scholarly journals 668 A toolkit for the quantitative analysis of the spatial distribution of cells of the tumor immune microenvironment

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A705-A705
Author(s):  
Anna Trigos ◽  
Tianpei Yang ◽  
Yuzhou Feng ◽  
Volkan Ozcoban ◽  
Maria Doyle ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Analysis ◽  
Spatial Distribution ◽  
Data Analysis ◽  
Spatial Data ◽  
R Package ◽  
Immune Microenvironment ◽  
Spatial Image ◽  
Tumor Immune Microenvironment ◽  
Cell Phenotypes

BackgroundSpatial technologies that query the location of cells in tissues such as multiplex immunohistochemistry and spatial transcriptomics are gaining popularity and are likely to become commonplace. The resulting data often includes the X, Y coordinates of millions of cells, cell phenotypes and marker or gene expression levels. In cancer, the spatial location of lymphocytes has been linked to prognosis and response to immunotherapy. While these advances have been exciting for the field, the methods currently being used are still coarse, making us severely underpowered in our ability to extract quantifiable information. Appropriate quantitative tools are desperately needed to refine and uncover novel biologically and clinically meaningful insights from the spatial distribution of cells of the tumor immune microenvironment.MethodsWe compiled over 60 prostate cancer and melanoma FFPE tumor sections and performed Opal multiplex immunohistochemistry for a diversity of T-cell and other immune markers, including CD3, CD4, CD8, FOXP3 and PDL1, as well as a prostate cancer (AMACR) or melanoma (SOX10) marker and DAPI. Following spectral imaging on the Vectra Polaris, we performed cell and tissue segmentation and phenotyping with the inForm or HALO image analysis software. The detected X, Y coordinates of cells and marker intensities were used for subsequent method development.ResultsWe developed SPIAT (Spatial Image Analysis of Tissues)1, an R package with a suite of data processing, quality control, visualization, data handling and data analysis tools for spatial data. SPIAT includes our novel algorithms for the identification of cell clusters, tumor margins and cell gradients, the calculation of neighborhood proportions and algorithms for the prediction of cell phenotypes. By interfacing with packages used in ecology, geographic data analysis and spatial statistics, we have begun to robustly address fundamental questions in the analysis of cell spatial data, such as metrics to measure mixing between cell types, the identification of tumor borders and statistical approaches to compare samples.ConclusionsSPIAT is compatible with multiplex immunohistochemistry, spatial transcriptomics and data generated from other spatial platforms, and continues to be actively developed. We expect SPIAT to become a user-friendly and speedy go-to package for the spatial analysis of cells in tissues, as well as promote the use of quantitative metrics in the spatial analysis of the tumor immune microenvironment.ReferencesTianpei Yang, Volkan Ozcoban, Anu Pasam, Nikolce Kocovski, Angela Pizzolla, Yu-Kuan Huang, Greg Bass, Simon P. Keam, Paul J. Neeson, Shahneen K. Sandhu, David L. Goode, Anna S. Trigos. SPIAT: An R package for the Spatial Image Analysis of Cells in Tissues. BioRxiv doi: https://doi.org/10.1101/2020.05.28.122614

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

PD19-11 TUMOR IMMUNE MICROENVIRONMENT ALTERATIONS IN PENILE SQUAMOUS CELL CARCINOMA USING MULTIPLEX IMMUNOFLUORESCENCE AND IMAGE ANALYSIS APPROACHES

2020 ◽  
Vol 203 ◽  
pp. e386
Author(s):  
MPH Jad Chahoud ◽  
Frederico Netto ◽  
Rossana Lazcano Segura ◽  
Edward Roger Parrra Cuentes ◽  
Xin Lu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Image Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Penile Squamous Cell Carcinoma

scholarly journals Spatial Distribution and Modelling of the Total Fertility Rate in Turkey Using Spatial Data Analysis Techniques

2018 ◽  
pp. 27-45 ◽  
Author(s):  
Olgu AYDIN ◽  
Pınar ASLANTAŞ BOSTAN ◽  
Ertuğrul Murat ÖZGÜR
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Spatial Data ◽  
Total Fertility Rate ◽  
Fertility Rate ◽  
Spatial Data Analysis ◽  
Analysis Techniques

scholarly journals Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Edwin Roger Parra
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
Tumor Microenvironment ◽  
Individual Cell ◽  
Clinical Information ◽  
Cell Populations ◽  
Cellular Distribution ◽  
Main Challenge ◽  
Cell Phenotypes

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.

scholarly journals Characterization of the m6A-Associated Tumor Immune Microenvironment in Prostate Cancer to Aid Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Zezhen Liu ◽  
Jiehui Zhong ◽  
Jie Zeng ◽  
Xiaolu Duan ◽  
Jianming Lu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Poor Prognosis ◽  
Response Rate ◽  
Checkpoint Inhibitors ◽  
Cell Infiltration ◽  
Intratumor Heterogeneity ◽  
Immune Microenvironment ◽  
Th2 Cell ◽  
Tumor Immune Microenvironment

