Hong, Changjin

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 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.

Abstract Background: Immune checkpoint inhibitors (ICIs) targeting PD-1 or PD-L1 have dramatically improved survival of patients with gastric cancer (GC). However, recent clinical trials, including KEYNOTE-059, KEYNOTE-061, and ATTRACTION-2, show that the objective response rate (ORR) for ICIs is still less than 20% in patients with advanced GC. Therefore, predicting who will respond to ICIs in advance is essential to make better treatment plans for GC patients. There are potential biomarkers to predict ICI response, but most of them can only be applied to a limited population of patients. For example, Kim ST., et al., (PMID: 30013197) show that patients with MSI-high or EBV-positive responded well to ICIs, but these subtypes account for less than 30 percent of GC patients (PMID:25079317). In this study, we propose a prediction model of ICI response using the 32 GC-specific genes that we identified from mutation data (PMID: 26635139) and showed their close association with survival and ICI response in GC (PMID: 35140202; PMID: 36508166). In addition, since the number of available GC-ICI samples is small, we design our prediction model in Bayesian and ensemble learning frameworks which are robust to small sample size problems. Methods: We collected mRNA sequencing data from GC-ICI treated patients from our institutions and publications (PMID: 30013197; PMID: 35254400; PMID: 33846173). We used 108 samples previously included in our previous work (PMID: 36508166) as the training data and 27 samples (13 responders and 14 non-responders) from (PMID: 3384617) as the test data. We built and trained a classifier to separate responders from non-responders using the expressions of the 32 genes. We proposed to use a Deep Gaussian process (GP) model, a multi-layered extension of a GP that is itself a Bayesian method. We optimized the parameters of each GP model in variational inference. To implement ensemble learning, we trained multiple DGP models with different initializations of the model parameters and data splitting (training/validation sets). For each training run, the best model was selected based on the prediction performance on the validation set, measured by AUC. The final prediction for a test sample was made by combining the prediction results of all the DGP models by taking the average. Results: We implemented the DGP-based prediction model using PyTorch in Python, and the codes will be publicly available on the GitHub repository (https://github.com/hwanglab/GC_ICI_response_prediction). We achieved a prediction performance of 0.813 AUC on the test data (sensitivity = 0.769 and specificity = 0.714). Conclusion: We propose the prediction model of ICI response, which can handle small sample size problems in the framework of Bayesian and ensemble learning. The experimental results using the GC-ICI data demonstrate that the 32 genes can be used as predictive markers for ICI response in GC. Citation Format: Sunho Park, Minji Kim, Changjin Hong, Tae Hyun Hwang. A deep Gaussian process (DGP)-based prediction model for immunecheckpoint inhibitor (ICI) response in gastric cancer (GC) [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 2316.

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.

Abstract Background: Spatial molecular analysis can provide functional characteristics of cellular components and their interactions present in the tumor immune microenvironment (TME), thus improving our understanding of the cancer ecosystem at a systemic level. We investigated the spatial and functional characteristics of the tumor, stroma, and immune cells, and their roles in the TME in facilitating cancer development, progression, and metastasis in pancreatic cancer. Methods: We performed spatial profiles of multiple regional TMEs of 13 pancreatic cancers (7 pancreatic adenocarcinoma, 3 pancreatic neuroendocrine, and 3 acinar cell carcinomas) using NanoString GeoMX at the whole transcriptome level (18,676 genes). We collected a total of 23 tissue samples containing matched primary tumors, adjacent normal pancreatic tissue, or normal/metastatic lymph node. Tumor, stroma, and immune cells are stained by PanCK, αSMA, and CD45 markers, respectively. We selected 234 regions of interest (ROIs) in the tumor core, border, adjacent normal pancreas, and lymph node to investigate spatial patterns of different cells and their cellular interactions in neighborhoods from paired primary and metastatic tumors. Results: TMEs in different ROIs are spatially correlated within a tumor but showed distinct phenotypes across tumors and tumor types. Each cell type in the ROIs has a unique role to drive cancer evolution in TMEs. Diverse types of cancer-associated fibroblasts (CAFs) are present in primary and metastatic TME, while CAFs in tumor border and metastatic lymph nodes showed mainly enriched with antigen-presenting CAF (apCAF) subtypes with higher extracellular matrix (ECM) and collagen fibrils components. Similarly, we found diverse functional phenotypes of the immune cell in TMEs. The immune response signaling pathway is activated in the stroma-infiltrated immune cells, but, in fact, not in the tumor-infiltrated immune cells. Instead, higher dysfunctional T cell and growth factors are present in the stroma-infiltrated immune cells from primary and metastatic tumors, but not in stromal immune normal pancreatic and lymph node tissues. The tumor-infiltrated immune cells showed suppressive immune activities by increasing TGFβ receptor signaling. We also discovered CCL5-driven metastatic patterns in TMEs from primary tumors and metastatic lymph nodes. Lastly, we concluded that these functional phenotypes of cells in TMEs are preserved in a certain pancreatic cancer type, suggesting the origin and evolution of different pancreatic tumors could be driven by diverse cellular and functional phenotypes of cells in TMEs. Conclusion: Our comprehensive analysis results demonstrate that the spatial analysis could provide insights into the spatial patterns that drive cancer evolution in pancreatic TMEs, thus helping understand the cellular microenvironment governing pancreatic cancer metastasis. Citation Format: Changjin Hong, Jean R. Clemenceau, Rondell P. Graham, Tae Hyun Hwang. Spatial transcriptomic landscape of primary and lymph node metastatic pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2362.

