Citations

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.

Abstract Introduction As total neoadjuvant therapy for locally advanced rectal cancer (LARC) emerged, the possibility of skipping radiotherapy for poorly responsive patients arose. Machine-learning algorithms have focused on the radiopathologic features of tumor segmentation in order to predict responsiveness to radiotherapy. However, in addition to the tumor itself, there are several factors related to responsiveness, such as vasculature affecting hypoxia or MRI-detected extramural vascular invasion status. We aimed to predict poor responders using pretreatment rectal MRI images without segmentation and to identify which factors mainly contribute to the prediction algorithm. Methods Between Jan 1, 2000, and Dec 30, 2020, 689 consecutive patients were retrospectively included in two hospitals. Poor responders were defined by tumor regression grades (TRG) 2 and 3 that were determined through surgical resection. The ResNet-50 model was trained to predict poor responders from pretreatment rectal MRIs (T2-weighted axial, sagittal, and coronal images). We adopted a tenfold cross-validation for training and testing the model and used Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important regions in the MRI scans that help predict poor responders. Results The number in each group of TRG was 108 (15.7%), 250 (36.3%), 265 (38.5%), and 66 (9.6%) for TRG0, TRG1, TRG2, and TRG3, respectively. There were 618 patients in the training cohort and 71 patients in the validation cohort. In the training and validation cohort, the accuracy for the prediction of poor responders was 85.6% (area under the curve (AUC) 0.856 [95% CI 0.761-0.950]) and 70.2% (AUC 0.703 [0.682-0.724]), although without segmentation. Our prediction model achieved a sensitivity of 0.724 (95% CI 0.700-0.748), a specificity of 0.684 (0.658-0.710), a positive predictive value of 0.697 (0.656-0.737), and a negative predictive value of 0.708 (0.666-0.751) in the validation cohort. Grad-CAM showed that the most important part of the accurately predicted images to contribute to the prediction was not the tumor (7/355, 1.9%) but the pelvic vasculature (353/355, 99.4%), including iliac vessels, femoral vessels, and presacral plexus, and followed by the mesorectum (38/355, 10.7%). Conclusion The pelvic vasculature contributes more to predicting poor responders to radiotherapy than the tumor itself. Therefore, when creating a prediction model for responsiveness to radiotherapy in LARC, this should be considered. Citation Format: Rumi Shin, Byunho Jo, Inyeop Jang, Cheong-Il Shin, Jin Sun Choi, Seung-Yong Jeong, Seung Chul Heo, Ji Won Park, Min Jung Kim, Tae Hyun Hwang. Why only focus on the tumor?: The crucial role of the extra-tumor environment to predict poor responders for locally advanced rectal 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 5374.

Abstract Background: The Tumor Immune microenvironment (TME) plays an important role in tumor development, progression, and treatment response. Recent studies show that spatial heterogeneity of Tumor-Infiltrating Lymphocytes (TILs), consisting of lymphocytic cell populations, is an important histopathologic feature and a powerful prognostic and predictive marker correlating with cancer progression and treatment response. Detecting and quantifying TIL density in Whole Slide Images (WSIs) is, however, labor-intensive, and it is difficult to assess differences in the composition of immune cells. Therefore, it remains a challenge to establish the analysis of TILs in the complex TME. We present an automated spatial image analysis of TILs on gastric cancer to address cancer immunotherapy. Method: 1,031WSIs of hematoxylin and eosin-stained (H&E) formalin-fixed paraffin-embedded (FFPE) sections from four patient cohorts: TCGA-STAD (329), Yonsei-1 (620 patients), Yonsei-2 (45), and St. Mary (37), were analyzed. Two treatment groups in response were classified as CR + PR, while another two treatment groups in non-response were classified as PD + SD. H&E stained WSIs were normalized using the Reinhard normalization method at 20X magnification for reducing the color variability of the stained tissue images. We used U-Net architecture to predict the normalized H&E stained WSIs. Three U-Net models were trained for predicting tumor, TILs, and stroma, using Adam optimizer, 1e-4 learning rate, a binary loss function, L2 regularization, and 50 epochs. The normalized H&E stained WSIs were predicted using the trained U-Net models. Spatial analysis of TILs was conducted on the predicted WSIs. Regional quantification of TILs was performed by two immune phenotype groups: immune-immersed (TILs enriched in tumor cells) and immune-excluded (TILs not enriched in tumor cells but in stromal cells). Kaplan-Meier with a log-rank test was used to evaluate the survival differences between the groups. Results: In TCGA-STAD, the proportion of immune-immersed composition is significantly different from the immune-excluded (p-value 0.05). In Yonsei-1, the immune-excluded composition is significantly different among the three risk groups (p-value 3e-05). In the Yonsei-2 and St. Mary cohorts, including anti-PD1 treated gastric cancer patients, the immune-immersed composition is significantly different between two response groups (p-value 0.007) and represented the diagnostic accuracy (AUROC: 0.78) on CR + PR groups. Conclusion: These findings suggest that the proposed TIL quantification in the complex TME is strongly associated with patient prognosis and anti-PD1 treatment response in gastric cancer. Citation Format: Sanghoon Lee, Sunho Park, Jae-Ho Cheong, Sam C. Wang, Matthew R. Porembka, In-Ho Kim, Sung Hak Lee, Tae Hyun Hwang. Spatial image analysis of tumor-infiltrating lymphocytes on gastric cancer to predict anti-PD1 inhibitor response [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 1360.