The aim of this study was to elucidate the correlation between m6A modification and the tumor immune microenvironment (TIME) in prostate cancer (PCa) and to identify the m6A regulation patterns suitable for immune checkpoint inhibitors (ICIs) therapy. We evaluated the m6A regulation patterns of PCa based on 24 m6A regulators and correlated these modification patterns with TIME characteristics. Three distinct m6A regulation patterns were determined in PCa. The m6A regulators cluster with the best prognosis had significantly increased METTL14 and ZC3H13 expression and was characterized by low mutation rate, tumor heterogeneity, and neoantigens. The m6A regulators cluster with a poor prognosis had markedly high KIAA1429 and HNRNPA2B1 expression and was characterized by high intratumor heterogeneity and Th2 cell infiltration, while low Th17 cell infiltration and Macrophages M1/M2. The m6Ascore was constructed to quantify the m6A modification pattern of individual PCa patients based on m6A-associated genes. We found that the low-m6Ascore group with poor prognosis had a higher immunotherapeutic response rate than the high-m6Ascore group. The low-m6Ascore group was more likely to benefit from ICIs therapy. This study was determined that immunotherapy is more effective in low-m6Ascore PCa patients with poor prognosis.

scholarly journals 420 PROSTVAC in combination with nivolumab enhanced immune cell infiltration in prostate cancer

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A450-A450
Author(s):  
Shania Bailey ◽  
Wiem Lassoued ◽  
Antonios Papanicolau-Sengos ◽  
Jennifer Marte ◽  
Nikki Williams ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
T Cell ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Microenvironment ◽  
Invasive Margin ◽  
Control Tumor Growth ◽  
Tumor Immune Microenvironment ◽  
Control Tumor

BackgroundProstate cancer (PC) is the most common non-cutaneous diagnosed cancer among men in USA.1 Although clinical outcomes are favorable for patients with localized disease, 20–30% of patients will develop metastatic prostate cancer (mPC) and have poor prognosis. Immunotherapy, as a single agent, provides benefit to a small subset of PC patients, which is thought to be partially due to its known cold tumor immune microenvironment (TIME). Combination studies are needed to enhance benefit.2 Prostvac is a therapeutic cancer vaccine engineered to activate an immune response against prostate-specific Antigen (PSA).3 Prostvac alone could induce systemic immune response by increasing immune-cell infiltrates in and around the tumor.4 In this study, we are exploring the effect of Prostvac in combination with nivolumab in TIME in prostate cancer.MethodsWe treated locally advanced prostate cancer patients (n=6) undergoing radical prostatectomy (RP) with neoadjuvant Prostvac in combination with nivolumab, an immune checkpoint PD-1 inhibitor. Dynamic changes in TIME before and after treatment were studied using multiplex immunofluorescence (Opal Method). Formalin fixed paraffin-embedded sections from matched pre-treated prostate biopsies and post-treated RP samples were stained with a validated T cell panel (DAPI, CD4, CD8, FOXP3, Ki67, Pan CK and PD-L1). To analyze the data, TIME was segmented into 3 compartments: intratumoral, invasive margin and benign.ResultsCombination immunotherapy significantly increased CD4+ T cell density in the invasive margin (mean 211.5 cells/mm2 vs 592.2 cells/mm2, p<0.05), with similar trend in the intratumoral and the benign compartments. CD8+ T cell density increased after treatment in the invasive margin (mean 47.25 cells/mm2 vs 157cells/mm2) and the benign compartment. 5/6 and 4/6 patients showed more than 2-fold increase of CD4 and CD8 T cells in the TIME, respectively, in at least one of the three compartments. Increased proliferative indices in CD4+ and CD8+ T cells were also seen after treatment. Tregs were present in low frequencies in TIME (maximum of 12 cells/mm2) with no significant changes. Moreover, a significant drop in tumor cell Ki67 after treatment (mean 252.8 cells/mm2 vs 100.5 cells/332, p<0.05) suggests that the combination may control tumor growth.ConclusionsThe combination of Neoadjuvant Prostvac and nivolumab was associated with increased immune cell infiltration in a cohort of early prostate cancer patients. A broader examination of the TIME and the role immune cells undertake to control tumor growth is on-going.Trial RegistrationNCT02933255ReferencesSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin (Internet) 2020;70:7–3Zhao SG, Lehrer J, Chang SL, et al. The immune landscape of prostate cancer and nomination of PD-L2 as a potential therapeutic target. J Natl Cancer Inst 2018;111:301–10.Madan RA, Arlen PM, Mohebtash M, et al. Prostvac-VF: a vectorbased vaccine targeting PSA in prostate cancer. Expert Opin Investig Drugs 2009;18:1001–11Abdul Sater H, Marté JL, Donahue RN, et al. Neoadjuvant PROSTVAC prior to radical prostatectomy enhances T-cell infiltration into the tumor immune microenvironment in men with prostate cancer. J Immunother Cancer 2020;8(1):655–64Ethics ApprovalThis study was performed in compliance with ethical standard and was approved by the NIH IRB, 17C-0007. All patients participating in this study gave an informed consent before taking part.

