Citations

Abstract BACKGROUND Craniopharyngiomas are a rare tumor along the pituitary-hypothalamic axis. Genetic analysis revealed that 95% of papillary craniopharyngiomas (PCP) have BRAF V600E mutations (Brastianos et al. Nature Genetics 2014). We previously reported that BRAF/MEK inhibition was associated with significant anti-tumor activity in newly-diagnosed PCP patients (Brastianos et al. NEJM 2023). We have now evaluated the efficacy of vemurafenib/cobimetinib in a separate cohort of patients with recurrent PCP whose tumors progressed after prior radiation. METHODS In this multicenter, NCI-sponsored cooperative group trial, patients with histologically confirmed BRAF-positive PCP who progressed after radiation were treated with vemurafenib/cobimetinib. The primary endpoint of response rate (RR) based on centrally determined imaging data was to be evaluated in 16 patients, where a partial response was defined as ≥20% decrease in volume. This single arm, Simon two-stage phase 2 trial had 89% power to detect a true RR ≥ 30% (vs. null 5%; α=0.04). In this design, 3 or more observed responses in 16 evaluable patients would be considered evidence of promising activity. RESULTS Of the planned 16 patients, a total of 5 patients enrolled before the study was closed due to slow accrual. Median follow-up was 50 months (95% CI: 32 to not yet reached). Based on volumetric response criteria, all 5 patients had a response to treatment (100%; 95% CI: 47.8% to 100%). To date, 2 patients progressed, one of whom died 22 months after study entry. This cohort met the criteria for efficacy under the original design with >3 responders. No grade 4 or 5 toxicities were observed. CONCLUSIONS Vemurafenib/cobimetinib resulted in an objective response in all patients with previously treated PCP. The study met primary endpoint, similar to that reported for previously untreated PCP. Thus, BRAF/MEK inhibitors could be an effective tool for previously treated PCP and warrants further evaluation. SUPPORT U10CA180821; U10CA180882; Genentech, https://acknowledgments.alliancefound.org;ClinicalTrials.gov: NCT03224767

Background: Tumor immune microenvironment (TIME) organization fundamentally shapes cancer progression and immunotherapy outcomes. Traditional 2D spatial molecular approaches inadequately capture the complex three-dimensional architecture and cellular interactions within TIME. We developed an AI-driven platform integrating multiple spatial molecular datasets, enabling comprehensive 3D visualization for therapeutic discovery and patient selection. Methods: Our platform combines holotomography imaging, providing 3D refractive index maps, with diverse spatial molecular data: in situ molecular imaging, spatial proteomics, multiplex immunohistochemistry, and spatial transcriptomics. AI algorithms align datasets within a unified 3D coordinate system, while Poisson implicit surface reconstruction generates subcellular-resolution models. Platform validation used clinical samples from immunotherapy-treated patients. Results: The platform integrated multimodal spatial data into high-resolution 3D TIME representations, revealing novel relationships between protein expression, transcriptional patterns, and cellular architecture. 3D surface protein analysis with subcellular morphology identified patients with target protein expression outside tight junctions, indicating higher risk for adverse effects from antibody-drug conjugates and cellular therapies. This insight, unavailable through 2D analysis, enabled precise patient selection. Conclusions: This platform provides detailed subcellular-resolution TIME architecture analysis, offering actionable insights for therapeutic targeting, biomarker discovery, and patient selection. AI-driven 3D spatial multimodal TIME analysis advances precision medicine across multiple therapeutic approaches, including immunotherapy, targeted therapy, and drug delivery optimization. AI Disclosure: Original abstract content was written by the authors. Claude was used for editorial assistance and formatting without altering the scientific content or context of the work. Citation Format: Inyeop Jang, Yoonho Choi, Minji Kim, Jeonghwan Park, Jiyeon Sung, Byungsoo Ann, Clemenceau R. Jean, TaeHyun Hwang. AI-enabled 3D multimodal integration platform reveals subcellular architecture of tumor immune microenvironment for therapeutic target and biomarker discovery and patient selection [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 3648.

