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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-02-12)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-02-12)
    Abstract: Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with evidence suggesting they represent a heterogeneous population. This study summarises the prognostic role of all proteins characterised in CAFs with immunohistochemistry in non-small cell lung cancer thus far. The functions of these proteins in cellular processes crucial to CAFs are also analysed. Five databases were searched to extract survival outcomes from published studies and statistical techniques, including a novel method, used to capture missing values from the literature. A total of 26 proteins were identified, 21 of which were combined into 7 common cellular processes key to CAFs. Quality assessments for sensitivity analyses were carried out for each study using the REMARK criteria whilst publication bias was assessed using funnel plots. Random effects models consistently identified the expression of podoplanin (Overall Survival (OS)/Disease-specific Survival (DSS), univariate analysis HR 2.25, 95% CIs 1.80–2.82) and α-SMA (OS/DSS, univariate analysis HR 2.11, 95% CIs 1.18–3.77) in CAFs as highly prognostic regardless of outcome measure or analysis method. Moreover, proteins involved in maintaining and generating the CAF phenotype (α-SMA, TGF-β and p-Smad2) proved highly significant after sensitivity analysis (HR 2.74, 95% CIs 1.74–4.33) supporting attempts at targeting this pathway for therapeutic benefit.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 21_Supplement ( 2020-11-01), p. PO-039-PO-039
    Abstract: In this study we set out to define the prominent fibroblast subpopulations found in human non-small cell lung cancer. Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumor progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. We then performed in silico and in vitro analyses to examine the molecular mechanisms regulating fibroblast phenotypes. Single-cell RNA sequencing identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Multiplex immunohistochemistry showed that these fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-β1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo¬ phenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signaling identified additional pathways and cell interactions likely to be involved in fibroblast activation. This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC. Citation Format: Christopher Jon Hanley, Sara Waise, Parker Rachel, Maria-Antoinette Lopez, Julian Taylor, Lucy Kimbley, Jonathon West, Christian Ottensmeier, Mat Rose-Zerilli, Gareth Thomas. Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-039.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 3
    Online Resource
    Online Resource
    Elsevier BV ; 2018
    In:  Diagnostic Histopathology Vol. 24, No. 3 ( 2018-03), p. 124-125
    In: Diagnostic Histopathology, Elsevier BV, Vol. 24, No. 3 ( 2018-03), p. 124-125
    Type of Medium: Online Resource
    ISSN: 1756-2317
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2422358-X
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  • 4
    In: BIO-PROTOCOL, Bio-Protocol, LLC, Vol. 9, No. 23 ( 2019)
    Type of Medium: Online Resource
    ISSN: 2331-8325
    Language: English
    Publisher: Bio-Protocol, LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2833269-6
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  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2018
    In:  Cancer Research Vol. 78, No. 13_Supplement ( 2018-07-01), p. 5084-5084
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 5084-5084
    Abstract: The aim of this work is to examine the heterogeneity in the cancer-associated fibroblast (CAF) population in non-small cell lung cancer through single-cell RNA sequencing. Fresh primary lung tissue was obtained directly from surgery, and the disaggregation process optimised to extract the highest number of fibroblasts (using collagenase P for sixty minutes). Single-cell RNA sequencing was performed using a droplet-barcoded sequencing (Drop-seq) platform. Quality control was performed on the raw sequencing data and the resulting digital gene expression matrix. Initial bioinformatic analysis, including cluster identification, was performed using the Seurat package in R. Subsequent cell type identification and gene set enrichment analysis were carried out with the ToppFun tool and GSEA program respectively. Correlations between the presence of fibroblast subtypes and clinical parameters were analysed using CIBERSORT. Our preliminary analysis of 11 non-small cell lung cancer (NSCLC) tumors and 5 samples of matched non-involved lung revealed the presence of 6 discrete CAF subtypes with differential prognostic impact. Four of the subgroups showed transcriptomic overlap with normal fibroblasts. Of the distinct CAF subtypes, one showed higher expression of genes normally associated with the ‘myofibroblastic' CAF phenotype, including multiple collagens, and was enriched for genes associated with the ‘extracellular structure organisation' gene ontology (GO) term. The second CAF cluster showed higher expression of genes encoding growth factors and regulators of cell growth, with enrichment of genes in the ‘regulation of epithelial cell proliferation' and ‘negative regulation of cell death' GO terms. The remaining four subtypes also showed enrichment of genes in keeping with previously-described fibroblast functions: two subtypes were enriched for genes associated with tissue remodelling, including in the ‘regulation of proteolysis' and ‘regulation of peptidase activity' GO terms. The fifth and sixth clusters showed enrichment for genes in the ‘innate immune response' and ‘angiogenesis' GO terms, respectively. Despite their abundance in most solid cancers, CAF remain a poorly characterised cell population. No single molecular marker identifies all CAF, and it is not yet clear whether different CAF phenotypes exist or whether such subgroups have different functions. Our analysis has identified six discrete CAF subtypes in NSCLC. These subtypes have differential gene set enrichment, suggestive of functional differences. In keeping with this, we found that the CAF subgroups differentially impact on patient prognosis. Identification of CAF subgroups associated with aggressive tumor progression may facilitate the development of more specific stromal targeting strategies. Citation Format: Sara Waise, Christopher Hanley, Rachel Parker, Matthew Rose-Zerilli, Christian Ottensmeier, Gareth Thomas. Characterizing heterogeneity in the cancer-associated fibroblast population in non-small cell lung cancer: Relating phenotype to function [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5084.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 3762-3762