scholarly journals Single-cell tumor-immune microenvironment of BRCA1/2 mutated high-grade serous ovarian cancer

2021 ◽  
Author(s):  
Inga-Maria Launonen ◽  
Nuppu Lyytikäinen ◽  
Julia Casado ◽  
Ella Anttila ◽  
Angéla Szabó ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Spatial Data ◽  
Single Cells ◽  
High Grade ◽  
Spatial Interactions ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Cellular Phenotypes

Abstract The majority of high-grade serous ovarian cancers (HGSCs) are deficient in homologous recombination (HR) DNA repair, most commonly due to mutations or hypermethylation of the BRCA1/2 genes. We aimed to discover how BRCA1/2 mutations shape the cellular phenotypes and spatial interactions of the tumor microenvironment. Using a highly multiplex immunofluorescence and image analysis on 112 tumor cores we generated single-cell spatial data for 21 markers in 124,623 single cells from 31 tumors with BRCA1/2 mutation (BRCA1/2mut), and 13 tumors without any alterations in HR genes (HRwt). We identified a phenotypically distinct tumor microenvironment in the BRCA1/2mut tumors with evidence of increased immunosurveillance. Importantly, we found an opposing prognostic role of a proliferative tumor-cell phenotypic subpopulation in the HR-genotypes, which associated with enhanced spatial interactions in the tumor-immune cellular communities. The single-cell spatial landscapes indicate distinct patterns of spatial immunosurveillance with the premise to improve immunotherapeutic strategies and patient stratification in HGSC.

Qualitative Evaluation of Pilling

2013 ◽  
Vol 740 ◽  
pp. 649-654
Author(s):  
Lenka Techniková ◽  
Maroš Tunák ◽  
Jiří Janáček
Keyword(s):  
Image Analysis ◽  
Data Analysis ◽  
3D Reconstruction ◽  
Spatial Data ◽  
Qualitative Evaluation ◽  
Spatial Data Analysis ◽  
3D Image ◽  
Gradient Fields ◽  
Fabric Surface ◽  
Function Density

This paper presents 3D reconstruction of fabric surface with the pills, detection of the pills and qualitative evaluation of pilling with the help of spatial data analysis. 3D reconstruction of fabric surface is performed with the help of gradient fields method. The surface pills are extracted from 3D image by techniques of image analysis. Consequently, the tools of spatial data analysis as K function, density and test are used for experiment of qualitative evaluation of pills distribution in the space.

scholarly journals A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Zhi-Bin Ke ◽  
Qi You ◽  
Jiang-Bo Sun ◽  
Jun-Ming Zhu ◽  
Xiao-Dong Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Biochemical Recurrence ◽  
Molecular Clusters ◽  
Molecular Signature ◽  
Recurrence Free Survival ◽  
Immune Microenvironment ◽  
Free Survival ◽  
Training Cohort ◽  
Tumor Immune Microenvironment

Objective: To identify ferroptosis-related molecular clusters, and to develop and validate a ferroptosis-based molecular signature for predicting biochemical recurrence-free survival (BCRFS) and tumor immune microenvironment of prostate cancer (PCa).Materials and Methods: The clinical data and transcriptome data of PCa were downloaded from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained from FerrDb database. We performed consensus clustering analysis to identify ferroptosis-related molecular subtypes for PCa. Univariate and multivariate Cox regression analysis were used to establish a ferroptosis-based signature for predicting BCRFS. Internal verification, external verification and subgroup survival analysis were then successfully performed.Results: There was a total of 40 differentially expressed FRGs in PCa. We then identified three ferroptosis-related molecular clusters of PCa, which have significantly different immune infiltrating cells, tumor immune microenvironment and PD-L1 expression level. More importantly, a novel ferroptosis-based signature for predicting BCRFS of PCa based on four FRGs (including ASNS, GPT2, NFE2L2, RRM2) was developed. Internal and external verifications were then successfully performed. Patients with high-risk score were associated with significant poor BCRFS compared with those with low-risk score in training cohort, testing cohort and validating cohort, respectively. The area under time-dependent Receiver Operating Characteristic (ROC) curve were 0.755, 0.705 and 0.726 in training cohort, testing cohort and validating cohort, respectively, indicating the great performance of this signature. Independent prognostic analysis indicated that this signature was an independent predictor for BCRFS of PCa. Subgroup analysis revealed that this signature was particularly suitable for younger or stage T III-IV or stage N0 or cluster 1-2 PCa patients. Patients with high-risk score have significantly different tumor immune microenvironment in comparison with those with low-risk score. The results of qRT-PCR successfully verified the mRNA expression levels of ASNS, GPT2, RRM2 and NFE2L2 in DU-145 and RWPE-1 cells while the results of IHC staining exactly verified the relative protein expression levels of ASNS, GPT2, RRM2 and NFE2L2 between PCa and BPH tissues.Conclusions: This study successfully identified three ferroptosis-related molecular clusters. Besides, we developed and validated a novel ferroptosis-based molecular signature, which performed well in predicting BCRFS and tumor immune microenvironment of PCa.

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