citations.bib

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@Article{Pelon2020,
  author          = {Pelon, Floriane and Bourachot, Brigitte and Kieffer, Yann and Magagna, Ilaria and Mermet-Meillon, Fanny and Bonnet, Isabelle and Costa, Ana and Givel, Anne Marie and Attieh, Youmna and Barbazan, Jorge and Bonneau, Claire and Fuhrmann, Laetitia and Descroix, St{\'{e}}phanie and Vignjevic, Danijela and Silberzan, Pascal and Parrini, Maria Carla and Vincent-Salomon, Anne and Mechta-Grigoriou, Fatima},
  journal         = {Nature Communications 2020 11:1},
  title           = {{Cancer-associated fibroblast heterogeneity in axillary lymph nodes drives metastases in breast cancer through complementary mechanisms}},
  year            = {2020},
  issn            = {2041-1723},
  month           = {jan},
  number          = {1},
  pages           = {1--20},
  volume          = {11},
  abstract        = {Although fibroblast heterogeneity is recognized in primary tumors, both its characterization in and its impact on metastases remain unknown. Here, combining flow cytometry, immunohistochemistry and RNA-sequencing on breast cancer samples, we identify four Cancer-Associated Fibroblast (CAF) subpopulations in metastatic lymph nodes (LN). Two myofibroblastic subsets, CAF-S1 and CAF-S4, accumulate in LN and correlate with cancer cell invasion. By developing functional assays on primary cultures, we demonstrate that these subsets promote metastasis through distinct functions. While CAF-S1 stimulate cancer cell migration and initiate an epithelial-to-mesenchymal transition through CXCL12 and TGF$\beta$ pathways, highly contractile CAF-S4 induce cancer cell invasion in 3-dimensions via NOTCH signaling. Patients with high levels of CAFs, particularly CAF-S4, in LN at diagnosis are prone to develop late distant metastases. Our findings suggest that CAF subset accumulation in LN is a prognostic marker, suggesting that CAF subsets could be examined in axillary LN at diagnosis. Cancer associated fibroblasts are known to promote the progression of cancer. Here, the authors show that two particular subsets of cancer associated fibroblasts induce metastasis but work via distinct mechanisms including, chemokine signalling and Notch signalling.},
  doi             = {10.1038/s41467-019-14134-w},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pelon et al. - 2020 - Cancer-associated fibroblast heterogeneity in axillary lymph nodes drives metastases in breast cancer through comp.pdf:pdf},
  keywords        = {Breast cancer, Cancer, Cancer microenvironment, Tumour heterogeneity},
  mendeley-groups = {PhD/Stromal cells},
  pmid            = {31964880},
  publisher       = {Nature Publishing Group},
  url             = {https://www.nature.com/articles/s41467-019-14134-w},
}

@Article{Li2022,
  author          = {Li, Yumei and Ge, Xinzhou and Peng, Fanglue and Li, Wei and Li, Jingyi Jessica},
  journal         = {Genome Biology},
  title           = {{Exaggerated false positives by popular differential expression methods when analyzing human population samples}},
  year            = {2022},
  issn            = {1474760X},
  month           = {dec},
  number          = {1},
  pages           = {1--13},
  volume          = {23},
  abstract        = {When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20{\%} when the target FDR is 5{\%}. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.},
  doi             = {10.1186/S13059-022-02648-4/FIGURES/2},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Li et al. - 2022 - Exaggerated false positives by popular differential expression methods when analyzing human population samples.pdf:pdf},
  keywords        = {Animal Genetics and Genomics, Bioinformatics, Evolutionary Biology, Human Genetics, Microbial Genetics and Genomics, Plant Genetics and Genomics, mRNA isoforms, transcriptomics},
  mendeley-groups = {PhD/RNA-seq},
  pmid            = {35292087},
  publisher       = {BioMed Central Ltd},
  url             = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4},
}

@Article{Zhao2020,
  author          = {Zhao, Shanrong and Ye, Zhan and Stanton, Robert},
  journal         = {RNA},
  title           = {{Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols}},
  year            = {2020},
  issn            = {14699001},
  month           = {aug},
  number          = {8},
  pages           = {903},
  volume          = {26},
  abstract        = {In recent years RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (Reads Per Kilobase of transcript per Million reads mapped) and TPM (Transcripts Per Million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions is very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.},
  doi             = {10.1261/RNA.074922.120},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhao, Ye, Stanton - 2020 - Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols.pdf:pdf},
  keywords        = {FPKM, Normalization, RNA-seq, RPKM, TPM},
  mendeley-groups = {PhD/RNA-seq},
  pmid            = {32284352},
  publisher       = {Cold Spring Harbor Laboratory Press},
  url             = {/pmc/articles/PMC7373998/ /pmc/articles/PMC7373998/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373998/},
}

@Article{Newman2019,
  author          = {Newman, Aaron M. and Steen, Chlo{\'{e}} B. and Liu, Chih Long and Gentles, Andrew J. and Chaudhuri, Aadel A. and Scherer, Florian and Khodadoust, Michael S. and Esfahani, Mohammad S. and Luca, Bogdan A. and Steiner, David and Diehn, Maximilian and Alizadeh, Ash A.},
