Dataset Information

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

ABSTRACT: Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.

SUBMITTER: Gong C

PROVIDER: S-EPMC6330341 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

Gong Chang C Anders Robert A RA Zhu Qingfeng Q Taube Janis M JM Green Benjamin B Cheng Wenting W Bartelink Imke H IH Vicini Paolo P Wang Bing B Popel Aleksander S AS

Frontiers in oncology 20190107

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to ...[more]

PMID: 30666298

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Project description:BackgroundThe extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification.MethodsQuantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures.ResultsOn the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment.ConclusionsProtein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

Project description:Amyloids are heterogeneous assemblies of extremely stable fibrillar aggregates of proteins. Although biological activities of the amyloids are dependent on its conformation, quantitative evaluation of heterogeneity of amyloids has been difficult. Here we use disaggregation of the amyloids of tetramethylrhodamine-labeled Aβ (TMR-Aβ) to characterize its stability and heterogeneity. Disaggregation of TMR-Aβ amyloids, monitored by fluorescence recovery of TMR, was negligible in native buffer even at low nanomolar concentrations but the kinetics increased exponentially with addition of denaturants such as urea or GdnCl. However, dissolution of TMR-Aβ amyloids is different from what is expected in the case of thermodynamic solubility. For example, the fraction of soluble amyloids is found to be independent of total concentration of the peptide at all concentrations of the denaturants. Additionally, soluble fraction is dependent on growth conditions such as temperature, pH, and aging of the amyloids. Furthermore, amyloids undissolved in a certain concentration of the denaturant do not show any further dissolution after dilution in the same solvent; instead, these require higher concentrations of the denaturant. Taken together, our results indicate that amyloids are a heterogeneous ensemble of metastable states. Furthermore, dissolution of each structurally homogeneous member requires a unique threshold concentration of denaturant. Fraction of soluble amyloids as a function of concentration of denaturants is found to be sigmoidal. The sigmoidal curve becomes progressively steeper with progressive seeding of the amyloids, although the midpoint remains unchanged. Therefore, heterogeneity of the amyloids is a major determinant of the steepness of the sigmoidal curve. The sigmoidal curve can be fit assuming a normal distribution for the population of the amyloids of various kinetic stabilities. We propose that the mean and the standard deviation of the normal distribution provide quantitative estimates of mean kinetic stability and heterogeneity, respectively, of the amyloids in a certain preparation.

Dataset Information

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

Publications

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

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