Citations

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.

Abstract Background: Cancer-associated fibroblasts (CAFs) are a highly diverse cell population that plays a crucial role in shaping the tumor microenvironment (TME). Their spatial interactions within the TME remain to be systematically characterized, and the factors driving their phenotypic heterogeneity are not yet fully understood. Recent advancements in single-cell spatial transcriptomics technologies and cell segmentation approaches enable the precise measurement of thousands of transcripts at subcellular resolution. Combined with innovative bioinformatics, these advancements have unlocked unprecedented opportunities for in-depth characterization of CAFs and their interactions within the TME. Methods: We conducted a comprehensive spatial transcriptomic analysis of CAFs across 8 cancer types, using the CosMx and MERSCOPE platforms to profile nearly 6 million cells as part of our discovery cohorts. We developed a novel computational method to spatially cluster CAF subpopulations and defined four distinct spatial CAF subtypes. We further examined their phenotypic differences, spatial niche organization, and interactions with neighboring cells. Additionally, we investigated the influence of CAF subtypes on immune cell phenotypes and infiltration levels. To validate our findings, we employed multiple spatial platforms, including Visium, Xenium, COMET, CODEX, and IMC, to ensure reproducibility across diverse technologies. Moreover, we performed correlative studies in large-cohorts with rich clinical data to evaluate the clinical significance of these CAF subtypes. Results: Our approach provided an unprecedented high-resolution examination of the spatial phenotypes of CAFs in large, complex tissues, uncovering novel insights. We comprehensively characterized CAFs across various solid tumor types and identified four conserved spatial CAF subtypes. These subtypes are characterized by distinct cell organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic features. Moreover, they demonstrate significant correlations with the abundance, distribution, and phenotypes of tumor-infiltrating immune cells, as well as associations with tumor histopathological features and clinical outcomes. Conclusions: Our study provides a pan-cancer, high-resolution characterization of CAF spatial organization and interactions in solid tumors and identified conserved spatial CAF subtypes linked to distinct organizational patterns. Our findings reveal that the transcriptomic states and phenotypes of CAFs are significantly shaped by their interactions with neighboring cancer and immune cells. Beyond offering novel insights into the spatial heterogeneity of CAFs, this work paves the way for developing strategies to effectively target and modulate CAFs for therapeutic benefit. Citation Format: Yunhe Liu, Ansam Sinjab, Jimin Min, Guangchun Han, Francesca Paradiso, Yuanyuan Zhang, Yang Liu, Ruiping Wang, Guangsheng Pei, Kyung Serk Cho, Enyu Dai, Akshay Basi, Jared K. Burks, Kimal I. Rajapakshe, Yanshuo Chu, Jiahui Jiang, Daiwei Zhang, Xinmiao Yan, Paola A. Guerrero, Alejandra Serrano, Mingyao Li, Tae Hyun Hwang, Andrew Futreal, Jaffer A. Ajani, Luisa M Solis Soto, Amir A. Jazaeri, Humam Kadara, Anirban Maitra, Linghua Wang. Spatial subtypes and cellular interactions of cancer-associated fibroblasts revealed by single-cell spatial omics [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 166.

