A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging
TL;DR: Organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression is demonstrated and provided a framework to apply multiplexed imaging to immune oncology.
About: This article is published in Cell.The article was published on 2018-09-06 and is currently open access. It has received 648 citations till now. The article focuses on the topics: Tumor microenvironment & Immune system.
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TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
7,892 citations
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Abstract: Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat
2,037 citations
TL;DR: This review focuses on how tumour and tumour-associated stromal cells deposit, biochemically and biophysically modify, and degrade tumours' extracellular matrix (ECM).
Abstract: Tissues are dynamically shaped by bidirectional communication between resident cells and the extracellular matrix (ECM) through cell-matrix interactions and ECM remodelling. Tumours leverage ECM remodelling to create a microenvironment that promotes tumourigenesis and metastasis. In this review, we focus on how tumour and tumour-associated stromal cells deposit, biochemically and biophysically modify, and degrade tumour-associated ECM. These tumour-driven changes support tumour growth, increase migration of tumour cells, and remodel the ECM in distant organs to allow for metastatic progression. A better understanding of the underlying mechanisms of tumourigenic ECM remodelling is crucial for developing therapeutic treatments for patients.
769 citations
TL;DR: A review of the recent progress in cancer immunotherapy is outlined, particularly by focusing on landmark studies and the recent single-cell characterization of tumor-associated immune cells, and the phenotypic diversities of intratumoral immune cells and their connections with cancer Immunotherapy are summarized.
Abstract: Immunotherapy has revolutionized cancer treatment and rejuvenated the field of tumor immunology Several types of immunotherapy, including adoptive cell transfer (ACT) and immune checkpoint inhibitors (ICIs), have obtained durable clinical responses, but their efficacies vary, and only subsets of cancer patients can benefit from them Immune infiltrates in the tumor microenvironment (TME) have been shown to play a key role in tumor development and will affect the clinical outcomes of cancer patients Comprehensive profiling of tumor-infiltrating immune cells would shed light on the mechanisms of cancer–immune evasion, thus providing opportunities for the development of novel therapeutic strategies However, the highly heterogeneous and dynamic nature of the TME impedes the precise dissection of intratumoral immune cells With recent advances in single-cell technologies such as single-cell RNA sequencing (scRNA-seq) and mass cytometry, systematic interrogation of the TME is feasible and will provide insights into the functional diversities of tumor-infiltrating immune cells In this review, we outline the recent progress in cancer immunotherapy, particularly by focusing on landmark studies and the recent single-cell characterization of tumor-associated immune cells, and we summarize the phenotypic diversities of intratumoral immune cells and their connections with cancer immunotherapy We believe such a review could strengthen our understanding of the progress in cancer immunotherapy, facilitate the elucidation of immune cell modulation in tumor progression, and thus guide the development of novel immunotherapies for cancer treatment
737 citations
TL;DR: The intersection between deep learning and cellular image analysis is reviewed and an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists are provided.
Abstract: Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.
714 citations
References
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TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
44,587 citations
"A Structured Tumor-Immune Microenvi..." refers methods in this paper
...The nuclear images of patients 1 and 2 were manually segmented in ImageJ (Schneider et al., 2012) using a Wacom Intuos Draw graphics tablet as previously described (Van Valen et al., 2016)....
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...The nuclear images of patients 1 and 2 were manually segmented in ImageJ (Schneider et al., 2012) using a Wacom Intuos Draw graphics tablet as previously described (Van Valen et al....
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...NIH Image to ImageJ: 25 years of image analysis....
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TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
"A Structured Tumor-Immune Microenvi..." refers methods in this paper
...The neighbors of each cell were defined as cells with direct contact with the cell of interest and were identified using Haralick’s gray-level co-occurrence matrix (Haralick et al., 1973)....
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TL;DR: The surface of nucleosomes is studded with a multiplicity of modifications that can dictate the higher-order chromatin structure in which DNA is packaged and can orchestrate the ordered recruitment of enzyme complexes to manipulate DNA.
