koga Y, Bando H, Ishii G, Kuwata T, Park WY, Ohtsu A, Yoshino T. The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine. Cancer discovery, 14:2243-2261, 2024

27. Adachi M, Taki T, Kojima M, Sakamoto N, Matsuura K, Hayashi R, Tabuchi K, Ishikawa S, Ishii G, Sakashita S. Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists. The journal of pathology. Clinical research, 10:e12392, 2024

28. Kitaoka T, Harada K, Sakashita S, Kojima M, Taki T, Kuwata T, Kinoshita T, Futakuchi M, Ishii G, Sakamoto N. Quantification of Gremlin 1 throughout the tumor stroma using whole slide imaging and its clinicopathological significance in gastric cancer. Virchows Archiv, 485:1107-1116, 2024

29. Taki T, Koike Y, Adachi M, Sakashita S, Sakamoto N, Kojima M, Aokage K, Ishikawa S, Tsuboi M, Ishii G. A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis. Cancer science, 115:3804-3816, 2024

30. Inaba A, Ikematsu H, Kojima M, Sakamoto N, Wakabayashi M, Sunakawa H, Nakajo K, Murano T, Kadota T, Shinmura K, Yano T. Association between pathological T1 colorectal cancer with lymphoid follicular replacement and risk of lymph node metastasis. Journal of gastroenterology and hepatology, 39:2631-2638, 2024

31. Tanaka H, Koga Y, Sugahara M, Fuchigami H, Ishikawa A, Yamaguchi T, Banba A, Shinozaki T, Matsuura K, Hayashi R, Sakashita S, Yasunaga M, Yano T. Real-Time Fluorescence Monitoring System for Optimal Light Dosage in Cancer Photoimmunotherapy. Pharmaceuticals (Basel, Switzerland), 17:1246, 2024

32. Minakata N, Kadota T, Sakashita S, Inaba A, Sunakawa H, Takashima K, Nakajo K, Murano T, Shinmura K, Yoda Y, Ikematsu H, Fujita T, Kinoshita T, Yano T. Tumor thickness is associated with metastasis in patients with submucosal invasive adenocarcinoma of the esophagogastric junction. Diseases of the esophagus, 37:doae083, 2024

33. Yang C, Komura D, Ochi M, Kakiuchi M, Katoh H, Ushiku T, Ishikawa S. SegRep: Mask-Supervised Learning for Segment Representation in Pathology Images. IEEE Access, 12:141729-141740, 2024