Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

Spagnolo, Daniel M.; Al-Kofahi, Yousef; Zhu, Peihong; Lezon, Timothy R.; Gough, Albert; Stern, Andrew M.; Lee, Adrian V.; Ginty, Fiona; Sarachan, Brion; Taylor, D. Lansing; Chennubhotla, S. Chakra

doi:10.1158/0008-5472.can-17-0676

Cited by 18 publications

(16 citation statements)

References 16 publications

(20 reference statements)

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“…Methods to understand the complex biology and the prognostic and predictive implications of the many cell types within the tumor microenvironment (TME) are rapidly progressing. Techniques to incorporate spatial information with hyperspectral analysis typically involve identifying specific compartments within the TME [3, 12, 13]. While these methodologies identify clinically relevant compartments and cell densities within them, they are currently severely limited in their ability to reproducibly assess, in an automated fashion, all aspects of tumor architecture and the associated immune cell population.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Enfield¹,

Martin

Marshall³

et al. 2019

j. immunotherapy cancer

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BackgroundThe tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells, and the immune component of the TME is highly prognostic for tumor progression and patient outcome. In lung cancer, anti-PD-1 therapy significantly improves patient survival through activation of T cell cytotoxicity against tumor cells. Direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, indicating that spatial organization of immune cells within the TME reflects a critical process in anti-tumor immunity. Current immunohistochemistry (IHC) imaging techniques identify immune cell numbers and densities, but lack assessment of cell–cell spatial relationships (or “cell sociology”). Immune functionality, however, is often dictated by cell-to-cell contact and cannot be resolved by simple metrics of cell density (for example, number of cells per mm2). To address this issue, we developed a Hyperspectral Cell Sociology technology platform for the analysis of cell–cell interactions in multi-channel IHC-stained tissue.MethodsTissue sections of primary tumors from lung adenocarcinoma patients with known clinical outcome were stained using multiplex IHC for CD3, CD8, and CD79a, and hyperspectral image analysis determined the phenotype of all cells. A Voronoi diagram for each cell was used to approximate cell boundaries, and the cell type of all neighboring cells was identified and quantified. Monte Carlo analysis was used to assess whether cell sociology patterns were likely due to random distributions of the cells.ResultsHigh density of intra-tumoral CD8+ T cells was significantly associated with non-recurrence of tumors. A cell sociology pattern of CD8+ T cells surrounded by tumor cells was more significantly associated with non-recurrence compared to CD8+ T cell density alone. CD3+ CD8- T cells surrounded by tumor cells was also associated with non-recurrence, but at a similar significance as cell density alone. Cell sociology metrics improved recurrence classifications of 12 patients. Monte Carlo re-sampling analysis determined that these cell sociology patterns were non-random.ConclusionHyperspectral Cell Sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0488-6) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Enfield¹,

Martin

Marshall³

et al. 2019

j. immunotherapy cancer

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“…Whole scanned slides give more complete, wider fields of view, views that are not possible with a light microscope. Combined with image registration tools, multiple layers of whole slide images obtained from the same sample can be mapped together, allowing functional annotation with multiple channels and spatial analysis among different labels (12, 13). The available image analysis algorithms have also progressed significantly in recent years.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…QSP begins with a focus on patient sample analytics (Fig. 1a) where patient biospecimens (adjacent “normal” and disease biopsies and longitudinal samples where possible) are obtained and analyzed by methods including DNA sequencing, transcriptomics, epigenetics, proteomics, metabolomics, and computational pathology (Spagnolo et al 2016, 2017). Despite the challenge of obtaining longitudinal tissue biopsies, single time point measurements can often provide valuable insights into disease progression.…”

Section: Introductionmentioning

confidence: 99%

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Taylor

Gough

Schurdak

et al. 2019

Concepts and Principles of Pharmacology

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Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, targetcentric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

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Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

Cited by 18 publications

References 16 publications

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

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

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

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