  journal         = {Nature Biotechnology 2019 37:7},
  title           = {{Determining cell type abundance and expression from bulk tissues with digital cytometry}},
  year            = {2019},
  issn            = {1546-1696},
  month           = {may},
  number          = {7},
  pages           = {773--782},
  volume          = {37},
  abstract        = {Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.},
  doi             = {10.1038/s41587-019-0114-2},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Newman et al. - 2019 - Determining cell type abundance and expression from bulk tissues with digital cytometry.pdf:pdf},
  keywords        = {Cancer microenvironment, Computational biology and bioinformatics, Immunology},
  mendeley-groups = {PhD/RNA-seq},
  pmid            = {31061481},
  publisher       = {Nature Publishing Group},
  url             = {https://www.nature.com/articles/s41587-019-0114-2},
}

@Article{Chu2022,
  author          = {Chu, Tinyi and Wang, Zhong and Pe'er, Dana and Danko, Charles G.},
  journal         = {Nature Cancer 2022 3:4},
  title           = {{Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology}},
  year            = {2022},
  issn            = {2662-1347},
  month           = {apr},
  number          = {4},
  pages           = {505--517},
  volume          = {3},
  abstract        = {Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data. Danko and colleagues develop BayesPrism, a bulk RNA sequencing deconvolution tool to infer cell type composition and cell-specific expression levels across clinical cancer datasets.},
  doi             = {10.1038/s43018-022-00356-3},
  file            = {:home/kevin/Downloads/bayesprism.pdf:pdf},
  keywords        = {Cancer, Cancer genomics, Statistical methods},
  mendeley-groups = {PhD/Other},
  publisher       = {Nature Publishing Group},
  url             = {https://www.nature.com/articles/s43018-022-00356-3},
}

@Article{Newman2015,
  author          = {Newman, Aaron M. and Liu, Chih Long and Green, Michael R. and Gentles, Andrew J. and Feng, Weiguo and Xu, Yue and Hoang, Chuong D. and Diehn, Maximilian and Alizadeh, Ash A.},
  journal         = {Nature Methods},
  title           = {{Robust enumeration of cell subsets from tissue expression profiles}},
  year            = {2015},
  issn            = {15487105},
  month           = {apr},
  number          = {5},
  pages           = {453--457},
  volume          = {12},
  abstract        = {We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).},
  doi             = {10.1038/nmeth.3337},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Newman et al. - 2015 - Robust enumeration of cell subsets from tissue expression profiles.pdf:pdf},
  keywords        = {Computational biology and bioinformatics, Gene expression analysis, Immunology, Tumour heterogeneity},
  mendeley-groups = {MSc/MA5107},
  pmid            = {25822800},
  publisher       = {Nature Publishing Group},
  url             = {http://cibersort.stanford.edu/},
}
@article{Szolek2014,
abstract = {Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97{\%} enabling its use in a broad range of applications.},
author = {Szolek, Andr{\'{a}}s and Schubert, Benjamin and Mohr, Christopher and Sturm, Marc and Feldhahn, Magdalena and Kohlbacher, Oliver},
doi = {10.1093/BIOINFORMATICS/BTU548},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Szolek et al. - 2014 - OptiType precision HLA typing from next-generation sequencing data.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics},
mendeley-groups = {PhD/Immunoinformatics},
month = {dec},
number = {23},
pages = {3310--3316},
pmid = {25143287},
publisher = {Oxford Academic},
title = {{OptiType: precision HLA typing from next-generation sequencing data}},
url = {https://academic.oup.com/bioinformatics/article/30/23/3310/206910},
volume = {30},
year = {2014}
}
@article{Kieffer2020,
abstract = {A subset of cancer-associated fibroblasts (FAP+/CAF-S1) mediates immunosup-pression in breast cancers, but its heterogeneity and its impact on immunotherapy response remain unknown. Here, we identify 8 CAF-S1 clusters by analyzing more than 19,000 single CAF-S1 fibroblasts from breast cancer. We validate the five most abundant clusters by flow cytometry and in silico analyses in other cancer types, highlighting their relevance. Myofibroblasts from clusters 0 and 3, characterized by extracellular matrix proteins and TGF$\beta$ signaling, respectively, are indicative of primary resistance to immunotherapies. Cluster 0/ecm-myCAF upregulates PD-1 and CTLA4 protein levels in regulatory T lymphocytes (Tregs), which, in turn, increases CAF-S1 cluster 3/TGF$\beta$-myCAF cellular content. Thus, our study highlights a positive feedback loop between specific CAF-S1 clusters and Tregs and uncovers their role in immunotherapy resistance. Significance: Our work provides a significant advance in characterizing and understanding FAP+ CAF in cancer. We reached a high resolution at single-cell level, which enabled us to identify specific clusters associated with immunosuppression and immunotherapy resistance. Identification of cluster-specific signatures paves the way for therapeutic options in combination with immunotherapies.},
