https://openworks.mdanderson.org/sumexp22/1078/thumbnail.jp

Bhat, Krishna

Calderon, Carlos

Gupta, Pravesh

Jahedi, Afrooz

Kannan, Kasthuri

Kumar, Gayatri

Patel, Sneh

English

OpenWorks @ MD Anderson

Introduction ResultsMethodologyTitle of the Poster Presentation Goes HereAuthors of the Poster Presentation Goes HereThe University of Texas MD Anderson Cancer Center ReferencesAcknowledgementsConclusionsFig. 1 Percentage of different cell populations in the primary and recurrent TME of matched IDH-WT GBM patients. (a) Visual representation of the distribution of CD4 presenting CD68 macrophages expressing Gal9 in the primary and recurrent TME. The orange arrow depicts a triple positive cell with expression of CD4, CD68 and Gal9. The blue arrow illustrates a CD4 T-cell. The yellow arrow points to a CD68 macrophage that is not expressing Gal9. (b) Distribution of CD45+ immune cells (p = 0.92, paired t-test). (c) Distribution of CD3e+ T-cells expressing cytotoxic marker granzyme B (p = 0.39, paired t-test). (d) Distribution of CD68+ macrophages (p = 0.54, paired t-test). (e) Distribution of CD4 presenting CD68+ macrophages (p = 0.02, paired t-test). (f) Percentage of CD68+ macrophages expressing Gal9 in the primary and recurrent TME of matched IDH-WT GBM patients (p = 0.20).Fig. 2 Spatial analysis data from a primary tumor to illustrate the results of the training model to quantify enriched and depleted cell interactions. 2a-d Visual representation of cell-cell interactions. (a) Two CD4 presenting CD68 macrophages expressing Gal9 are interact with CD4 T-cell. (b) CD8 T-cell interacts with a CD4 T-cell. (c) CD68 macrophage interacts with Olig2 (nonimmune marker used to identify proliferating tumor cells). (d) CD8 T-cell interacts with Olig2. (e) Pairwise adjacency plot from region 1 of P5 for enriched interactions between clusters identified from the unsupervised analysis displaying log10 (adjusted p-value). Red represents interactions that are highly enriched in this region. (f) Pairwise adjacency plot from region 1 of P10 for depleted interactions identified from the unsupervised analysis displayed log10 (adjusted p-value). Red represents interactions that are greatly depleted in this region.Each antibody was partially reduced and conjugated with a DNA oligonucleotide to recover the oligonucleotide-antibody conjugate. Subsequently, the tissue was prepared for antigen retrieval, stained with the oligonucleotide-antibody conjugate, hybridized with the corresponding fluorescent oligonucleotide and imaged5. After treating the tissue with all the 42 markers in a multicycle reaction, images were processed for data analysis.IDH-WT primary GBM patients (n=5)IDH-WT recurrent GBM patients (n=5)MatchedTumor resected from 5 matched IDH-WT GBM patients were preserved using the standard protocol for formalin-fixed paraffin embedding. Sections of the embedded tissue were processed using the CODEX method to obtain the multiplex images with 42 markers (only a subset of 10 were used for this project).Glioblastoma multiforme (GBM) is the most malignant and prevalent form of primary astrocytomas. It is the most lethal form of primary brain tumors in adult patients with an average life expectancy of approximately 14 to 15 months from the diagnosis1. The disease progression and response to chemotherapy and radiation are deeply associated with the tumor microenvironment (TME)2.Tumor-infiltrating lymphocytes (TILs), such as T-cells and NK T-cells, and macrophages are two populations that play a central role in fighting the disease in the TME of GBM3,4. The spatial context of these cells in GBM microenvironment as well as differences in the immune environment between newly diagnosed and recurrent GBM are not well studied. An unbiased multiplex imaging approach using the co-detection by indexing (CODEX) technology was applied to investigate the distribution of T-cells and macrophages in primary and recurrent GBM tumors.After cell segmentation, quality control and filtering was performed on each cell using basic QC metrics such as total sum of the signal, DAPI intensity, cell size, and coefficient of variation. Then, the intensity data was normalized (arcsin transformation) and scaled to account for batch effects.  