464 Background: A larger proportion of Hispanic gastric cancer patients are diagnosed at a young age than patients of other ethnicities. Despite diagnosis at an early age, young Hispanic patients also present more frequently with advanced disease. Whether there are molecular differences between tumors from young and old Hispanic gastric cancer patients is unknown. Methods: We performed RNA-sequencing on pre-treatment tumor samples of gastric cancer patients of self-identified Hispanic ethnicity. We stratified patients into two groups: <50 years and ≥50 years. Differentially expressed genes between groups were detected using the DESeq2 package (RStudio). We performed Gene Set Enrichment Analysis (GSEA) to identify pathways that were uniquely overexpressed in each age group. Results: 57 patients were included in our study. 23 patients were <50 years and 33 patients were ≥50 years. Comparison of the top 10% most differentially expressed genes showed no clustering based on age. However, visual inspection of the top 40 most differentially expressed genes revealed 2 distinct patterns of gene expression between age groups. GSEA revealed upregulation of DNA replication, cell cycle, and DNA repair pathways in the younger patients The tumors in younger patients also had upregulation of multiple immune activation pathways. Cancers from older patients had upregulation of programmed cell death pathways. Conclusions: Early-onset gastric cancer in Hispanic patients may represent a distinct biologic process.

Abstract Purpose: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. Experimental Design: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. Results: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02–1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34–1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA–positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). Conclusions: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Abstract Background: Gastric carcinogenesis has been found to be associated with intragastric microbiome, gastric mucosal gene expression, and immune cells comprising the tumor microenvironment separately in independent studies. However, to date, there has been no multi-omic investigations analyzing these biological traits simultaneously. Thus, there is a paucity in our understanding of the intricate relationship between intragastric microbes and the host in gastric disease. To address this gap, we aimed to comprehensively investigate the microbiome, host gene expression, and immune cells in the stomach across a range of different states along the gastric carcinogenesis pathway. Methods: Gastric biopsy samples were obtained from healthy individuals or patients with gastritis or gastric cancer (biopsy cohort, n=30). Surgical samples (of cancer and adjacent severe gastritis tissues) were obtained from gastric cancer patients (surgery cohort, n=40). Using 16S rRNA gene sequencing, RNA-seq, and cell-type enrichment analysis of each participant’s biospecimens, we identified associations between the microbiome and host transcriptome or inferred immune cell types. Results: Microbiome composition and gene expression patterns showed significant differences between disease states (healthy, gastritis, or cancer). Expression of ‘Cell cycle’ pathway genes was enriched in gastric cancer in both biopsy and surgery cohorts. In the biopsy cohort, Helicobacteraceae, which was abundant in gastritis, was highly correlated with FAM3D, which was previously found to be involved in gastrointestinal inflammation. In the surgery cohort, Lachnospiraceae, which was abundant in gastric cancer, was highly correlated with UBD, which is known to regulate mitosis and reduce cell cycle time. Cell-type enrichment analysis revealed lower B cell infiltration in gastric cancer than in gastritis (both cohorts). Tissue-infiltrating B cells were associated with Helicobacteraceae. Conclusions: Associations between the intragastric microbiome, human gene expression, and tissue-infiltrating immune cells are unique to gastric disease state. The findings herein may aid the discovery of novel biomarkers or therapeutic targets of gastric cancer. Citation Format: Chan Hyuk Park, Changjin Hong, A-reum Lee, Jaeyun Sung, Tae Hyun Hwang. Integrated analysis of intragastric microbiome and human gene expression uncovers genetic, microbial, and immunological associations in gastritis and gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5013.