author = {Kieffer, Yann and Hocine, Hocine R. and Gentric, G{\'{e}}raldine and Pelon, Floriane and Bernard, Charles and Bourachot, Brigitte and Lameiras, Sonia and Albergante, Luca and Bonneau, Claire and Guyard, Alice and Tarte, Karin and Zinovyev, Andrei and Baulande, Sylvain and Zalcman, Gerard and Vincent-Salomon, Anne and Mechta-Grigoriou, Fatima},
doi = {10.1158/2159-8290.CD-19-1384/333435/AM/SINGLE-CELL-ANALYSIS-REVEALS-FIBROBLAST-CLUSTERS},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kieffer et al. - 2020 - Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer.pdf:pdf},
issn = {21598290},
journal = {Cancer Discovery},
mendeley-groups = {PhD/Stromal cells},
month = {sep},
number = {9},
pages = {1330--1351},
pmid = {32434947},
publisher = {American Association for Cancer Research Inc.},
title = {{Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer}},
url = {https://aacrjournals.org/cancerdiscovery/article/10/9/1330/2752/Single-Cell-Analysis-Reveals-Fibroblast-Clusters},
volume = {10},
year = {2020}
}

@Article{Newman2015,
  author          = {Newman, Aaron M. and Liu, Chih Long and Green, Michael R. and Gentles, Andrew J. and Feng, Weiguo and Xu, Yue and Hoang, Chuong D. and Diehn, Maximilian and Alizadeh, Ash A.},
  journal         = {Nature Methods 2015 12:5},
  title           = {{Robust enumeration of cell subsets from tissue expression profiles}},
  year            = {2015},
  issn            = {1548-7105},
  month           = {mar},
  number          = {5},
  pages           = {453--457},
  volume          = {12},
  abstract        = {A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper. We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets ( http://cibersort.stanford.edu/ ).},
  doi             = {10.1038/nmeth.3337},
  file            = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Newman et al. - 2015 - Robust enumeration of cell subsets from tissue expression profiles(2).pdf:pdf},
  keywords        = {Computational biology and bioinformatics,Gene expression analysis,Immunology,Tumour heterogeneity},
  mendeley-groups = {PhD/Deconvolution},
  pmid            = {25822800},
  publisher       = {Nature Publishing Group},
  url             = {https://www.nature.com/articles/nmeth.3337},
}
@article{Su2018,
abstract = {Carcinoma-associated fibroblasts (CAFs) are abundant and heterogeneous stromal cells in tumor microenvironment that are critically involved in cancer progression. Here, we demonstrate that two cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients. CD10+GPR77+ CAFs promote tumor formation and chemoresistance by providing a survival niche for cancer stem cells (CSCs). Mechanistically, CD10+GPR77+ CAFs are driven by persistent NF-$\kappa$B activation via p65 phosphorylation and acetylation, which is maintained by complement signaling via GPR77, a C5a receptor. Furthermore, CD10+GPR77+ CAFs promote successful engraftment of patient-derived xenografts (PDXs), and targeting these CAFs with a neutralizing anti-GPR77 antibody abolishes tumor formation and restores tumor chemosensitivity. Our study reveals a functional CAF subset that can be defined and isolated by specific cell-surface markers and suggests that targeting the CD10+GPR77+ CAF subset could be an effective therapeutic strategy against CSC-driven solid tumors. CD10 and GPR77 identify a cancer stemness-sustaining cancer-associated fibroblast subset.},
author = {Su, Shicheng and Chen, Jianing and Yao, Herui and Liu, Jiang and Yu, Shubin and Lao, Liyan and Wang, Minghui and Luo, Manli and Xing, Yue and Chen, Fei and Huang, Di and Zhao, Jinghua and Yang, Linbin and Liao, Dan and Su, Fengxi and Li, Mengfeng and Liu, Qiang and Song, Erwei},
doi = {10.1016/J.CELL.2018.01.009},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Su et al. - 2018 - CD10 GPR77 Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness.pdf:pdf},
issn = {1097-4172},
journal = {Cell},
keywords = {A549 Cells,Anaphylatoxin C5a,Cell Transformation,Chemokine / immunology*,Drug Resistance,Erwei Song,Fibroblasts / immunology*,Fibroblasts / pathology,Humans,Jianing Chen,MCF-7 Cells,MEDLINE,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,Neoplasm / immunology*,Neoplasm Proteins / immunology,Neoplasms / immunology*,Neoplasms / pathology,Neoplastic / immunology*,Neoplastic / pathology,Neoplastic Stem Cells / immunology*,Neoplastic Stem Cells / pathology,Neprilysin / immunology*,Non-U.S. Gov't,PubMed Abstract,Receptor,Receptors,Research Support,Shicheng Su,Tumor Microenvironment / immunology*,doi:10.1016/j.cell.2018.01.009,pmid:29395328},
mendeley-groups = {PhD/Stromal cells},
month = {feb},
number = {4},
pages = {841--856.e16},
pmid = {29395328},
publisher = {Cell},
title = {{CD10 + GPR77 + Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness}},
url = {https://pubmed.ncbi.nlm.nih.gov/29395328/},
volume = {172},
year = {2018}
}

@Comment{jabref-meta: databaseType:bibtex;}
@article{Zhang2020,
abstract = {The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.},
author = {Zhang, Yuqing and Parmigiani, Giovanni and Johnson, W. Evan},
doi = {10.1093/NARGAB/LQAA078},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhang, Parmigiani, Johnson - 2020 - ComBat-seq batch effect adjustment for RNA-seq count data.pdf:pdf},
issn = {26319268},
journal = {NAR Genomics and Bioinformatics},
mendeley-groups = {PhD/RNA-seq},
month = {sep},
number = {3},
publisher = {Oxford Academic},
title = {{ComBat-seq: batch effect adjustment for RNA-seq count data}},
url = {https://academic.oup.com/nargab/article/2/3/lqaa078/5909519},
volume = {2},
year = {2020}
}