Abstract Background: Understanding the spatiotemporal dynamics of the tumor microenvironment (TME) during treatment is crucial for improving outcomes in pancreatic ductal adenocarcinoma (PDAC). This study presents a pre-planned spatiotemporal analysis from a phase II trial evaluating neoadjuvant mFOLFIRINOX in resectable PDAC, aimed at elucidating treatment resistance mechanisms and identifying predictive biomarkers. Methods: This multi-institutional, single-arm phase II trial enrolled treatment-naïve patients with histologically confirmed resectable PDAC (NCT05066802) who underwent six cycles of neoadjuvant mFOLFIRINOX followed by surgery. The primary endpoint was the R0 resection rate. Matched pre-treatment tumor biopsies and post-treatment surgical specimens were analyzed from selected responders (R) and non-responders (NR) using high-resolution spatial transcriptomics (10X Genomics Visium). This approach enabled comprehensive mapping of TME evolution and treatment-induced changes. Results: Between May 2020 and February 2022, 27 patients were enrolled. Of these, 24 patients underwent surgery after neoadjuvant treatment (resection rate: 88.9%), and 23 achieved R0 resection (85.2%), meeting the primary endpoint. However, 20 resected patients (83.3%) recurred. Spatiotemporal analysis was performed on matched samples from four R and four NR. In pre-treatment samples from NR group, clusters dominated by fibroblasts (enriched for ECM, EMT, and CAF phenotypes) and B cell subtypes were distinct compared to R group, which exhibited elevated antibody responses and robust T cell and macrophage activity. NR samples showed increased IgM expression but lacked corresponding Fc receptors, suggesting limited immune effector function. Post-treatment NR surgical specimens displayed elevated expression of MUC5AC and CEACAM6 in malignant ductal cells, alongside genes promoting tumor growth. Neoadjuvant mFOLFIRINOX increased fibroblast, while reducing immune cell components. NR tumors formed distinct "resistance niches" after chemotherapy, marked by ECM production, CAF activation, and RTK signaling. Conversely, R tumors showed enhanced epithelial organization with increased type-I hemidesmosome complexes facilitating epithelial adhesion to the basement membrane. Conclusion: Three months of neoadjuvant mFOLFIRINOX was feasible and tolerable for resectable PDAC but associated with high recurrence rate, emphasizing the need for better patient selection. Spatiotemporal analysis revealed critical differences in TME dynamics according to treatment response, including the emergence of resistance niches and distinct pre-treatment spatial signatures. These findings support the integration of spatial biology into future pancreatic cancer trials to improve stratification and therapy. Citation Format: Choong-kun Lee, Changjin Hong, Ho Kyoung Hwang, Min Hwan Kim, Jean Rene Clemenceau, Seock-Jin Chung, Isabel Barnfather, Sumanth Reddy Nakkireddy, Jeong Hwan Park, Ji-Youn Sung, Minji Kim, Yooho Choi, Inyeop Jang, Sunho Park, Sung Hyun Kim, Chang Moo Kang, Hye Jin Choi, Tae Hyun Hwang. Spatiotemporal evolution of the tumor microenvironment predicts treatment response in phase II trial of neoadjuvant chemotherapy for resectable pancreatic cancer [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 771.

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 Background: Recent studies indicate a correlation between increased stromal composition in tumors and resistance to immune checkpoint inhibitors (ICIs) across various cancer types. Specifically, the extracellular matrix (ECM) within the stromal compartment significantly influence the tumor microenvironment, affecting cancer cell proliferation and immune cell recruitment. However, identifying and quantifying the ECM at the collagen fiber level in whole slide images (WSIs) is challenging due to its inherent heterogeneity and complexity. In this work, we utilized statistical analysis and a deep learning (DL)-based approach to identify and assess collagen in the ECM, examining its characteristics in relation to the ICIs treatment response in gastric cancer. Methods: We analyzed 116 WSIs of hematoxylin and eosin (H&E) stained slides before ICIs treatment from three institutions. Responders were defined as those with a complete response or partial response (N=18); those with stable disease (SD) (N=34) or progressive disease (PD) (N=64) were considered non-responders (N=98). For non-responders, post-treatment H&E slides were available for 21 patients; SD (N=5) and PD (N=16). We applied a DL-based second harmonic generation (SHG) synthesis algorithm to tumor regions identified by a tumor detection model. We then calculated various collagen properties, including volume, fiber orientation and alignment, waviness, straightness, and fiber thickness, based on collagen fiber centerlines extracted from the generated SHG images. An attention-based multiple instance learning (ABMIL) model was trained to automatically predict the treatment response solely based on collagen features. The dataset of 116 pre-treatment slides was divided into training and validation subsets using stratified 3-fold cross-validation. The area under the curve (AUC) was used to assess the performance of the trained models. Results: Collagen fiber features were significantly associated with ICIs response. Notably, ICIs responders have a decrease in collagen waviness and fiber orientation variation (p-value<0.05, by Mann-Whitney U test). Among non-responders, the PD group showed a statistical decrease in waviness after treatment (p-value<0.05, by Mann-Whitney U test). The ABMIL model achieved a mean AUC score of 0.823, with a standard deviation of 0.023. Conclusion: This study provides a comprehensive analysis of collagen in the ECM, and its properties in relation to gastric cancer ICIs response within Tumor Immune Microenvironment. We also developed a model using collagen features and demonstrated high accuracy in predicting treatment response. Citation Format: Minji Kim, Ekaterina Redekop, Yoonho Choi, Sung Hak Lee, Jae-Ho Cheong, Yu Aoki, Kohei Shitara, Tae Hyun Hwang. AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness 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 4262.

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.