Abstract Purpose: Black women are disproportionately affected by aggressive breast cancers compared to white women, suggesting underlying biological differences in normal breast tissue. This study aims to identify molecular disparities in normal breast tissue between premenopausal Black and white women that may contribute to increased breast cancer risk. Experimental Procedures: Visium spatial transcriptomic analysis was performed on a pilot cohort of normal breast tissue samples from premenopausal Black and white women. Tissue sections were spatially profiled, and capture spots were classified based on predominant cell types: epithelial, stromal, or endothelial. Differential gene expression analysis between racial groups was conducted for each cell type classification. Multiplex immunofluorescence (mIF) was used to validate selected markers in the pilot cohort. Meta-analysis compared differentially expressed genes with published gene sets associated with breast cancer subtypes. Results: Significant racial differences in gene expression profiles were identified in both epithelial and stromal spots. In epithelial spots, genes upregulated in Black women significantly overlapped with over 20 published gene sets associated with estrogen receptor-negative (ER−) breast cancer, including key genes involved in Wnt signaling inhibition (e.g., WIF1) and basal epithelial markers (e.g., KRT14, KRT15). This suggests alterations in pathways linked to more aggressive breast cancer phenotypes. In stromal spots, differentially expressed genes related to extracellular matrix remodeling and fibroblast activation (e.g., PI16, DPT, EFEMP1) were observed, indicating potential differences in stromal-epithelial interactions. Ongoing deconvolution and spatial analyses are being performed to further elucidate these findings. Preliminary mIF validation confirmed increased expression of PI16 in samples from Black women. Validation of additional markers is underway in a larger cohort of premenopausal Black and white women. Conclusions: Spatial transcriptomic profiling of normal breast tissue reveals distinct molecular differences between Black and white women, particularly in pathways associated with aggressive breast cancer subtypes and stromal interactions. These findings suggest that inherent variations in the normal breast microenvironment may contribute to the increased risk and aggressiveness of breast cancer in Black women. Further validation and analysis may identify potential biomarkers for risk assessment and inform targeted prevention strategies. Citation Format: Camila Pacheco Erak, Nicole Cruz-Reyes, Pria Westerman, Joshua Ogony, Jean R. Clemenceau, Tae Hyun Hwang, Sarah McLaughlin, Derek Radisky. Spatial transcriptomic profiling of normal breast tissue reveals potential mechanisms underlying increased breast cancer risk in Black women [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 765.

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 The extracellular matrix (ECM) plays a critical role in cancer progression, with its biophysical properties influencing cancer behavior. ECM stiffness can induce long-lasting adaptations in cancer cells, promoting sustained invasion through a process known as “mechanical memory” (MM). Glioblastoma (GBM) exhibits spatial heterogeneity in stiffness, with stiffer ECM found at the tumor periphery where invasive cells reside and promote recurrence. We hypothesize that increased stiffness at the tumor periphery primes GBM cells for sustained invasion through MM. METHODS Cell lines were established from image-guided human GBM samples from tumor margin and core (n=10 patients, 20 cell lines). Cells from random locations were conditioned in physiologically relevant extracellular matrices for two weeks. Human organotypic assay (n=3), Migration Assays, Atomic Force Microscopy (AFM), Immunocytochemistry, Western Blot, and RNA-sequencing (n=6) were performed. RESULTS GBM cells at higher stiff surfaces displayed increased migration (p<0.01). Magnetic Resonance Elastography (n=9) confirmed GBM heterogeneity, with softer cores (0.2-0.5kPa) transitioning to stiffer margins (>2kPa). Organotypic scaffolds mimicking the tumor margin promoted enhanced migration of GFP-labeled GBM cells. Margin cells and cells conditioned on stiffer matrices demonstrated higher migratory potential compared to their core and soft-conditioned counterparts (p<0.05). These trends persist over time and across multiple passages. ECM stiffness exposure causes nanomechanical, cytoskeletal, and transcriptional adaptations. AFM shows that cells from the margin (n=6) are softer (p<0.01) and more deformable (p<0.01). RNAseq identified upregulation of MM-specific genes in GBM margin cells. The mechanosensitive transcription factor YAP1 emerged as a key driver of MM. Notably, YAP1 is targetable by the FDA-approved drug Verteporfin. YAP1 expression was elevated in margin and stiff-conditioned cells (p<0.05). Stiff-conditioned cells and margin cells displayed increased sensitivity to Verteporfin treatment (p<0.05), with treatment significantly reducing margin migration compared to the core (p<0.001). CONCLUSION Stiffer GBM margins promote the formation of aggressive phenotypes through MM. Targeting YAP1 with Verteporfin, a readily available drug, presents a promising strategy to halt GBM invasion and improve patient outcomes.