Abstract Background: While immune checkpoint inhibitors (ICI) extend survival for patients suffering from a variety of cancers, most gastric cancer (GC) patients do not respond to ICI. There is a critical need to identify novel biomarkers that guide the use of ICI or strategies to improve outcomes for GC patients. We aim to gain a deeper understanding of the GC tumor microenvironment (TME) to refine the use of ICI using spatial transcriptomes from ICI-treated gastric tumors. Method: We collected 12 pre-ICI treatment gastric tumor samples from five responders and seven nonresponders and performed spatial profiling to investigate spatial heterogeneity, functions, and phenotypes of tumor and neighboring stromal and immune cells to identify features associated with ICI response. We used pan-cytokeratin (pan-CK) to mark epithelial cells, CD45 for immune cells, and smooth muscle actin (SMA) for fibroblasts. We generated gene expression profiles of each cell type within regions of interest (ROI) with a NanoString GeoMx Cancer Transcriptome Atlas panel, comprised of 1,800 genes. A total of 58 ROIs were analyzed from responders and 62 ROIs from nonresponders. Results: We measured the spatial heterogeneity of each cell type in ROIs within and across tumors. We found the tumor, immune, and fibroblasts from nonresponders are transcriptionally more heterogeneous, compared to those from responders. This might indicate a higher degree of intratumor transcriptional heterogeneity is linked to ICI resistance. We found that nonresponse was associated with higher expression of VEGFA and VEGFB in tumor cells and higher ACTA2 in fibroblasts, not in other cell types. These findings are intriguing when considering our other finding that increased endothelial cell and fibroblast proportions in the TME are associated with ICI nonresponse. We also found that the TME of nonresponders had fewer M1 and more M2 macrophages, and there were more T cells that overexpressed gene programs consistent with dysfunction and exhaustion. These findings indicate that ICI-resistant TMEs are densely surrounded by immune-suppressive cellular components which might reduce ICI efficacy and result in ICI resistance. Finally, we performed cell-cell interaction and identified ligand-receptor interactions that are stronger in nonresponders, including CXCL12-CXCR4, CD74-MIF, and LGALS9-LRP1 ligand-receptor interactions. The predicted ligand-receptor interactions are present in cell-type-specific manners, indicating the functional role of each cell type in facilitating ICI response. Conclusion: We investigated how tumor and neighboring stromal and immune cells interact within GC TME to mediate ICI response, which demonstrate that spatial analysis could be used to identify novel biomarkers that predict response to ICI, and new therapies to enhance ICI efficacy in GC patients. Citation Format: Sunho Park, Changjing Hong, Jae-Ho Cheong, Sam C. Wang, Matthew R. Porembka, In-Ho Kim, Sung Hak Lee, Tae Hyun Hwang. Spatial architecture and cellular interactions of tumor immune microenvironment to discover biomarkers and predict immune checkpoint inhibitor response in gastric 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 2262.

Abstract Background: Recent studies support that high levels of tumor-infiltrating lymphocytes (TILs) were associated with better prognosis in HPV(+) oropharyngeal squamous cell carcinoma (OPSCC). While these studies provide a potential role for TILs as a prognostic biomarker, the analyses were relied on manual quantification performed by pathologists, resulting in inter-observer variability. Deep Learning (DL)-based whole slide hematoxylin and eosin (H&E) image analyses may overcome these challenges. However, this analysis only provides the presence of cell types without consideration for the functional roles of each cell within the tumor immune microenvironment (TME). In this work, we develop a computational pipeline to integrate paired H&E and immunohistochemistry (IHC) images to functionally characterize TILs and investigate their prognostic utility. Methods: We analyzed 88 patients: 42 stage I, 39 stage II, and 7 stage III. Our data contain both H&E and IHCs examining FoxP3, CD3, PD-L1, CD20, etc. on serial sections of the tissue. In-house DL-based H&E analysis used to identify TILs, tumors, and stroma in each tumor, then performed registration between adjacent H&E and IHC images from the same tissue. The patients were then classified into three basic immune phenotypes: immune inflamed (IN; high TILs in the tumor region), immune excluded (EX; TILs are mostly localized in stroma), and immune desert (ID; few/no TILs) based on TIL enrichment in the TME. To functionally characterize TILs, we quantified protein expression from the adjacent IHCs. For example, we further classified each patient into different subtypes based on enriched protein expression (e.g., FoxP3 high IN, FoxP3 low IN). We used the Kaplan-Meier method and multivariate Cox analysis to evaluate the prognostic value of different subtypes enriched with different proteins to predict disease-free survival (DFS). Results: The IN group with 43 patients was significantly associated with good prognosis. Interestingly, further stratification of the IN subgroup based on Foxp3 quantification on TIL regions (i.e., high FoxP3 IN and low FoxP3 IN) showed that high protein expression of FoxP3 in TILs in the IN subgroup is significantly associated with a better prognosis compared to other immune subgroups (HR, 0.16; p-value, 0.003). Multivariate analysis, including other clinical covariates showed that the immune subtypes associated with high FoxP3 are independently associated with DFS. These results demonstrate that DL-based integrative IHC and H&E image analysis could be used to identify subgroups with distinct clinical outcomes. Furthermore, our results reveal unknown roles for Foxp3 expression in the TILs in HPV(+) OPSCC as a prognostic biomarker, a finding which should be evaluated in a larger cohort. Citation Format: Sumanth Reddy Nakkireddy, Inyeop Jang, Minji Kim, Linda X. Yin, Michael Rivera, Joaquin J. Garcia MD, Kathleen R. Bartemes, David M. Routman, Eric J. Moore, Daniel J. Ma, Kathryn M. Van Abel, Tae Hyun Hwang. Integrative spatial analysis of paired IHC and H&E images identifies Foxp3 enriched tumor-infiltrating lymphocytes associated with disease-free survival in human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma. [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 4638.

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 Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

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.