Citations

The distinct impacts of fouling mechanisms on a carbon fiber microelectrode and an Ag/AgCl reference electrode, affecting their performance in neurotransmitter detection via Fast-scan Cyclic Voltammetry.

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.

Abstract Background: PDAC is a particularly fatal condition lacking established biomarkers for the choice of first-line treatment (1L). The PurIST algorithm is a gene signature (Rashid et al. Clin. Cancer Res., 2020) that categorizes PDAC tumors into basal or classical molecular subtypes and has been previously validated in a patient cohort of advanced PDAC (N=258). In this post-hoc analysis study, we examine whether the PurIST subtypes can distinguish patients likely to respond to FOLFIRINOX (FFX) versus Gemcitabine + nab-paclitaxel (GnP). Methods: A cohort of de-identified PDAC patients was selected from the Tempus Oncology Database following an IRB-approved protocol. Inclusion criteria required no prior systemic treatment, a surgically unresectable/metastatic status, available RNA-sequencing data from primary or metastatic tissue, and the use of FFX or GnP as 1L systemic therapy with documented outcomes. RNA-sequencing was generated by the CAP/CLIA validated Tempus xR assay. The PurIST gene signature was applied to normalized gene expression values to classify samples into basal or classical subtypes. Univariate and multivariate Cox PH analyzes comparing overall survival (OS) with adjustment for delayed entry, between two common 1L treatment regimens, FFX and GnP, were performed for classical patients with an ECOG performance status of 0 or 1. Results: Application of the PurIST model to the PDAC cohort (N=258) resulted in 67% (N=173) labeled as classical, 23% (N=59) as basal, and the remainder as no-call. Amongst classical patients, 35% (N=61) had a known ECOG score of 0 or 1 and were retained for subsequent analysis. Within this subset, 37 were treated with FFX and 24 with GnP as 1L, with an overall median OS of 11.4 months (95% CI: 9.5-16.5) and a 12-month survival rate of 47.2%. A univariate Cox PH analysis showed that classical patients treated with FFX had a significantly longer OS time (HR (95% CI) = 2.32 (1.29-4.17)). This result remained significant in a multivariate Cox PH, adjusting for age and ECOG, (HR=2.5 (1.15-5.44)). Conclusion: In this real-world cohort of advanced PDAC patients, we showed that classical patients with low ECOG scores had superior OS with 1L FFX treatment compared to 1L GnP. These findings demonstrate the potential for PurIST to aid in optimal treatment selection for patients with advanced PDAC. Citation Format: Stephane Wenric, James M. Davison, John Guittar, Gregory M. Mayhew, Kirk Beebe, Alia Zander, Seung Won Hyun, Kyle Beauchamp, Michael V. Milburn, Vincent Chung, Tanios S. Bekaii-Saab, Charles M. Perou. Real-world validation of the PurIST classifier demonstrates enhanced therapy selection for pancreatic ductal adenocarcinoma (PDAC) patients [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 2542.

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 Lung cancer is the leading cause of cancer-related deaths worldwide. Existing therapeutic options have limited efficacy, underscoring the critical need for the identification of new actionable therapeutic targets. We previously identified the Transmembrane Serine Protease TMPRSS11B as a novel gene that promotes the transformation of human bronchial epithelial cells in vitro and induces tumorigenesis in vivo. TMPRSS11B promotes extracellular release of Basigin, an obligate chaperone of the lactate monocarboxylate transporters MCT1/4, enhancing lactate export and glycolytic metabolism, and thereby promoting tumorigenesis. Importantly, TMPRSS11B is frequently overexpressed in human lung squamous cell cancers (LSCC) and high expression is associated with poor patient survival. Moreover, as a cell surface protein and enzyme, TMPRSS11B represents a tractable target for therapeutic intervention. To investigate whether TMPRSS11B activity impacts the host immune system and the tumor microenvironment (TME), we evaluated the effect of Tmprss11b depletion in a syngeneic LSCC mouse model, KLN205. Tmprss11b loss of function in KLN205 LSCC cells significantly reduced tumor burden in immunocompetent mice and triggered an accumulation of CD4+ T cells. We are currently extending these studies by investigating the effect of Tmprss11b loss of function using CRISPR/Cas9 editing in the Rosa26LSL-Sox2-IRES-GFP; Nkx2-1 fl/fl; Lkb1fl/fl (SNL) mouse model of LSCC. Tumor burden analysis and pH-sensitive fluorescent imaging are ongoing to assess changes in lactate accumulation. Moreover, RNA FISH analysis and spatial transcriptomics revealed that Tmprss11b expression is highly enriched in lung squamous tumors compared to lung adenocarcinomas and normal lung in SNL mice. Spatial analysis of the transcriptome is currently ongoing to assess the effects of Tmprss11b expression on the TME. Collectively, these studies will elucidate the molecular mechanisms through which TMPRSS11B promotes tumor growth and alters the tumor microenvironment in LSCC. Citation Format: Hari Shankar Sunil, Barrett L. Updegraff, Jingfei Zhu, Lisa Thomas, Bret M. Evers, Jean R. Clemenceau, Isabel Barnfather, John D. Minna, Ralph J. Deberardinis, Trudy G. Oliver, Tae Hyun Hwang, Kathryn A. O'Donnell. Transmembrane serine protease TMPRSS11B promotes tumorigenesis in lung squamous cell carcinoma through enhanced lactate export and modulation of the tumor 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 1793.

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 Background: Microsatellite instability-high (MSI-H) tumors respond well to immune checkpoint blockade and oftentimes not to chemotherapeutics. Despite value of determining MSI status to guide therapy, many patients’ samples are not untested. Deep learning models may be able to predict MSI status by analyzing hematoxylin and eosin (H&E) stained whole-slide images (WSIs). Validation of this tool will provide another tool to increase MSI testing. However, to be deployed into clinical routine care, prediction models need be externally validated in large and diverse patient cohorts. In addition, these models should provide uncertainty of prediction to help clinicians make informed decisions. Results: In this study, we introduce a novel Bayesian prediction model using whole-slide images (WSIs) and deep Gaussian processes (DGPs) in a weakly supervised learning context. Extensively tested on large datasets (n=3,146) from multiple centers, our model not only demonstrated superior performance but also showed that incorporating uncertainty into predictions significantly improves MSI prediction accuracy, achieving AUROC of >0.91 in colon cancer and >0.94 in gastric cancer. Additionally, an in-depth analysis within the gastric cancer cohort indicated a significant correlation between predicted MSI-H regions and immunotherapy response. Patients who responded to immune checkpoint inhibitors had a higher proportion of MSI-H regions (P-value < 0.05). This correlation was also observed in patients categorized as MSS but responsive to ICIs, highlighting the potential of our model to identify MSI-H patterns not detected through standard laboratory tests. This advancement could lead to more precise patient selection for ICI therapy, enhancing personalized treatment strategies in gastrointestinal cancer. Citation Format: Sunho Park, Minj Kim, Ji-Youn Sung, Jeong Hwan Park, Sung Hak Lee, Sam C. Wang, Jaeho Cheong, Tae Hyun Hwang. Explainable AI model incorporating uncertainty estimation for enhanced MSI-H prediction and immunotherapy response in gastrointestinal 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 7385.

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.