Summary: The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Abstract Glioblastoma remains as one of the deadliest malignancies with most cases likely to recur. Recent work has focused on characterizing not only glioma cells, but the tumor microenvironment via single-cell and spatial transcriptomic technologies. This has improved our understanding of the high heterogeneity of this disease and described potential tumor domains. However, there is still a need to further understand the changes that occur between the primary and the recurrent lesion. We propose that a spatial, multi-omic characterization of matched primary and recurrent glioblastoma can yield insights into recurrence mechanisms occurring throughout the tumor microenvironment. We collected 22 IDH-WT glioblastoma samples from 12 patients (11 primary, 11 recurrent, 10 matched pairs) from the Mayo Clinic Neuroscience Biobank. We performed single-nucleus RNA sequencing (15 samples, 10 patients, 5 matched pairs) and proteomics (16 samples, 6 matched pairs) from fresh-frozen tissue, as well as spatial transcriptomics with 10x Visium (14 samples, 8 patients, 6 matched pairs) and 10x Xenium in-situ molecular imaging (6 samples, 2 matched pairs). Using spatial transcriptomics data, we identified regional subsets present in different proportions and distributions between primary and recurrent glioblastoma. These clusters were characterized with gene set and gene ontology analysis, identifying regions enriched with known glioma subtypes and cellular activity. Spatial-based cluster genes were consistent with significantly differentially expressed proteins. We used single-nucleus RNAseq and in-situ molecular imaging data from matched tissue samples to further interrogate relevant cell populations and location in the tumor micro-environment. We identified regions with enriched extra cellular matrix interaction and remodeling signatures to be highly expressed in recurrent glioblastoma. Primary glioblastoma was enriched with oligodendrocyte-like regions with high cell communication and extracellular vesicle signatures. This study will further our understanding of the changes in the mechanisms at play in the complex ecosystem of glioblastoma as it recurs. Citation Format: Jean R. Clemenceau, Paola Suarez-Meade, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment [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 5482.

Abstract Background: Analyzing interactions and the microanatomical distribution of the tumor immune microenvironment (TIME) is essential for a comprehensive understanding of tumor progression. Imaging Mass Cytometry (IMC) is a high-dimensional tissue imaging system that enables the deeper and multiparametric in situ exploration of tumor microenvironments at a single-cell level. This study describes a pipeline for analyzing 39-plex IMC images of human HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) tumor sections, elucidating immune cells' nature, functions, and interactions with tumor cells. The IMC data analysis provides valuable information for clinical studies that could be used for the identification of prognostic biomarkers and mechanisms of resistance to current immunotherapies. Methods: A retrospective study was conducted on (N=20) patients (10 progressors and 10 non-progressors) with HPV(+) OPSCC. The dataset comprises paired hematoxylin and eosin (H&E) images with IMC data from multiple Regions of Interest (ROIs) (N=82), enabling the in-situ profiling of 39 proteins in primary tumor tissue. Cell segmentation was followed by employing original images and their segmentation masks for cell phenotype identification using a probabilistic neural network method to spatially profile cancer cells, macrophages, NK-M cells, B-cells, and distinct T-cell populations. We identified protein expression at single-cell resolution and investigated the impact of multicellular spatial organization on the disease progression. The spatial neighborhood interaction score of tumor cells with various other cells was computed for all the ROIs across the two groups. Results: The markers showed varying expression levels of intensity distribution, likely due to differences in tissue sample composition. Specifically, CD3, CD4, CD8, CD19, CD20, and CD68 showed significantly higher intensity in non-progressors. Interestingly, FoxP3 also had a significantly higher intensity level in non-progressors. Cell phenotyping emphasized that non-progressors generally have increased levels of NK-M cell markers. Spatial analysis revealed a significant increase in interaction between CD3 cells and tumor cells in non-progressors (p-value=0.07, Mann-Whitney U test). Furthermore, a significant rise in the interaction between B-cells and FoxP3+ cells was observed in non-progressors (p-value<0.05, Mann-Whitney U test) suggesting a coordinated suppression of tumor growth. Conclusion: We developed a deep learning-based pipeline to analyze spatial relationships in the TIME of IMC data, demonstrating its clinical relevance in predicting disease progression. Our findings emphasize the importance of spatial relationships in the TME for response and suggest that proximity between either CD3+ T-cells to cancer cells or B-cells to FoxP3+ cells are candidate biomarkers. Citation Format: Sumanth Reddy Nakkireddy, Routman David, Kathleen Bartemes, Daniel Ma, Tae Hyun Hwang, Kathryn Van Abel, Chadi Nimeh Abdel-Halim. AI-enhanced spatial proteomics reveals B-cells & FoxP3+ cells interactions in HPV(+) oropharyngeal squamous cell carcinoma Progression [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 5488.