Pericytes in the Premetastatic Niche

Paiva, Ana; Lousado, Luiza; Guerra, Daniel; Azevedo, Patrick O.; Sena, Isadora F. G.; Andreotti, Julia P.; Santos, Gabryella S P; Gonçalves, Ricardo; Mintz, Akiva; Birbrair, Alexander

doi:10.1158/0008-5472.can-17-3883

Cited by 68 publications

(48 citation statements)

References 116 publications

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“…The importance of the stroma was first recognized more than a century ago, beginning with observations that certain cancers metastasize exclusively to specific organs 1 . During the past two decades numerous studies have revealed interactions between malignant cells and the stroma that promote diverse tumor functions including angiogenesis 4,5 , metastasis 6 , and immunosuppression 7,8 . Stromal cells differ between patients and tumor types [9][10][11][12][13][14][15] and the abundance of certain stromal cell populations are used in the clinic as biomarkers [16][17][18][19] and therapeutic targets [20][21][22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies. Results Bayesian inference of cell type composition and tumor expressionTED uses a scRNA-seq reference dataset to infer two parameters of interest from bulk RNA-seq data: (i) the proportion of cell types in the bulk population and (ii) the average expression profiles of each cell type. TED describes the proportion and expression profiles of each cell type as latent variables that it infers from the data ( Fig. 1a, Supplementary Fig. 1, Supplementary Note 1). TED makes the key simplifying assumption that each non-malignant cell type shares a common gene expression profile across patients, as observed in the cases analyzed to date 28,30,31 .Critically, each bulk RNA-seq sample is then assumed to have a unique tumor expression profile that we infer from the data.Expression in the reference and bulk RNA-seq data often differ substantially due to batch effects or tumor heterogeneity. To account for uncertainty in the reference cell type expression matrix, TED implements a fully Bayesian inference of tumor composition. First, TED uses Gibbs sampling to estimate the posterior joint distribution of cell type composition, θ0, and gene expression profiles, Z, i.e. P(θ0, Z | φ, X; α) ( Fig. 1a, red, top). Second, to account for tumor cells, or other cell types which cannot be observed in the reference dataset, TED infers a maximum likelihood estimate (MLE) for tumor expression profiles, ψtum (Fig. 1a, red, mid). During this step TED also infers the maximum a posterior estimate (MAP) of the expression profile of non-malignant stromal cells, ψstr, to correct for batch effects between bulk and single cell RNAseq platforms. Last, TED uses the updated expression profile for each patient and cell type to resample the posterior distribution of cell type composition, θ, i.e. P(θ | ψtum , ψstr ,X; α) ( Fig. 1a, red, bottom). Optionally, TED has an additional mode which can be used to learn common patterns of expression heterogeneit...

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Section: Introductionmentioning

confidence: 99%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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“…Interestingly, some findings support the possibility of epithelial-pericyte transition occurring within the TME to support tumor processes such as angiogenesis [29]. Overall, research into cancer-associated pericytes remains limited, but given their role in normal tissue and noncancer diseases, and the importance of angiogenesis in tumorigenesis and progression, a better understanding will likely improve TME targeting strategies [29,30].…”

Section: Epithelial Cell Typesmentioning

confidence: 96%

Implications for Tumor Microenvironment and Epithelial Crosstalk in the Management of Gastrointestinal Cancers

Westphalen

Wee

et al. 2019

Journal of Oncology

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Rapid advances in technology are revealing previously unknown organization, cooperation, and limitations within the population of nontumor cells surrounding the tumor epithelium known as the tumor microenvironment (TME). Nowhere are these findings more pertinent than in the gastrointestinal (GI) tract where exquisite cell specialization supports a complex microenvironmental niche characterized by rapid stemness-associated cell turnover, pathogen sensing, epithelial orchestration of immune signaling, and other facets that maintain the complex balance between homeostasis, inflammation, and disease. Here, we summarize and discuss select emerging concepts in the precancerous microenvironment, TME, and tumor epithelial-TME crosstalk as well as their implications for the management of GI cancers.

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“…The use of typical knockout mice models has proved useful in understanding the role of key molecules in physiological and pathological conditions . Nonetheless, these technologies have their limitations.…”

Section: Perspectives/future Directionsmentioning

confidence: 99%

“…However, this model is not yet available commercially, and those mice should be created in the future. In addition to studies using genetic mouse models, transcriptomic and single‐cell analyses represent fundamental tools that will help us understand the roles of macrophages within the skin healing microenvironment. These studies may be carried out by isolating via flow cytometry macrophages’ subpopulations from the skin before, and, at different time points, after lesion from both mice and humans.…”

Section: Perspectives/future Directionsmentioning

confidence: 99%

Macrophage‐derived GPNMB accelerates skin healing

Silva

Prazeres

Paiva

et al. 2018

Experimental Dermatology

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Healing is a vital response important for the re-establishment of the skin integrity following injury. Delayed or aberrant dermal wound healing leads to morbidity in patients. The development of therapies to improve dermal healing would be useful. Currently, the design of efficient treatments is stalled by the lack of detailed knowledge about the cellular and molecular mechanisms involved in wound healing. Recently, using state-of-the-art technologies, it was revealed that macrophages signal via GPNMB to mesenchymal stem cells, accelerating skin healing. Strikingly, transplantation of macrophages expressing GPNMB improves skin healing in GPNMB-mutant mice. Additionally, topical treatment with recombinant GPNMB restored mesenchymal stem cells recruitment and accelerated wound closure in the diabetic skin. From a drug development perspective, this GPNMB is a new candidate for skin healing.

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Pericytes in the Premetastatic Niche

Cited by 68 publications

References 116 publications

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Implications for Tumor Microenvironment and Epithelial Crosstalk in the Management of Gastrointestinal Cancers

Macrophage‐derived GPNMB accelerates skin healing

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