Multiplexed Tissue Imaging Research Articles

The Desmoplastic Stroma of Pancreatic Cancer: Multilayered Levels of Heterogeneity, Clinical Significance, and Therapeutic Opportunities

Simple SummaryPancreatic cancer is a highly malignant disease with treatment resistance to standardized chemotherapies. In addition, only a small fraction of patients with pancreatic cancer has, to date, actionable genetic aberrations, leading to a narrow therapeutic window for molecularly targeted therapies or immunotherapies. A lot of preclinical and translational studies are ongoing to discover potential vulnerabilities to treat pancreatic cancer. Histologically, human pancreatic cancer is characterized by abundant cancer-associated fibrotic stroma, called “desmoplastic stroma”. Recent technological advances have revealed that desmoplastic stroma in pancreatic cancer is much more complicated than previously thought, playing pleiotropic roles in manipulating tumor cell fate and anti-tumor immunity. Moreover, real-world specimen-based analyses of pancreatic cancer stroma have also uncovered spatial heterogeneity and an intertumoral variety associated with molecular alterations, clinicopathological factors, and patient outcomes. This review describes an overview of the current efforts in the field of pancreatic cancer stromal biology and discusses treatment opportunities of stroma-modifying therapies against this hard-to-treat cancer.Pancreatic cancer remains one of the most lethal malignancies and is becoming a dramatically increasing cause of cancer-related mortality worldwide. Abundant desmoplastic stroma is a histological hallmark of pancreatic ductal adenocarcinoma. Emerging evidence suggests a promising therapeutic effect of several stroma-modifying therapies that target desmoplastic stromal elements in the pancreatic cancer microenvironment. The evidence also unveils multifaceted roles of cancer-associated fibroblasts (CAFs) in manipulating pancreatic cancer progression, immunity, and chemotherapeutic response. Current state-of-the-art technologies, including single-cell transcriptomics and multiplexed tissue imaging techniques, have provided a more profound knowledge of CAF heterogeneity in real-world specimens from pancreatic cancer patients, as well as in genetically engineered mouse models. In this review, we describe recent advances in the understanding of the molecular pathology bases of pancreatic cancer desmoplastic stroma at multilayered levels of heterogeneity, namely, (1) variations in cellular and non-cellular members, including CAF subtypes and extracellular matrix (ECM) proteins; (2) geographical heterogeneity in relation to cell–cell interactions and signaling pathways at niche levels and spatial heterogeneity at locoregional levels or organ levels; and (3) intertumoral stromal heterogeneity at individual levels. This review further discusses the clinicopathological significance of desmoplastic stroma and the potential opportunities for stroma-targeted therapies against this lethal malignancy.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple SummaryCancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Abstract 2131: Curating cartography: Enabling the harmonisation, visualisation, and reuse of diverse multiplexed imaging data through the Human Tumor Atlas Network Data Coordinating Center

