Choi, Yoon Ho

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, collagen-rich stroma that impedes drug delivery. We investigated how iRGD-mediated nanoparticle delivery modifies collagen architecture using humanized mouse models. Computational analysis of Picro-Sirius Red-stained whole-slide images (WSIs) using curvelet transform-based fiber extraction method (CT-FIRE) was performed to quantitatively examine changes in collagen features, thereby understanding treatment-induced alterations in the tumor microenvironment (TME). Methods: Humanized PDAC mice were prepared with either the human PANC-1 cell line or 0779 primary human PDAC cells and treated with or without iRGD monotherapy for two months (n=12). Primary tumors were sectioned and stained with Picro-Sirius Red to visualize collagen structures. WSIs of Picro-Sirius Red-stained tissues were analyzed with CT-FIRE to extract six morphological features of collagen, including average fiber length, waviness, and density to capture structural changes. WSIs were divided into 256×256 pixel tiles with consistent sampling through bootstrapping, followed by permutation testing for statistical validation (1,000 permutations, p<0.01). Results: Computational analysis revealed significant iRGD-induced collagen remodeling with quantifiable changes in key structural and morphological features. Permutation testing showed that, compared to controls, average fiber length decreased by 3.27% (p<0.01), waviness reduced by 3.25% (p<0.01), and density diminished by 36.3% (p<0.01), compared to controls. The structural changes were more notable in 0779-derived tumors compared to PANC-1, with reductions in average fiber length (1.03%, p<0.01), waviness (8.78%, p<0.01), and collagen density (24.27%, p<0.01), demonstrating cell-line dependent stromal remodeling. Conclusions: Our findings suggest that long-term iRGD therapy alters collagen morphology in PDAC. Further investigation is warranted to understand how these alterations contribute to enhanced penetration of drugs and immune cells observed in iRGD-treated PDAC tumors. The observed cell-line dependent variations in stromal remodeling suggest tumor heterogeneity influences treatment response, highlighting the potential for personalized therapeutic approaches in stromal-rich tumors like PDAC. Citation Format: Yoon Ho Choi, Minji Kim, Hotaka Kawai, Yukihito Kuroda, Norio Miyamura, Seock-Jin Chung, Kazuki N. Sugahara, Tae Hyun Hwang. Altered collagen morphology in pancreatic cancer treated with the iRGD tumor-penetrating peptide [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 2698.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) exhibits a complex, immunosuppressive tumor immune microenvironment (TIME), necessitating molecular-level understanding to identify therapeutic targets. We investigated how iRGD peptide therapy remodels the immunosuppressive TIME to an immune-favorable state using single-cell spatial molecular analysis. Methods: Single-cell level in situ molecular imaging was performed using Xenium analyzer with 5K gene panel on tumor samples from humanized PDAC mice (n=7) treated with iRGD monotherapy (n=3) or control (n=4), enabling comprehensive spatial molecular characterization of the TIME. Results: iRGD therapy significantly transformed the immunosuppressive TIME to an immune-favorable state through T-cell composition remodeling. We observed increased populations of CD4+ memory T cells, CD4+ helper T cells, and cytokine-secreting effector T cells, indicating enhanced immune memory and effector functions. Concurrent decreases in regulatory T cells and activated CD4+ T cells reflected reduced immune suppression. Additionally, decreased TGF-β signaling in the iRGD cohort suggested TGF-β antagonism as a mechanism for overcoming immunosuppression. Conclusions: iRGD therapy effectively converted the immunosuppressive PDAC microenvironment to an immune-favorable state by enhancing beneficial T cell populations and reducing immunosuppression through TGF-β pathway modulation. These findings support previous work by Kuroda et al. and provide mechanistic rationale for the ongoing iRGD-based immunotherapy clinical trial (iLSTA:ACTRN12623000223639).Acknowledgments: We thank DeepL for the English language editing. Citation Format: Minji Kim, Yoon Ho Choi, Hotaka Kawai, Yukihito Kuroda, Norio Miyamura, Kazuki N. Sugahara, Tae Hyun Hwang. iRGD peptide therapy transforms immunosuppressive microenvironment to immune-favorable state in pancreatic ductal adenocarcinoma [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 2701.

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.

Chronic kidney disease (CKD) progression involves morphological changes in the kidney, such as decreased length and thickness, with associated histopathological alterations. However, the relationship between morphological changes in the kidneys and glomerular filtration rate (GFR) has not been quantitatively and comprehensively evaluated. We evaluated the three-dimensional size and shape of the kidney using computed tomography (CT)-derived features in relation to kidney function. We included 257 patients aged ≥18 years who underwent non-contrast abdominal CT at the Inha University Hospital. The features were quantified using predefined algorithms in the pyRadiomics package after kidney segmentation. All features, except for flatness, significantly correlated with estimated GFR (eGFR). The surface-area-to-volume ratio (SVR) showed the strongest negative correlation (r = −0.75, p < 0.0001). Kidney size features, such as volume and diameter, showed moderate to high positive correlations; other morphological features showed low to moderate correlations. The calculated area under the receiver operating characteristic (ROC) curve (AUC) for different features ranged from 0.51 (for elongation) to 0.86 (for SVR) for different eGFR thresholds. Diabetes patients had weaker correlations between the studied features and eGFR and showed less bumpy surfaces in three-dimensional visualization. We identified alterations in the CKD kidney based on various three-dimensional shape and size features, with their potential diagnostic value.