@article{Rassart2020,
abstract = {ApoD is a 25 to 30 kDa glycosylated protein, member of the lipocalin superfamily. As a transporter of several small hydrophobic molecules, its known biological functions are mostly associated to lipid metabolism and neuroprotection. ApoD is a multi-ligand, multi-function protein that is involved lipid trafficking, food intake, inflammation, antioxidative response and development and in different types of cancers. An important aspect of ApoD's role in lipid metabolism appears to involve the transport of arachidonic acid, and the modulation of eicosanoid production and delivery in metabolic tissues. ApoD expression in metabolic tissues has been associated positively and negatively with insulin sensitivity and glucose homeostasis in a tissue dependent manner. ApoD levels rise considerably in association with aging and neuropathologies such as Alzheimer's disease, stroke, meningoencephalitis, moto-neuron disease, multiple sclerosis, schizophrenia and Parkinson's disease. ApoD is also modulated in several animal models of nervous system injury/pathology.},
author = {Rassart, Eric and Desmarais, Frederik and Najyb, Ouafa and Bergeron, Karl F. and Mounier, Catherine},
doi = {10.1016/J.GENE.2020.144874},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rassart et al. - 2020 - Apolipoprotein D.pdf:pdf},
issn = {18790038},
journal = {Gene},
keywords = {Apolipoprotein D,Arachidonic acid,Lipid metabolism,Lipid transport,Lipocalin,Neuroprotection},
mendeley-groups = {PhD/Other},
month = {sep},
pages = {144874},
pmid = {32554047},
publisher = {NIH Public Access},
title = {{Apolipoprotein D}},
url = {/pmc/articles/PMC8011330/ /pmc/articles/PMC8011330/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011330/},
volume = {756},
year = {2020}
}
@article{Lurton1999,
abstract = {Fibroblasts are the major cell type responsible for synthesizing matrix constituents in lung and other connective tissues. Evidence indicates that fibroblasts are heterogeneous, and that subpopulat...},
author = {Lurton, J. and Rose, T. M. and Raghu, G. and Narayanan, A. S.},
doi = {10.1165/AJRCMB.20.2.3368},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lurton et al. - 2012 - Isolation of a Gene Product Expressed by a Subpopulation of Human Lung Fibroblasts by Differential Display.pdf:pdf},
issn = {10441549},
journal = {American Journal of Respiratory Cell and Molecular Biology},
mendeley-groups = {PhD/Stromal cells},
month = {dec},
number = {2},
pages = {327--331},
pmid = {9922225},
publisher = {American Thoracic SocietyNew York, NY},
title = {{Isolation of a Gene Product Expressed by a Subpopulation of Human Lung Fibroblasts by Differential Display}},
url = {www.atsjournals.org},
volume = {20},
year = {1999}
}
@article{Kang2021,
abstract = {TMEM176B is a member of the membrane spanning 4-domains (MS4) family of transmem-brane proteins, and a putative ion channel that is expressed in immune cells and certain cancers. We aimed to understand the role of TMEM176B in cancer cell signaling, gene expression, cell pro-liferation, and migration in vitro, as well as tumor growth in vivo. We generated breast cancer cell lines with overexpressed and silenced TMEM176B, and a therapeutic antibody targeting TMEM176B. Proliferation and migration assays were performed in vitro, and tumor growth was evaluated in vivo. We performed gene expression and Western blot analyses to identify the most differentially regulated genes and signaling pathways in cells with TMEM176B overexpression and silencing. Silencing TMEM176B or inhibiting it with a therapeutic antibody impaired cell proliferation, while over-expression increased proliferation in vitro. Syngeneic and xenograft tumor studies revealed the attenuated growth of tumors with TMEM176B gene silencing compared with controls. We found that the AKT/mTOR signaling pathway was activated or repressed in cells overexpressing or silenced for TMEM176B, respectively. Overall, our results suggest that TMEM176B expression in breast cancer cells regulates key signaling pathways and genes that contribute to cancer cell growth and progression, and is a potential target for therapeutic antibodies.},
author = {Kang, Chifei and Rostoker, Ran and Ben-Shumel, Sarit and Rashed, Rola and Duty, James Andrew and Demircioglu, Deniz and Antoniou, Irini M. and Isakov, Lika and Shen-Orr, Zila and Bravo-Cordero, Jose Javier and Kase, Nathan and Cuajungco, Math P. and Moran, Thomas M. and Leroith, Derek and Gallagher, Emily Jane},
doi = {10.3390/CELLS10123430/S1},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kang et al. - 2021 - Tmem176b regulates aktmtor signaling and tumor growth in triple-negative breast cancer.pdf:pdf},
issn = {20734409},
journal = {Cells},
keywords = {AKT/mTOR signaling,Calcium channel,RNA-seq,TMEM176B,Therapeutic antibodies,Triple negative breast cancer},
mendeley-groups = {PhD},
month = {dec},
number = {12},
pages = {3430},
pmid = {34943938},
publisher = {MDPI},
title = {{Tmem176b regulates akt/mtor signaling and tumor growth in triple-negative breast cancer}},
url = {/pmc/articles/PMC8700203/ /pmc/articles/PMC8700203/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700203/},
volume = {10},
year = {2021}
}
@article{Short2017,