Abstract The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI) Cancer Moonshot Initiative to generate three-dimensional molecular and spatial atlases of diverse human tumors and characterize crucial transitions in cancer progression and treatment. A series of manuscripts describing an extensive array of genomics, transcriptomics, proteomics, and imaging datasets are emerging but careful curation is required to maximize the data’s utility. HTAN centers have generated data from over 30 assay types including more than a dozen imaging methods (e.g., fluorescence microscopy, metal-tagged imaging, digital pathology, spatial transcriptomics, and electron microscopy). The HTAN Data Coordinating Center (DCC) develops infrastructure and tools to ingest, curate, explore, and share these data in a findable, accessible, interoperable, and reusable (FAIR) manner. As of November 2021 the DCC had ingested over 150 TB of data, including nearly 150,000 imaging data files. HTAN metadata schemas were developed through a community-driven Request for Comments process, including schemas for raw, processed and QC-checked imaging data, segmentation masks and feature arrays. This work led to a set of Minimum Information for Highly Multiplexed Tissue Imaging (MITI) guidelines, which are complemented by detailed metadata on participants and biospecimens. The Schematic python package is used to provide a user-friendly interface for defining data-model schemas, generate (meta)data submission spreadsheets, and asset-store interfaces on various cloud platforms. Across assay types, the HTAN schema currently encompasses over 700 attributes from 35 components. The DCC manages the HTAN Data Portal, which provides community access to the atlases and enables filtering of released atlas data based on metadata fields, and pointers to data availability. To enable visualization and exploration of imaging data directly from the portal, centers can submit Minerva stories, which provide guided storytelling of multiplexed tissue images. We provide methods to automatically generate default stories with visually appropriate overlays of sequential four-channel groups. We developed Miniature to generate unsupervised and interpretable multiplexed image thumbnails to provide rapid contextual information when browsing image collections. Dimensionality reduction is used to reduce a multiplexed image pyramid to three dimensions. Pixels are recolored according to their coordinates in low-dimensional space, where Pixels with similar marker expression are assigned similar colours. Public data sharing requires careful consideration of data egress costs and de-identification of images and their metadata. The DCC is working closely with NCI Cancer Research Data Commons to ensure long term-legacy and reuse of HTAN data. Citation Format: Adam J. Taylor, Milen Nikolov, Ino de Brujin, Jeremy Muhlich, Mialy De Felice, Artem Sokolov, Denis Schapiro, Peter Sorger, Julie Bletz, Nikolaus Schultz, Vésteinn Thorsson, James Eddy, Ethan Cerami. Curating cartography: Enabling the harmonisation, visualisation, and reuse of diverse multiplexed imaging data through the Human Tumor Atlas Network Data Coordinating Center [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 2131.

Role of Tertiary Lymphoid Structures and Immunotherapy in Head & Neck Squamous Cell Carcinoma

<h3>Purpose/Objective(s)</h3> The development of novel immunotherapies has provided an avenue to treat many malignancies, including Head and Neck Squamous Cell Carcinoma (HNSCC). Although much research has been done on identifying T-cell mediated immune changes after nivolumab treatment in HNSCC, characterization of humoral immune system changes remains to be fully understood. Furthermore, tertiary lymphoid structures (TLSs) with tumor-inhibiting functions such as antibody generation and clonal expansion have been shown to play a role in predicting positive patient outcomes in several cancers. The relationship that TLSs and immunotherapy might have in patients with HNSCC remains to be elucidated. <h3>Materials/Methods</h3> 11 HNSCC patients with tumors in the non-oropharynx were treated with neoadjuvant nivolumab 240mg on days 1 and 15 with or without tadalafil. Surgery was completed on day 31. Samples of tumors were taken before the commencement of immunotherapy and after the completion of immunotherapy. Samples after the completion of immunotherapy were stained with Hematoxylin & Eosin (H&E) staining, imaged with a light microscope, and uploaded to Leica's Aperio Imaging software. An analysis of H&E stained images was performed by classifying lymphocyte populations using a previously published organization scale of TLSs into no organization (grade 1), some organization (grade 2), more organization (grade 3), and most organization (grade 4). <h3>Results</h3> H&E staining showed three patients to not have any lymphocyte organization in their tumor samples after immunotherapy (grade 1), four patients having some lymphocyte organization (grade 2), two patients having more organization (grade 3), and one patient having organization of lymphocytes in a way that TLSs develop (grade 4). <h3>Conclusion</h3> These results warrant further characterization of TLSs in immunotherapy-treated HNSCC patients. Pre-immunotherapy and post-immunotherapy samples will be sent for CODEX multiplexed tissue imaging analysis for TLS markers: CD20 for B-cells, CD4 for helper T-cells, CD8 for cytotoxic T-cells, PNAd for high endothelial venues (HEVs), CD21 for follicular dendritic cells, and CD208 for mature dendritic cells.

MITI minimum information guidelines for highly multiplexed tissue images.

The imminent release of atlases combining highly multiplexed tissue imaging with single cell sequencing and other omics data from human tissues and tumors creates an urgent need for data and metadata standards compliant with emerging and traditional approaches to histology. We describe the development of a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that draws on best practices from genomics and microscopy of cultured cells and model organisms.

