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Table of contents
These populations demonstrated opposite association trends when patients were stratified by clinical outcomes. Activated NK cells were more frequent in patients achieving pathological complete response while MDSC-like cells were more frequent in those with residual disease Figures 1D-1E.
We demonstrated the feasibility of a complete pipeline for deep phenotyping of cryopreserved PBMCs in cancer patients. Our approach identified rare cell subsets using an unbiased analysis tool, linking specific populations to opposite clinical outcomes. High dimensional immune monitoring is feasible and should be applied to study the immune system of cancer patients at large. Guider; the Eveleigh Family; George M. The tumor microenvironment TME is a network of complex interactions between the tumor and surrounding immune cells. Immunotherapies including immune checkpoint blockade have demonstrated therapeutic efficacy and durable responses for several tumor types, however most patients are nonresponsive or develop resistance to such immunotherapies.
To identify new predictive biomarkers to better stratify patients, it is essential to comprehensively characterize the immune cells within the TME at the molecular level. The RNAscope Multiplex Fluorescence assay was used again to visually confirm the differentially expressed genes between the T and B-cell-enriched regions with single cell resolution. To show a workflow combining RNAscope molecularly guided visualization and GeoMx DSP profiling is feasible, we confirmed that both assay protocols are compatible.
To test the full automated workflow, we compared the differentially expressed genes within the T cell and B cell-enriched ROI. We present a robust workflow that overcomes the historical limitations of ISH and IHC by combining high resolution imaging with high plex profiling.
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Since the introduction of checkpoint blockade inhibitors for cancer immunotherapy, numerous studies have sought to identify biomarkers predictive of patient response [1]. However, the relevance of antigen-driven responses to the tumor has yet to be investigated. To address this question, we examined T cell responses to MART-1, an antigen overexpressed in melanoma cells and a target for melanoma clinical trials that have had variable degrees of success.
No significant difference in the frequency of MART-1 expanded T cells was seen between healthy donors and melanoma patients with or without checkpoint therapy. These clusters were homologous to each other as well as the DMF4 T cell receptor TCR , one of the first clinically used genetically engineered T cells, with a known crystal structure [3,4].
Strategies for Predicting Response to Checkpoint Inhibitors. Curr Hematol Malig Rep. January ; 6 2 N Engl J Med. Immunotherapy is an actively growing arena in oncotherapeutics research and development. In this context, whether testing CAR-T cells, checkpoint inhibitors, or novel bispecific antibodies, the ultimate goal is to modulate the immune system to harness its tumor killing power.
T cells play a critical role in immune-regulated clearance of both liquid and solid tumors. Upon antigenic stimulation and activation, T cells rapidly expand, secrete cytokines, and differentiate to various functional subsets e. On the other hand, suppression of T cells i. Mouse models remain the most commonly used animal system for in vivo and in vitro cancer biology research and drug discovery. As researchers move forward to either better understand the role of T cells in cancer biology or to develop novel immunotherapies, there is a need for improved methods to quickly gather comprehensive data on T cell biology in this model.
To address this, we demonstrate a multiplexed, high-throughput, robust assay workflow capable of measuring multiple murine T cell biology endpoints quickly and reproducibly in a single-well format. Data were acquired on the iQue3 technology VBR configuration and analyzed on a plate-based level using the integrated ForeCyt software. Our assay workflow enabled simultaneous evaluation of viability, interrogation of helper and cytotoxic T cells for markers of activation and exhaustion, and identification of key memory subsets.
Data analysis and visualization of multiple endpoints was streamlined and performed in real time using the ForeCyt software. The assay was completed in four hours, including data analysis. Altogether, our workflow allows for easy phenotype and functional profiling of murine T cells in a single-well format while generating actionable results in a matter of hours. The tumor stroma consists of various components of the tumor microenvironment including tumor cells, fibroblasts, immune cells and the extracellular matrix.
Spatial organization and dynamic interplay of the complex cell-to-cell interactions play an important role in cellular phenotypes that can result in permanent alterations in cellular functions and response to oncology as well as immuno-oncology drug treatments.
While informative, conventional 2D tumor dissociated models do not maintain the stromal-stoichiometry of the tumor microenvironment, lacking vital support mechanisms necessary to accurately assess ex-vivo tumor cell viability and immune-cell activation after drug treatment. Here, we describe a functional quantitative multiplex immunofluorescence platform, 3D-plEX, to quantify drug-mediated changes in tumor immune microenvironment and tumor cell viability in intact 3D tumor organoids of patient tumor samples.