abstract = {Selenium is a micronutrient essential to human health and has long been associated with cancer prevention. Functionally, these effects are thought to be mediated by a class of selenium-containing proteins known as selenoproteins. Indeed, many selenoproteins have antioxidant activity which can attenuate cancer development by minimizing oxidative insult and resultant DNA damage. However, oxidative stress is increasingly being recognized for its “double-edged sword” effect in tumorigenesis, whereby it can mediate both negative and positive effects on tumor growth depending on the cellular context. In addition to their roles in redox homeostasis, recent work has also implicated selenoproteins in key oncogenic and tumor-suppressive pathways. Together, these data suggest that the overall contribution of selenoproteins to tumorigenesis is complicated and may be affected by a variety of factors. In this review, we discuss what is currently known about selenoproteins in tumorigenesis with a focus on their contextual roles in cancer development, growth, and progression.},
author = {Short, Sarah P. and Williams, Christopher S.},
doi = {10.1016/BS.ACR.2017.08.002},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Short, Williams - 2017 - Selenoproteins in tumorigenesis and cancer progression.pdf:pdf},
issn = {21625557},
journal = {Advances in cancer research},
keywords = {Glutathione peroxidase,Selenium,Selenoprotein F,Selenoprotein P,Selenoproteins,Thioredoxin reductase},
mendeley-groups = {PhD},
pages = {49},
pmid = {29054422},
publisher = {NIH Public Access},
title = {{Selenoproteins in tumorigenesis and cancer progression}},
url = {/pmc/articles/PMC5819884/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819884/},
volume = {136},
year = {2017}
}
@article{Cheng2014,
abstract = {Pigment Epithelium Derived Factor (PEDF) is a secreted factor that has broad biological activities. It was first identified as a neurotrophic factor and later as the most potent natural antiangiogenic factor, a stem cell niche factor, and an inhibitor of cancer cell growth. Numerous animal models demonstrated its therapeutic value in treating blinding diseases and diverse cancer types. A long-standing challenge is to reveal how PEDF acts on its target cells and the identities of the cell-surface receptors responsible for its activities. Here we report the identification of transmembrane proteins PLXDC1 and PLXDC2 as cell-surface receptors for PEDF. Using distinct cellular models, we demonstrate their cell type-specific receptor activities through loss of function and gain of function studies. Our experiments suggest that PEDF receptors form homooligomers under basal conditions, and PEDF dissociates the homooligomer to activate the receptors. Mutations in the intracellular domain can have profound effects on receptor activities.},
author = {Cheng, Guo and Zhong, Ming and Kawaguchi, Riki and Kassai, Miki and Al-Ubaidi, Muayyad and Deng, Jun and Ter-Stepanian, Mariam and Sun, Hui},
doi = {10.7554/ELIFE.05401},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cheng et al. - 2014 - Identification of PLXDC1 and PLXDC2 as the transmembrane receptors for the multifunctional factor PEDF.pdf:pdf},
issn = {2050084X},
journal = {eLife},
keywords = {biochemistry,cell biology,human disease,membrane receptors,none,signal transduction},
mendeley-groups = {PhD},
pages = {e05401},
pmid = {25535841},
publisher = {eLife Sciences Publications, Ltd},
title = {{Identification of PLXDC1 and PLXDC2 as the transmembrane receptors for the multifunctional factor PEDF}},
url = {/pmc/articles/PMC4303762/ /pmc/articles/PMC4303762/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303762/},
volume = {3},
year = {2014}
}
@article{Wetzig2013,
abstract = {Background: Mesenchymal stem cells have properties that make them amenable to therapeutic use. However, the acceptance of mesenchymal stem cells in clinical practice requires standardized techniques for their specific isolation. To date, there are no conclusive marker (s) for the exclusive isolation of mesenchymal stem cells. Our aim was to identify markers differentially expressed between mesenchymal stem cell and non-stem cell mesenchymal cell cultures. We compared and contrasted the phenotype of tissue cultures in which mesenchymal stem cells are rich and rare. By initially assessing mesenchymal stem cell differentiation, we established that bone marrow and breast adipose cultures are rich in mesenchymal stem cells while, in our hands, foreskin fibroblast and olfactory tissue cultures contain rare mesenchymal stem cells. In particular, olfactory tissue cells represent non-stem cell mesenchymal cells. Subsequently, the phenotype of the tissue cultures were thoroughly assessed using immuno-fluorescence, flow-cytometry, proteomics, antibody arrays and qPCR. Results: Our analysis revealed that all tissue cultures, regardless of differentiation potential, demonstrated remarkably similar phenotypes. Importantly, it was also observed that common mesenchymal stem cell markers, and fibroblast-associated markers, do not discriminate between mesenchymal stem cell and non-stem cell mesenchymal cell cultures. Examination and comparison of the phenotypes of mesenchymal stem cell and non-stem cell mesenchymal cell cultures revealed three differentially expressed markers - CD24, CD108 and CD40.Conclusion: We indicate the importance of establishing differential marker expression between mesenchymal stem cells and non-stem cell mesenchymal cells in order to determine stem cell specific markers. {\textcopyright} 2013 Wetzig et al.; licensee BioMed Central Ltd.},