Temporal and spatial topography of cell proliferation in cancer.

Proliferation is a fundamental trait of cancer cells but its properties and spatial organization in tumors are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumor cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as “cell cycle coherence”. By contrast, the cell cycles of other tumor cell populations are skewed toward specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumor behavior. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images

Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single‐cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning‐based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning‐based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types.

CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

BackgroundAlgorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher’s need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills.ResultsCellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations.ConclusionCellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data.

Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 109 or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.

Rhesus Macaque CODEX Multiplexed Immunohistochemistry Panel for Studying Immune Responses During Ebola Infection.

Non-human primate (NHP) animal models are an integral part of the drug research and development process. For some biothreat pathogens, animal model challenge studies may offer the only possibility to evaluate medical countermeasure efficacy. A thorough understanding of host immune responses in such NHP models is therefore vital. However, applying antibody-based immune characterization techniques to NHP models requires extensive reagent development for species compatibility. In the case of studies involving high consequence pathogens, further optimization for use of inactivated samples may be required. Here, we describe the first optimized CO-Detection by indEXing (CODEX) multiplexed tissue imaging antibody panel for deep profiling of spatially resolved single-cell immune responses in rhesus macaques. This 21-marker panel is composed of a set of 18 antibodies that stratify major immune cell types along with a set three Ebola virus (EBOV)-specific antibodies. We validated these two sets of markers using immunohistochemistry and CODEX in fully inactivated Formalin-Fixed Paraffin-Embedded (FFPE) tissues from mock and EBOV challenged macaques respectively and provide an efficient framework for orthogonal validation of multiple antibody clones using CODEX multiplexed tissue imaging. We also provide the antibody clones and oligonucleotide tag sequences as a valuable resource for other researchers to recreate this reagent set for future studies of tissue immune responses to EBOV infection and other diseases.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCL) are rare but aggressive cancers without effective treatments. While a subset of patients derive benefit from PD-1 blockade, there is a critically unmet need for predictive biomarkers of response. Herein, we perform CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced CTCL patients enrolled in a pembrolizumab clinical trial (NCT02243579). We find no differences in the frequencies of immune or tumor cells between responders and non-responders. Instead, we identify topographical differences between effector PD-1+ CD4+ T cells, tumor cells, and immunosuppressive Tregs, from which we derive a spatial biomarker, termed the SpatialScore, that correlates strongly with pembrolizumab response in CTCL. The SpatialScore coincides with differences in the functional immune state of the tumor microenvironment, T cell function, and tumor cell-specific chemokine recruitment and is validated using a simplified, clinically accessible tissue imaging platform. Collectively, these results provide a paradigm for investigating the spatial balance of effector and suppressive T cell activity and broadly leveraging this biomarker approach to inform the clinical use of immunotherapies.

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data.

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry

SummaryDeciphering the spatial composition of cells in tissues is essential for detailed understanding of biological processes in health and disease. Recent technological advances enabled the assessment of the enormous complexity of tissue-derived parameters by highly multiplexed tissue imaging (HMTI), but elaborate machinery and data analyses are required. This severely limits broad applicability of HMTI. Here we demonstrate for the first time the application of ChipCytometry technology, which has unique features for widespread use, on formalin-fixed paraffin-embedded samples, the most commonly used storage technique of clinically relevant patient specimens worldwide. The excellent staining quality permits workflows for automated quantification of signal intensities, which we further optimized to compensate signal spillover from neighboring cells. In combination with the high number of validated markers, the reported platform can be used from unbiased analyses of tissue composition to detection of phenotypically complex rare cells, and can be easily implemented in both routine research and clinical pathology.

Multimodal platform for assessing drug distribution and response in clinical trials.

Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.

Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

SummaryThe emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features.

Multiplex Tissue Imaging Harmonization: A Multicenter Experience from CIMAC-CIDC Immuno-Oncology Biomarkers Network.