For thresholding, the scaled intensity data was fitted to a two component gaussian distribution. The threshold was calculated by finding the mean of the lower curve and the threshold was set to two standard deviations from that mean. Cell locations K-nearest neighbors Building neighbor graph Counting interactions Computing enrichmentsTo conduct spatial analyses and describe cell-cell interactions, unsupervised clustering and dimensionality reduction was applied across regions in each patient. A graph of cell-cell interactions using XY coordinates was generated using k = 5 nearest neighbors' parameter. Pairwise adjacency was computed and evaluated statistically using the hypergeometric test to determine if certain clusters or cell-type interactions were enriched or depleted between the two groups.Schematic representation of the complexity of the GBM TME.Spatial Context of Tumor Immune Microenvironment of Matched Primary and Recurrent GlioblastomasSneh Patel1, Pravesh Gupta2, Afrooz Jahedi2, Gayatri Kumar2, Carlos Calderon3, Aaron Mayer4, Alex Trevino4, Jason Huse2, Kasthuri Kannan2, Krishna Bhat2Department of Neuroscience and Behavioral Biology at Emory University1, Georgia, USA, Department of Translational Molecular Pathology at The University of Texas MD Anderson Cancer Center2, Texas, USA, School of Medicine at University of Puerto Rico3, EnableMedicine at Menlo Park4, California, USA.1. Ohka F, Natsume A, Wakabayashi T. Current trends in targeted therapies for glioblastoma multiforme. Neurol Res Int. 2012;2012:878425.2. Quail DF, Joyce JA. The Microenvironmental Landscape of Brain Tumors. Cancer Cell. 2017 Mar 13;31(3):326-341. doi: 10.1016/j.ccell.2017.02.009. PMID: 28292436; PMCID: PMC5424263.3. Buonfiglioli, A., Hambardzumyan, D. Macrophages and microglia: the cerberus of glioblastoma. acta neuropathol commun 9, 54 (2021). https://doi.org/10.1186/s40478-021-01156-z.4. Najem H. et al. Central nervous system immune interactome is a function of cancer lineage, tumor microenvironment, and STAT3 expression. JCI Insight. 2022 May 9;7(9):e157612. doi: 10.1172/jci.insight.157612. PMID: 35316217; PMCID: PMC9090258.5. Black, S., Phillips, D., Hickey, J.W. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 16, 3802–3835 (2021). https://doi.org/10.1038/s41596-021-00556-8.Research Objectives1. Define different cell-types in the GBM TME and investigate the spatial immune context and interactions between different cell populations.2. Identify differences in the distribution and spatial context of immune cells between primary and recurrent GBM tumors.3. Identify differences in the distribution and spatial context of immune cells between the necrotic core of the tumor, infiltrating edge, and the healthy tissue in the brain.Antibody PanelCD45 (pan immune marker)T-cells Macrophages Monocytes FunctionalCD3 CD68 CD14 GranzymeB (Gr.B)CD4 CD163 CD16 Galectin-9 (Gal9)CD8v There is an enormous amount of heterogeneity between the distribution of T-cells and macrophages across patients and the tumor subtype.v CD4 expression of myeloid lineage cells was detected in both primary and recurrent tumors. There was a significant increase in the number of the CD68 macrophages expressing CD4 in the recurrent TME compared to their matched primary counterparts.v Gal9 was expressed in over 60% of all macrophages in the TME and the percentage of CD68 macrophages expressing Gal9 increased in the recurrent setting for a large majority of patients.v PD1 and PDL1 were mainly undetectable in all patients in this cohort. Gal9 and its binding partner TIM3 appear to be the dominant checkpoint marker in the GBM TME.% (of TME cells)CD45+ Gz.B+CD3+ T-cell CD68+ MAC CD4+CD68+ MAC% (of CD68 MAC)Gal9+CD68+ MACDAPI CD45 DAPI CD45CD68DAPI CD45CD68 CD4DAPI CD45CD3 Gz.BDAPI CD45CD68 Gal9b. c. d. e. f.CD68 CD4 Gal9PrimaryRecurrenta.p = 0.02Increased CD4+ CD68+ macrophages in the recurrent than primary TMETraining model to quantify enriched and depleted cell-cell interactions e. f.a.CD68 CD4Gal9 CD8 CD4Olig2 CD68 Olig2 CD8b.c. d.v Special thanks to Lisa Norberg for tissue embedding and sectioning for the CODEX slides.v Researchers at Enabled Medicine for their collaboration on this project with our labv Nancy Milan for administrative support throughout the project.v Dr. Bhat and Dr. Gupta for teaching and mentoring me throughout the summer.v Dr. Marites Melancon, Dr. Chandra Bartholomeusz, and Nancy Strange for organizing the MD Anderson Cancer Center Summer Experience and Summer Undergraduate Research Program (SURP).v Many thanks to MD Anderson Cancer Center for this enriching research opportunity.Study cohort and antibody panel for CODEXCODEX workflowData quality control and thresholdingSpatial AnalysesSignal intensity sumCell sizeDAPI signal intensityCoefficient of variationDensityDensityCD45 Intensity DistributionDensityScaled Intensity DataRed blood cellPericyteCancer associated fibroblastExtracellular matrixB-lymphocyteMacrophage Dendritic cell Myeloid-derived suppressor cellT-lymphocyte NK cell Cancer cell

Spatial Context of Tumor Immune Microenvironment of Matched Primary and Recurrent Glioblastomas

https://openworks.mdanderson.org/cgi/viewcontent.cgi?article=1078&context=sumexp22

Spatial Context of Tumor Immune Microenvironment of Matched Primary and Recurrent Glioblastomas

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