    Abstract: This work aims to characterise the heterogeneity and spatial relationships of the cancer-associated fibroblast (CAF) population in non-small cell lung cancer. Fresh human lung tissue was dissociated for sixty minutes to extract the maximum possible proportion of fibroblasts. Single-cell RNA sequencing was performed using a droplet-barcoded platform (Drop-seq). Quality control was performed on the raw sequencing data and resulting gene expression matrix. Bioinformatic analysis was performed using multiple packages in R. Spatial relationships between cell types were assessed using a multi-immunohistochemical (IHC) staining technique. We developed a workflow for efficient processing of raw Drop-seq data including quality control, normalisation and visualisation. Low-quality events were identified by integrating previously-described and novel quality-control metrics into a machine learning (random forest) model, and demonstrated that this approach improves clustering quality. Applying this method to samples from twelve non-small cell lung cancer (NSCLC) patients, we identified 5 distinct fibroblast subtypes; 3 predominantly derived from normal tissue and 2 largely from tumor samples. Of the normal subtypes, one showed gene expression consistent with the previously-described "inflammatory" fibroblast phenotype. Trajectory analysis identified a branched differentiation process from normal to CAF phenotypes, suggesting that these cells share a common initial activation before differentiation to either a "matrix remodelling" or "hypoxic" subtype. The prevalence and impact of these sub-populations appears to differ between NSCLC subtypes. The "matrix remodelling" subtype is present in both adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), but confers a negative prognostic effect in LUAD only; the "hypoxic" phenotype appears relatively LUSC-specific. Multiplexed IHC using identified cluster markers demonstrated that these subtypes have different spatial distributions and relationships to other cell types. CAF remain a poorly-characterised population, despite their abundance in most solid cancers. No single molecular marker identifies all CAF, and there has been a scarcity of evidence regarding the existence of distinct subtypes and whether such subgroups have different functions. Our analysis has revealed five distinct CAF subtypes in NSCLC. In addition to divergent differentiation pathways, these subtypes have differential gene set enrichment, indicative of functional differences. In keeping with this, the phenotypes show distinct prognostic impact across NSCLC subtypes. Characterisation of CAF subgroups associated with aggressive tumor progression may facilitate identification of novel stromal targeting strategies. Citation Format: Sara Waise, Christopher J. Hanley, Rachel Parker, Christian H. Ottensmeier, Matthew Rose-Zerilli, Gareth J. Thomas. Single-cell analysis of cancer-associated fibroblast heterogeneity in non-small cell lung cancer: Mapping molecular phenotypes in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3762.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 7
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2020
    In:  BMC Medical Research Methodology Vol. 20, No. 1 ( 2020-12)
    In: BMC Medical Research Methodology, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2020-12)
    Abstract: Meta-analyses of studies evaluating survival (time-to-event) outcomes are a powerful technique to assess the strength of evidence for a given disease or treatment. However, these studies rely on the adequate reporting of summary statistics in the source articles to facilitate further analysis. Unfortunately, many studies, especially within the field of prognostic research do not report such statistics, making secondary analyses challenging. Consequently, methods have been developed to infer missing statistics from the commonly published Kaplan-Meier (KM) plots but are liable to error especially when the published number at risk is not included. Methods We therefore developed a method using non-linear optimisation (nlopt) that only requires the KM plot and the commonly published P value to better estimate the underlying censoring pattern. We use this information to then calculate the natural logarithm of the hazard ratio (ln (HR)) and its variance (var) ln (HR), statistics important for meta-analyses. Results We compared this method to the Parmar method which also does not require the number at risk to be published. In a validation set consisting of 13 KM studies, a statistically significant improvement in calculating ln (HR) when using an exact P value was obtained (mean absolute error 0.014 vs 0.077, P  = 0.003). Thus, when the true HR has a value of 1.5, inference of the HR using the proposed method would set limits between 1.49/1.52, an improvement of the 1.39/1.62 limits obtained using the Parmar method. We also used Monte Carlo simulations to establish recommendations for the number and positioning of points required for the method. Conclusion The proposed non-linear optimisation method is an improvement on the existing method when only a KM plot and P value are included and as such will enhance the accuracy of meta-analyses performed for studies analysing time-to-event outcomes. The nlopt source code is available, as is a simple-to-use web implementation of the method.
    Type of Medium: Online Resource
    ISSN: 1471-2288
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2041362-2
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  • 8
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-01-31)
    Abstract: Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 9
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-07-03)
    Abstract: Single-cell RNA sequencing (scRNA-Seq) provides a valuable platform for characterising multicellular ecosystems. Fibroblasts are a heterogeneous cell type involved in many physiological and pathological processes, but remain poorly-characterised. Analysis of fibroblasts is challenging: these cells are difficult to isolate from tissues, and are therefore commonly under-represented in scRNA-seq datasets. Here, we describe an optimised approach for fibroblast isolation from human lung tissues. We demonstrate the potential for this procedure in characterising stromal cell phenotypes using scRNA-Seq, analyse the effect of tissue disaggregation on gene expression, and optimise data processing to improve clustering quality. We also assess the impact of in vitro culture conditions on stromal cell gene expression and proliferation, showing that altering these conditions can skew phenotypes.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2615211-3
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  • 10
    Online Resource
    Online Resource
    Royal College of Physicians ; 2013
    In:  Clinical Medicine Vol. 13, No. 1 ( 2013-02), p. 32-34
    In: Clinical Medicine, Royal College of Physicians, Vol. 13, No. 1 ( 2013-02), p. 32-34
    Type of Medium: Online Resource
    ISSN: 1470-2118 , 1473-4893
    Language: English
    Publisher: Royal College of Physicians
    Publication Date: 2013
    detail.hit.zdb_id: 2074994-6
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