author = {Wetzig, Andrew and Alaiya, Ayodele and Al-Alwan, Monther and Pradez, Christian B. and Pulicat, Manogaran S. and Al-Mazrou, Amer and Shinwari, Zakia and Sleiman, Ghida M. and Ghebeh, Hazem and Al-Humaidan, Hind and Gaafar, Ameera and Kanaan, Imaduddin and Adra, Chaker},
doi = {10.1186/1471-2121-14-54},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wetzig et al. - 2013 - Differential marker expression by cultures rich in mesenchymal stem cells.pdf:pdf},
issn = {14712121},
journal = {BMC Cell Biology},
keywords = {Bone marrow mesenchymal stem cell,Breast adipose stem cell,Cell surface markers,Fibroblasts and olfactory,Mesenchymal stem cell},
mendeley-groups = {PhD/Stromal cells},
month = {dec},
number = {1},
pages = {54},
pmid = {24304471},
publisher = {BioMed Central},
title = {{Differential marker expression by cultures rich in mesenchymal stem cells}},
url = {/pmc/articles/PMC4235221/ /pmc/articles/PMC4235221/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235221/},
volume = {14},
year = {2013}
}
@article{Liao2021,
abstract = {Breast cancer is the leading cause of cancer-related deaths in women worldwide. Several studies have indicated that abnormal chondroitin sulfate (CS) chains accumulate in breast cancer tissues; however, the functions and dysregulation of CS synthases are largely unknown. Here, we demonstrate that chondroitin polymerising factor (CHPF) is frequently upregulated in breast cancer tissues and that its high expression is positively associated with tumor metastasis, high stages, and short survival time. CHPF modulates CS formation in breast cancer cells. Additionally, we found that CHPF promotes tumor growth and metastasis accompanied by an increase in G-CSF levels and the number of myeloid-derived suppressor cells in tumor tissue. We revealed that tumor cell-derived G-CSF is co-localised with CS on the cell surface. Interestingly, our study is the first to identify that syndecan-4 (SDC4) is modified by CHPF and that it is involved in CHPF-mediated phenotypes. Moreover, breast cancer patients with high expression of both SDC4 and CHPF had worse overall survival compared to other subsets, which implied the synergistic effects of these two genes. In summary, our results indicated that the upregulation of CHPF in breast cancer contributes to the malignant behaviour of cancer cells, thereby providing novel insights on the significance of CHPF-modified SDC4 in breast cancer pathogenesis.},
author = {Liao, Wen-Chieh and Yen, Hung-Rong and Chen, Chia-Hua and Chu, Yin-Hung and Song, Ying-Chyi and Tseng, To-Jung and Liu, Chiung-Hui},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Liao et al. - 2021 - CHPF promotes malignancy of breast cancer cells by modifying syndecan-4 and the tumor microenvironment.pdf:pdf},
issn = {2156-6976},
journal = {American Journal of Cancer Research},
keywords = {Breast cancer,CHPF,chondroitin sulfate,syndecan-4,tumor microenvironment},
mendeley-groups = {PhD/Stromal cells},
number = {3},
pages = {812},
pmid = {33791155},
publisher = {e-Century Publishing Corporation},
title = {{CHPF promotes malignancy of breast cancer cells by modifying syndecan-4 and the tumor microenvironment}},
url = {/pmc/articles/PMC7994168/ /pmc/articles/PMC7994168/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994168/},
volume = {11},
year = {2021}
}
@article{Dong2021,
abstract = {Cell migration-inducing hyaluronidase 1 (CEMIP), a Wnt-related protein and also known as KIAA1199, is implicated in the process of metastatic colonization in a variety of malignant tumors, including breast cancer (BC), which is one of the most frequently diagnosed tumors in women worldwide. In this study, multiple public databases, online analytical tools, and bioinformatics approaches were applied to explore the expression levels, regulatory mechanisms, and biological functions of CEMIP in BC. We illustrated that CEMIP was highly expressed in various kinds of carcinomas, including BC, especially advanced subtypes, and predicted less favorable prognosis (negatively associated with overall survival) in BC patients, which might be an independent prognostic factor. Then, we revealed that the mutation and high expression of CEMIP might lead to it as an oncogene. We also demonstrated that TP53 mutation, DNA hypo-methylation, and the expression changes of three potential upstream transcription factors (EZH2, EGR1, and JUN) of CEMIP were likely to cause the hyperexpression of CEMIP in BC. Moreover, our findings suggested that CEMIP might exert its carcinogenic roles in the tumor microenvironment via participation in the extracellular matrix formation, increasing cancer-associated fibroblast (CAF), M2 macrophage, and neutrophil infiltration and decreasing CD8+ T cell infiltration. In summary, our study provided more solid evidence for CEMIP as a prognostic and metastatic biomarker and a potential therapeutic target in BC. Of course, these findings also need more confirmations of basic experiments and further clinical trials in the future.},
author = {Dong, Xingxing and Yang, Yalong and Yuan, Qianqian and Hou, Jinxuan and Wu, Gaosong},
doi = {10.3389/FGENE.2021.768140/BIBTEX},
issn = {16648021},
journal = {Frontiers in Genetics},
keywords = {CEMIP,biomarker,breast cancer,prognosis,tumor microenvironment},
mendeley-groups = {PhD/Stromal cells},
month = {dec},
pages = {2512},
publisher = {Frontiers Media S.A.},
title = {{High Expression of CEMIP Correlates Poor Prognosis and the Tumur Microenvironment in Breast Cancer as a Promisingly Prognostic Biomarker}},