The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Commons (CIMAC-CIDC) network supported by the NCI Cancer Moonshot initiative was established to provide correlative analyses for clinical trials in cancer immunotherapy, using state-of-the-art technology. Fundamental to this initiative is implementation of multiplex IHC assays to define the composition and distribution of immune infiltrates within tumors in the context of their potential role as biomarkers. A critical unanswered question involves the relative fidelity of such assays to reliably quantify tumor-associated immune cells across different platforms. Three CIMAC sites compared across their laboratories: (i) image analysis algorithms, (ii) image acquisition platforms, (iii) multiplex staining protocols. Two distinct high-dimensional approaches were employed: multiplexed IHC consecutive staining on single slide (MICSSS) and multiplexed immunofluorescence (mIF). To eliminate variables potentially impacting assay performance, we completed a multistep harmonization process, first comparing assay performance using independent protocols followed by the integration of laboratory-specific protocols and finally, validating this harmonized approach in an independent set of tissues. Data generated at the final validation step showed an intersite Spearman correlation coefficient (r) of ≥0.85 for each marker within and across tissue types, with an overall low average coefficient of variation ≤0.1. Our results support interchangeability of protocols and platforms to deliver robust, and comparable data using similar tissue specimens and confirm that CIMAC-CIDC analyses may therefore be used with confidence for statistical associations with clinical outcomes largely independent of site, antibody selection, protocol, and platform across different sites.

CODEX multiplexed tissue imaging with DNA-conjugated antibodies.

Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity.

Abstract 1914: Orally available oncolytic reovirus, RC402, effectively promotes anti-cancer immunity and synergizes with immune checkpoint blockade in colon cancer

Abstract Introduction: Reoviruses are replication-competent and selectively infect and destroy cancer cells. Although the most common route of oncolytic virotherapy is intratumoral or intravenous administration, these routes are often not clinically feasible because of tumor inaccessibility, tumor multiplicity, and systemic toxicities. Here, we developed an orally available oncolytic reovirus, RC402, to elicit potent anti-tumor immune responses without the above mentioned limitations and thereby maximize the efficacy of PD-1 immune checkpoint blockade in colon cancer. Method: MC38 or CT26 colon cancer-bearing immunocompetent mice were treated with RC402 and/or anti-PD-1 antibody. Tumor growth was monitored after treatment and comprehensively analyzed by flow cytometry and/or multiplex tissue imaging. Results: RC402 treatment effectively delayed tumor growth regardless of the route of administration (per os as well as intratumorally). In particular, orally administered RC402 was well tolerated without gross toxicity and interacted with intestinal immune cells, eliciting adaptive T cell responses within intestinal lymphoid organs such as Peyer's patch. Most of the RC402 was cleared in the gastrointestinal tract within 10 days of the first treatment, and it was not detectable in the blood stream or major organs after oral administration. However, RC402 selectively induced strong and durable anti-tumor immunity in distant tumor tissues. Oral RC402 monotherapy markedly increased CD8+ cytotoxic T cells and decreased CD4+CD25+Foxp3+ regulatory T cells in distant colon cancers, while there were no significant changes in tumor-associated myeloid cells. Finally, oral RC402 treatment cooperated with anti-PD-1 blockade to further suppress colon cancer growth and augment anti-tumor immunity within the tumor microenvironment, leading to complete regression of tumors and survival benefit of tumor-bearing mice. Conclusion: Our study demonstrates that orally available oncolytic reovirus, RC402, could induce potent anti-cancer immune responses and effectively suppress colon cancer progression in combination with anti-PD-1 blockade. Citation Format: Chan Kim, Hong Jae Chon, Hye Jin Lee, Won Suk Lee, Hannah Yang, Jeong Hun Kim, So Jung Kong, Yu Seong Lee, Seung Joon Lee, Enkhtaivan Gansukh, Ki-Hoon Song, Yeon-Sook Lee, Beodeul Kang. Orally available oncolytic reovirus, RC402, effectively promotes anti-cancer immunity and synergizes with immune checkpoint blockade in colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1914.