Hwang, Tae Hyun

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

Abstract Although the majority of HPV (+) oropharyngeal squamous cell carcinoma (OPSCC) patients exhibit a favorable prognosis, approximately 20% face recurrent or metastatic disease, posing management challenges. Therefore, the identification of robust prognostic markers for risk stratification is essential. Our study focused on the comprehensive characterization of intratumor heterogeneity within the tumor immune microenvironment (TME) in both primary tumors and metastatic lymph nodes. Utilizing computational approaches, we integrated hematoxylin and eosin (H&E) and 11 adjacent immunohistochemistry (IHC)-stained slides to investigate the cellular composition and functional characteristics across different regions within the TME. Based on these detailed immune characteristics, we classified patients into specific immune subtypes. Our integrative TME analysis indicated that patients with high tumor-infiltrating lymphocytes (TIL), increased CD8 + levels, and reduced CD163 + cell counts within their primary tumors were likely to have a more favorable progression-free survival outcome. The integration of H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability and improves patient stratification in HPV(+) OPSCC.

Abstract Differences in clonality, genomic and epigenetic alterations are known to occur between primary Glioblastoma (pGBM) and recurrent (rGBM), but this has been insufficient to yield a promising therapeutic approach. Recent studies show that the GBM tumor microenvironment (TME), composed of reactive glial cells, microglial subtypes, neuronal activity, myeloid cells, lymphocytes, etc., plays a crucial role in the invasiveness and evolution of the glioma cells. Nevertheless, how the spatial architecture and interplay among these cells facilitates GBM resurgence is largely unknown. To this point, we developed a pipeline for characterizing the TME changes using single nucleus and spatial transcriptomics (ST). We retrospectively collected matched, fresh-frozen, IDH-wildtype pGBM and rGBM samples (n=14, 7 patients) from the Mayo Clinic Neuroscience Biobank. Single nucleus RNA sequencing was performed using the 10x Chromium platform. Matched FFPE blocks from 8 samples were collected from the Mayo Clinic pathology department for 4 of the 7 patients. A region of interest was identified from the corresponding histopathology slide by a pathologist, avoiding areas of necrosis and hemorrhage. The region was collected from the block using a 5mm biopsy punch. The tissue was re-paraffinized, and a 5µm thick section was used to perform ST with the 10x Visium for FFPE platform. We performed transcriptomic and image texture-based clustering of capture dots along with differential gene expression analysis. Based on gene expression, we identified and characterized conserved regions across samples. We used single nucleus data to infer cell composition. Spatial correlation and autocorrelation statistics were calculated on features, such as gene expression, gene set scores, cell type prediction, and antibody capture. The features’ relative spatial changes were compared between pGBM and rGBM. Our analyses showed high heterogeneity within and across samples, but we found higher molecular and textural heterogeneity in rGBM samples compared to pGBM. We found higher androgen response activity (p < 0.001) presents diffusely (I: 0.09) throughout rGBM tissue but localized to mesenchymal-like regions in pGBM (I: 0.15). KRAS signaling signature was decreased in pGBM (p < 0.01) and increased in rGBM (p < 0.01). This increase was significantly overexpressed in cluster 0 for rGBM (p=0.02), which is an area characterized by neural progenitor signature. No significant difference in KRAS or EGFR magnitude or distribution of expression was present for either GBM type, indicating an independent mechanism for pGBM. We established a pipeline for assessing the spatio-temporal changes of the GBM TME. We found evidence indicating differences in the type of growth signaling, the cells bing targeted, and their distribution across the TME between pGBM and rGBM. Our work suggests how ST can help us better understand the intricate dynamics of GBM. Citation Format: Jean R. Clemenceau, Paola Suarez Meade, Mark E. Jentoft, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment [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 144.

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.