volume = {12},
year = {2021}
}
@article{Gaud2011,
abstract = {Introduction Tumour progression is a complex multistep process that depends on an evolving crosstalk between cancer cells and the surrounding stromal tissue. The microenvironment is now recognized as having a pivotal role in promoting cancer initiation, progression and dissemination to form metastases [1]. The invasion process involves extracellular matrix (ECM)-degrading proteases, particularly matrix metalloproteinases (MMPs), that have been shown to be highly expressed and activated in the tumour microenvironment [2], especially in highly aggressive malignant tumours [3, 4]. Activated fibroblasts, the major cell component of the microenvironment, actively contribute to tumour invasiveness by secreting a consistent amount of MMPs at the tumour-stroma interface. Moreover, this MMP synthesis could be increased by the ECM metalloproteinase inducer (EMMPRIN) expressed by cancer cells. Abstract Tissue factor pathway inhibitor-2 (TFPI-2) is a potent inhibitor of plasmin which activates matrix metalloproteinases (MMPs) involved in degradation of the extracellular matrix. Its secretion in the tumour microenvironment makes TFPI-2 a potential inhibitor of tumour invasion and metastasis. As demonstrated in aggressive cancers, TFPI-2 is frequently down-regulated in cancer cells, but the mechanisms involved in the inhibition of tumour progression remained unclear. We showed in this study that stable TFPI-2 down-regulation in the National Cancer Institute (NCI)-H460 non-small cell lung cancer cell line using specific micro interfering micro-interfering RNA promoted tumour progression in a nude mice orthotopic model that resulted in an increase in cell invasion. Moreover, TFPI-2 down-regulation enhanced cell adhesion to collagen IV and laminin via an increase in 1 integrin on cell surface, and increased MMP expression (mainly MMP-1 and-3) contributing to cancer cell invasion through basement membrane components. This study also reveals for the first time that pulmonary fibroblasts incubated with conditioned media from TFPI-2 silencing cancer cells exhibited increased expression of MMPs, particularly MMP-1,-3 and-7, that are likely involved in lung cancer cell invasion through the surrounding stromal tissue, thus enhancing formation of metastases.},
author = {Gaud, Guillaume and Iochmann, Sophie and Guillon-Munos, Audrey and Brillet, Benjamin and Petiot, St{\'{e}}phanie and Seigneuret, Florian and Touz{\'{e}}, Antoine and Heuz{\'{e}}-Vourc'h, Nathalie and Courty, Yves and Lerondel, St{\'{e}}phanie and Gruel, Yves and Reverdiau, Pascale},
doi = {10.1111/J.1582-4934.2009.00989.X},
journal = {Journal of Cellular and Molecular Medicine},
mendeley-groups = {PhD/Stromal cells},
number = {2},
pages = {196},
pmid = {20015200},
publisher = {Wiley-Blackwell},
title = {{TFPI-2 silencing increases tumour progression and promotes metalloproteinase 1 and 3 induction through tumour-stromal cell interactions}},
url = {/pmc/articles/PMC3822788/ /pmc/articles/PMC3822788/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3822788/},
volume = {15},
year = {2011}
}
@article{Kim2017,
abstract = {Cancer-associated fibroblasts (CAFs) play important roles in cancer progression through their complex interactions with cancer cells. The secreted bone morphogenetic protein antagonist, gremlin1 (GREM1) is expressed by the CAFs of basal cell carcinomas (BCCs), and promotes the growth of cancer cells. In this study, we investigated the expression of GREM1 mRNAs in various benign and malignant skin tumors, including various BCC subtypes. Analysis by RNA in situ hybridization (ISH) revealed that fibroblasts in the scar tissue expressed GREM1 and $\alpha$-smooth muscle actin ($\alpha$-SMA), whereas resident fibroblasts in the dermis of the normal skin did not express GREM1. Real-time polymerase chain reaction analysis showed significantly higher GREM1 expression in skin cancers and pilomatricomas (PMCs) than in other benign skin tumors. Tissue microarrays analyzed by RNA ISH for GREM1 expression also demonstrated that 23{\%} of BCCs, 42{\%} of squamous cell carcinomas, 20{\%} of melanomas, and 90{\%} of PMCs were positive for GREM1 expression, whereas trichoepitheliomas, eccrine poromas, hidradenomas, and spiradenomas were negative for GREM1 expression. Most BCCs that were GREM1 expression positive were of desmoplastic or mixed subtypes, and GREM1 expression was localized to activated myofibroblasts at the tumoral-stromal interface. Interestingly, most PMCs harbored GREM1-expressing fibroblasts, probably because of the inflammatory responses caused by foreign body reactions to keratin. Additionally, in BCCs, stromal GREM1 expression had a strong correlation with CD10 expression. In conclusion, GREM1 is frequently expressed by myofibroblasts in scars or in the stroma of basal cell carcinomas, suggesting that GREM1 expression can be a marker for activated myofibroblasts in the cancer stroma or in scar tissue.},
author = {Kim, Hye Sung and Shin, Myung Soo and Cheon, Min Seok and Kim, Jae Wang and Lee, Cheol and Kim, Woo Ho and Kim, Young Sill and Jang, Bo Gun},
doi = {10.1371/JOURNAL.PONE.0174565},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim et al. - 2017 - GREM1 is expressed in the cancer-associated myofibroblasts of basal cell carcinomas.pdf:pdf},
issn = {19326203},
journal = {PLoS ONE},
mendeley-groups = {PhD/Stromal cells},