All patient tumor samples were obtained with patient consent and relevant IRB approval. Unpropagated live 3D tumoroids measuring micron in size were prepared from fresh patient tumors using a proprietary technology, pooled together to represent tumor heterogeneity and equally distributed to different treatment groups including nivolumab, ipilimumab, atezolizumab and urelumab singly or in different combinations. Cell media was collected for multiplex cytokine release assay.
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Tumoroids were fixed and embedded for multiplex immunofluorescence studies. Our results demonstrated that 3D-plEX platform using clinically relevant intact, uniformly sized tumoroids of fresh patient tumor tissue is highly versatile and reliable approach to quantify drug-mediated changes in cellular composition and spatial organization of the tumor immune microenvironment. Combination of this approach with multiplex cytokine release assay allows a comprehensive understanding of dynamic changes within the tumor tissue upon drug treatment. The impact of different immuno-oncology drug treatments ex vivo on TME will be discussed.
Application of this platform in the clinical studies may also allow determining the most effective combinatorial therapeutic strategies for individual patients. The key workhorse for studying these cellular interactions is via imaging; conventional imaging methods are limited by the number of channels and the spatial resolving capabilities.
MIBI can current attain single cell resolutions but has difficulties in resolving fine subcellular features. ExMIBI will be critical for the scientific community to obtain previously inaccessible insights into the fine details of tumor microenvironment and cancer-immune cell interactions, and promises to unravel fundamental insights in patient immunotherapy responses. Expansion microscopy ExM [3,4] is a technique that can physical expansion of biological specimens 4 to 10 folds through polymer chemistry, three-color fluorescent imaging of cellular features with an apparent lateral resolution of 70 nm in diffraction-limited confocal microscopes has been achieved.
However, the expanded gel is fragile and contains up to Various methods for sample charging removal are systematically tested for imaging a non-conductive gel in MIBI. This method can achieve 40 parameters. We will now be able to map previously inaccessible, finer details of the tumor microenvironment. The application of ExMIBI to dissect cellular immune interactions, in their spatial biological context, will allow a better understanding into the basic principles of our immune system in healthy and disease states.
This research has received advice and help from Prof. Michael Angelo, Prof. Sean Bendall and their research group.
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Harris Endowed Professorship to G. Multiplexed ion beam imaging of human breast tumors. Nature Medicine. Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. Nature Biotechnology. Manual pathology assessments of Immunohistochemistry IHC markers in immune oncology IO is often challenging and results can be highly variable[1,2]. Measuring biomarker presence in IO must take in to account both immune and tumor environments and provide contextual information on the interaction between tumor and immune biomarker landscapes [3].
Due to the complex nature surrounding tissue biomarker interpretation in IO, digital image analysis IA solutions have been developed that layer complex artificial intelligence AI and machine learning algorithms to obtain full tissue biomarker profiles necessary for drug development and patient stratification[4].
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Aggregation of all cellular and biomarker data generates tissue phenotypes that characterize the IO landscape of each tissue. Resulting image markups of cell detection and biomarker expression measured by image analysis were reviewed by an MD pathologist for acceptance. Tissues not meeting acceptance criteria were re-analyzed until acceptable to the reviewing pathologist. We demonstrate the synergistic value of layered image analysis algorithms which provide context to biomarker expression in NSCLC tissues.
Samples were grouped in to immune desert, excluded, and inflamed phenotypes based on total leukocyte and CD8 expression patterns in the tumor, stroma, and margin. PD-L1 expression was scored based on percentages of tumor and stromal expression, as well as digital representations of common PD-L1 scoring paradigms.
Additionally, samples were stratified by PD-L1 patterns of constitutive, induced, immune, or ignorant expression. Digital image analysis of IHC stained tissues creates comprehensive tissue biomarker profiles that are useful in assessment of tumor and immune interactions in IO drug development and patient stratification. Complex algorithms that utilize AI and machine learning can be overseen by MD pathologists to create clinically acceptable digital analysis solutions.
JAMA Oncol. Adv Anat Pathol. Implications of the tumor immune microenvironment for staging and therapeutics.
Mod Pathol. PD-L1 immunostaining scoring for non-small cell lung cancer based on immunosurveillance parameters. In colorectal cancer CRC there have been many recent advances in immune-related biomarkers that are both prognostic and predictive of response to immunotherapy.