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      Single-cell analysis reveals the pan-cancer invasiveness-associated transition of adipose-derived stromal cells into COL11A1-expressing cancer-associated fibroblasts

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          Abstract

          During the last ten years, many research results have been referring to a particular type of cancer-associated fibroblasts associated with poor prognosis, invasiveness, metastasis and resistance to therapy in multiple cancer types, characterized by a gene expression signature with prominent presence of genes COL11A1, THBS2 and INHBA. Identifying the underlying biological mechanisms responsible for their creation may facilitate the discovery of targets for potential pan-cancer therapeutics. Using a novel computational approach for single-cell gene expression data analysis identifying the dominant cell populations in a sequence of samples from patients at various stages, we conclude that these fibroblasts are produced by a pan-cancer cellular transition originating from a particular type of adipose-derived stromal cells naturally present in the stromal vascular fraction of normal adipose tissue, having a characteristic gene expression signature. Focusing on a rich pancreatic cancer dataset, we provide a detailed description of the continuous modification of the gene expression profiles of cells as they transition from APOD-expressing adipose-derived stromal cells to COL11A1-expressing cancer-associated fibroblasts, identifying the key genes that participate in this transition. These results also provide an explanation to the well-known fact that the adipose microenvironment contributes to cancer progression.

          Author summary

          Computational analysis of rich gene expression data at the single-cell level from cancer biopsies can lead to biological discoveries about the nature of the disease. Using a computational methodology that identifies the gene expression profile of the dominant cell population for a particular cell type in the microenvironment of tumors, we observed that there is a remarkably continuous modification of this profile among patients, corresponding to a cellular transition. Specifically, we found that the starting point of this transition has a unique characteristic signature corresponding to cells that are naturally residing in normal adipose tissue. We also found that the endpoint of the transition has another characteristic signature corresponding to a particular type of cancer-associated fibroblasts with prominent expression of gene COL11A1, which has been found strongly associated with invasiveness, metastasis and resistance to therapy in multiple cancer types. Our results provide an explanation to the well-known fact that the adipose tissue contributes to cancer progression, shedding light on the biological mechanism by which tumor cells interact with the adipose microenvironment. We provide a detailed description of the changing profile during the transition, identifying associated genes as potential targets for pan-cancer therapeutics inhibiting the underlying mechanism.

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          Most cited references58

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop 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. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              A framework for advancing our understanding of cancer-associated fibroblasts

              Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with diverse functions, including matrix deposition and remodelling, extensive reciprocal signalling interactions with cancer cells and crosstalk with infiltrating leukocytes. As such, they are a potential target for optimizing therapeutic strategies against cancer. However, many challenges are present in ongoing attempts to modulate CAFs for therapeutic benefit. These include limitations in our understanding of the origin of CAFs and heterogeneity in CAF function, with it being desirable to retain some antitumorigenic functions. On the basis of a meeting of experts in the field of CAF biology, we summarize in this Consensus Statement our current knowledge and present a framework for advancing our understanding of this critical cell type within the tumour microenvironment.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                20 July 2021
                July 2021
                : 17
                : 7
                : e1009228
                Affiliations
                [1 ] Department of Systems Biology, Columbia University, New York, New York, United States of America
                [2 ] Department of Electrical Engineering, Columbia University, New York, New York, United States of America
                [3 ] Center for Cancer Systems Therapeutics, Columbia University, New York, New York, United States of America
                [4 ] Immunology Department, School of Medicine and Health Sciences, University of Oviedo, Oviedo, Spain
                [5 ] Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, United States of America
                University of Chicago, UNITED STATES
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: Columbia University has filed a provisional patent application for ideas presented in this work.

                [¤]

                Current address: Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

                Author information
                https://orcid.org/0000-0002-0111-0848
                https://orcid.org/0000-0002-9193-0806
                https://orcid.org/0000-0002-5432-9413
                https://orcid.org/0000-0003-2459-909X
                Article
                PCOMPBIOL-D-21-00198
                10.1371/journal.pcbi.1009228
                8323949
                34283835
                bde817a2-0e86-4e9a-a2e0-98cd0e85fc68
                © 2021 Zhu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 February 2021
                : 30 June 2021
                Page count
                Figures: 2, Tables: 6, Pages: 22
                Funding
                Funded by: Columbia University
                Award Recipient :
                This work was funded by Columbia University’s unrestricted-purpose allocation of inventor’s (D.A.) research of royalties resulting from intellectual property totally unrelated to the work described in this paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Gene Expression
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Breast Tumors
                Breast Cancer
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Connective Tissue Cells
                Fibroblasts
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Gynecological Tumors
                Ovarian Cancer
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Gastrointestinal Tumors
                Pancreatic Cancer
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Lung and Intrathoracic Tumors
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Invasive Tumors
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-07-30
                All relevant data are within the manuscript and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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