month = {mar},
number = {3},
pmid = {28346486},
publisher = {Public Library of Science},
title = {{GREM1 is expressed in the cancer-associated myofibroblasts of basal cell carcinomas}},
url = {/pmc/articles/PMC5367809/ /pmc/articles/PMC5367809/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367809/},
volume = {12},
year = {2017}
}
@article{Ren2019,
abstract = {Background: Bone morphogenetic proteins (BMPs) have been reported to maintain epithelial integrity and to antagonize the transforming growth factor $\beta$ (TGF$\beta$)-induced epithelial to mesenchymal transition. The expression of soluble BMP antagonists is dysregulated in cancers and interrupts proper BMP signaling in breast cancer. Methods: In this study, we mined the prognostic role of BMP antagonists GREMLIN 1 (GREM1) in primary breast cancer tissues using in-house and publicly available datasets. We determined which cells express GREM1 RNA using in situ hybridization (ISH) on a breast cancer tissue microarray. The effects of Grem1 on the properties of breast cancer cells were assessed by measuring the mesenchymal/stem cell marker expression and functional cell-based assays for stemness and invasion. The role of Grem1 in breast cancer-associated fibroblast (CAF) activation was measured by analyzing the expression of fibroblast markers, phalloidin staining, and collagen contraction assays. The role of Grem1 in CAF-induced breast cancer cell intravasation and extravasation was studied by utilizing xenograft zebrafish breast cancer (co-) injection models. Results: Expression analysis of clinical breast cancer datasets revealed that high expression of GREM1 in breast cancer stroma is correlated with a poor prognosis regardless of the molecular subtype. The large majority of human breast cancer cell lines did not express GREM1 in vitro, but breast CAFs did express GREM1 both in vitro and in vivo. Transforming growth factor $\beta$ (TGF$\beta$) secreted by breast cancer cells, and also inflammatory cytokines, stimulated GREM1 expression in CAFs. Grem1 abrogated bone morphogenetic protein (BMP)/SMAD signaling in breast cancer cells and promoted their mesenchymal phenotype, stemness, and invasion. Moreover, Grem1 production by CAFs strongly promoted the fibrogenic activation of CAFs and promoted breast cancer cell intravasation and extravasation in co-injection xenograft zebrafish models. Conclusions: Our results demonstrated that Grem1 is a pivotal factor in the reciprocal interplay between breast cancer cells and CAFs, which promotes cancer cell invasion. Targeting Grem1 could be beneficial in the treatment of breast cancer patients with high Grem1 expression.},
author = {Ren, Jiang and Smid, Marcel and Iaria, Josephine and Salvatori, Daniela C.F. and {Van Dam}, Hans and Zhu, Hong Jian and Martens, John W.M. and {Ten Dijke}, Peter},
doi = {10.1186/S13058-019-1194-0/FIGURES/7},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ren et al. - 2019 - Cancer-associated fibroblast-derived Gremlin 1 promotes breast cancer progression.pdf:pdf},
issn = {1465542X},
journal = {Breast Cancer Research},
keywords = {Breast cancer,Cancer-associated fibroblasts,Gremlin 1,Invasion,Zebrafish},
mendeley-groups = {PhD/Stromal cells},
month = {sep},
number = {1},
pages = {1--19},
pmid = {31533776},
publisher = {BioMed Central Ltd.},
title = {{Cancer-associated fibroblast-derived Gremlin 1 promotes breast cancer progression}},
url = {https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-019-1194-0},
volume = {21},
year = {2019}
}
@article{Foroutan2018,
abstract = {Background: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore ). Results: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (N S{\textless}25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.},
author = {Foroutan, Momeneh and Bhuva, Dharmesh D. and Lyu, Ruqian and Horan, Kristy and Cursons, Joseph and Davis, Melissa J.},
doi = {10.1186/S12859-018-2435-4/FIGURES/2},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Foroutan et al. - 2018 - Single sample scoring of molecular phenotypes.pdf:pdf},
issn = {14712105},
journal = {BMC Bioinformatics},
keywords = {Dimensional reduction,Gene set enrichment,Gene set score,Gene signature,Molecular features,Molecular phenotypes,Personalised medicine,Single sample,Singscore,Transcriptome},
mendeley-groups = {PhD/RNA-seq},
month = {nov},
number = {1},
pages = {1--10},
pmid = {30400809},
publisher = {BioMed Central Ltd.},
title = {{Single sample scoring of molecular phenotypes}},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2435-4},
volume = {19},
year = {2018}
}
@article{Bhuva2019,
abstract = {Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.},
author = {Bhuva, Dharmesh D. and Foroutan, Momeneh and Xie, Yi and Lyu, Ruqian and Cursons, Joseph and Davis, Melissa J.},
doi = {10.12688/F1000RESEARCH.19236.3},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bhuva et al. - 2019 - Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures.pdf:pdf},
issn = {1759796X},
journal = {F1000Research},
keywords = {AML mutations,Gene set scoring,Mutation prediction,NPM1c mutation,Signature scoring,Single sample,TCGA},
mendeley-groups = {PhD/RNA-seq},
month = {jun},
pmid = {31723419},
publisher = {Faculty of 1000 Ltd},
title = {{Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures}},
url = {/pmc/articles/PMC6844140/ /pmc/articles/PMC6844140/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6844140/},
volume = {8},
year = {2019}
}