10,046 citations
"A Structured Tumor-Immune Microenvi..." refers background in this paper
...Since H3K9ac was shown to be correlated with open chromatin and H3K27me3 with closed chromatin (Kouzarides, 2007), these global changes in their ratios may indicate that cells on the tumor border are more transcriptionally active than those in the tumor center....
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The Royal Marsden NHS Foundation Trust1, European Institute of Oncology2, University of Colorado Denver3, Aix-Marseille University4, Baylor University5, University of Michigan6, University of Zurich7, Cross Cancer Institute8, Peter MacCallum Cancer Centre9, Netherlands Cancer Institute10, Hospital General Universitario Gregorio Marañón11, Memorial Sloan Kettering Cancer Center12, Huntsman Cancer Institute13, Yale University14, University of Melbourne15, University of Sydney16, Cornell University17, Ludwig Institute for Cancer Research18, University of Utah19, Yale Cancer Center20, Odense University Hospital21, Bristol-Myers Squibb22, Harvard University23
TL;DR: Among previously untreated patients with metastatic melanoma, nivolumab alone or combined with ipilimumab resulted in significantly longer progression-free survival than ipILimumab alone, and in patients with PD-L1-negative tumors, the combination of PD-1 and CTLA-4 blockade was more effective than either agent alone.
Abstract: The median progression-free survival was 11.5 months (95% confidence interval [CI], 8.9 to 16.7) with nivolumab plus ipilimumab, as compared with 2.9 months (95% CI, 2.8 to 3.4) with ipilimumab (hazard ratio for death or disease progression, 0.42; 99.5% CI, 0.31 to 0.57; P<0.001), and 6.9 months (95% CI, 4.3 to 9.5) with nivolumab (hazard ratio for the comparison with ipilimumab, 0.57; 99.5% CI, 0.43 to 0.76; P<0.001). In patients with tumors positive for the PD-1 ligand (PD-L1), the median progression-free survival was 14.0 months in the nivolumab-plus-ipilimumab group and in the nivolumab group, but in patients with PD-L1–negative tumors, progression-free survival was longer with the combination therapy than with nivolumab alone (11.2 months [95% CI, 8.0 to not reached] vs. 5.3 months [95% CI, 2.8 to 7.1]). Treatment-related adverse events of grade 3 or 4 occurred in 16.3% of the patients in the nivolumab group, 55.0% of those in the nivolumab-plus-ipilimumab group, and 27.3% of those in the ipilimumab group. CONCLUSIONS Among previously untreated patients with metastatic melanoma, nivolumab alone or combined with ipilimumab resulted in significantly longer progression-free survival than ipilimumab alone. In patients with PD-L1–negative tumors, the combination of PD-1 and CTLA-4 blockade was more effective than either agent alone. (Funded by Bristol-Myers Squibb; CheckMate 067 ClinicalTrials.gov number, NCT01844505.)
6,318 citations
"A Structured Tumor-Immune Microenvi..." refers background in this paper
...However, due to the interdependence of immunoregulatory protein expression here, combination immunotherapies may provide only a modest increase to the pool of responsive patients, as observed in anti-PD-1 anti-CTLA-4 combination therapies in melanoma (Larkin et al., 2015)....
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TL;DR: The origin, mechanisms of expansion and suppressive functions of MDSCs, as well as the potential to target these cells for therapeutic benefit are discussed.
Abstract: Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells that expand during cancer, inflammation and infection, and that have a remarkable ability to suppress T-cell responses. These cells constitute a unique component of the immune system that regulates immune responses in healthy individuals and in the context of various diseases. In this Review, we discuss the origin, mechanisms of expansion and suppressive functions of MDSCs, as well as the potential to target these cells for therapeutic benefit.
5,811 citations
"A Structured Tumor-Immune Microenvi..." refers background in this paper
...This cluster of patients displayed increased expression of PD-L1, PD-1, and IDO by CD11c CD11b immune cells, a phenotype suggestive of myeloid derived suppressor cells (Gabrilovich and Nagaraj, 2009)....
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