Abstract 6250: Clinical significance of co localization index in rectal cancer: a deep learning approach to cell interaction analysis
Authors: Kimihiro Yamashita, Tomoki Abe, Toru Nagasaka, Masayuki Ando, Takao Tsuneki, Yukari Adachi, Takaaki Tachibana, Hiroki Kagiyama, Tomoaki Aoki, Yasufumi Koterazawa, Ryuichiro Sawada, Hitoshi Harada, Yasunori Otowa, Naoki Urakawa, Hironobu Goto, Hiroshi Hasegawa, Shingo Kanaji, Takeru Matsuda, Ryohei Sasaki, Yoshihiro Kakeji
Journal: Cancer Research (2025)

Abstract Background: Tumor tissue is not just a cluster of cancer cells, but an organized ecosystem in which various cells, extracellular matrices, and various types of liquid factors interact with each other. There are high technical barriers to processing this complex ecosystem in large quantities and quantitatively. Therefore, we have established a histological analysis method using deep-learning-based imaging cytometry (DL-IC) (Abe T, Yamashita K, et al. Anticancer Res. 43:3755 2023). Identification of the constituent cells of tumor tissue and spatial understanding of the tumor immune microenvironment using DL-IC are important for predicting cancer prognosis and treatment efficacy. In this study, we propose a co-localization index (CLI) as a new indicator for analyzing the tumor immune microenvironment using AI. Purpose: We investigated the impact of the co-localization of cytotoxic lymphocytes and cancer cells on the prognosis of rectal cancer after surgery. Subjectsand Methods: Forty rectal cancer surgical specimens were analyzed using the DL-IC system Cu-Cyto. A bit pattern kernel filtering algorithm was implemented to prevent duplicate cell counting, and its performance on immunohistochemistry (IHC) specimens was evaluated. The accuracy was compared between analyses with and without the algorithm. Cell-cell interactions were quantified using CLI, and the usefulness of CLI as a prognostic indicator was evaluated, particularly focusing on interactions between cancer cells and CD8+ T cells. Additionally, a comprehensive analysis of CLI was conducted using combinations of multiple cell types. Results: In the training process, where the data size of the training data was scaled, the F1 scores for adenocarcinoma cells, lymphocytes, and CD8+ T cells were 0.589, 0.889, and 0.911, respectively, at a cell number of 1013. The introduction of a bit pattern kernel filtering algorithm improved the accuracy of the determination of each cell type. The 5-year disease-free survival rate was significantly prolonged in the high CLI group between cancer cells and CD8+ T cells (P=0.041), and multivariate analysis also showed that it was an independent prognostic factor. On the other hand, there was no significant difference in the 5-year overall survival rate (P=0.41). Furthermore, it was found that the CLI of the three-way interaction between cancer cells, macrophages, and CD8+ T cells was also an independent prognostic factor. Conclusion: Cu-Cyto has achieved highly accurate cell determination by introducing a bit pattern kernel filtering algorithm. CLI has shown correlation with patient prognosis as an objective and reproducible quantitative evaluation method for cell-cell interactions. In the future, it will be necessary to elucidate the oncological and biological significance of the combinations of each cell type. Citation Format: Kimihiro Yamashita, Tomoki Abe, Toru Nagasaka, Masayuki Ando, Takao Tsuneki, Yukari Adachi, Takaaki Tachibana, Hiroki Kagiyama, Tomoaki Aoki, Yasufumi Koterazawa, Ryuichiro Sawada, Hitoshi Harada, Yasunori Otowa, Naoki Urakawa, Hironobu Goto, Hiroshi Hasegawa, Shingo Kanaji, Takeru Matsuda, Ryohei Sasaki, Yoshihiro Kakeji. Clinical significance of co localization index in rectal cancer: a deep learning approach to cell interaction analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6250.