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% Encoding: UTF-8
@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,
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/ ).},
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.},
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},
issn = {1548-7105},
journal = {Nature Methods 2015 12:5},
keywords = {Computational biology and bioinformatics,Gene expression analysis,Immunology,Tumour heterogeneity},
mendeley-groups = {PhD/Deconvolution},
month = {mar},
number = {5},
pages = {453--457},
pmid = {25822800},
publisher = {Nature Publishing Group},
title = {{Robust enumeration of cell subsets from tissue expression profiles}},
url = {https://www.nature.com/articles/nmeth.3337},
volume = {12},
year = {2015}
}
@article{Huelsken2018,
abstract = {While functional heterogeneity of fibroblastic cells populating the tumor microenvironment is increasingly recognized, lack of definitive markers complicates elucidation of roles among ostensibly distinctive fibroblastic states. In this issue of Cell, Su et al. characterize a new pro-tumorigenic cancer-associated fibroblast subset mediating chemoresistance defined and driven by a novel signaling pathway. While functional heterogeneity of fibroblastic cells populating the tumor microenvironment is increasingly recognized, lack of definitive markers complicates elucidation of roles among ostensibly distinctive fibroblastic states. In this issue of Cell, Su et al. characterize a new pro-tumorigenic cancer-associated fibroblast subset mediating chemoresistance defined and driven by a novel signaling pathway.},
author = {Huelsken, Joerg and Hanahan, Douglas},
doi = {10.1016/J.CELL.2018.01.028},
file = {:home/kevin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Huelsken, Hanahan - 2018 - A Subset of Cancer-Associated Fibroblasts Determines Therapy Resistance.pdf:pdf},
issn = {0092-8674},
journal = {Cell},
mendeley-groups = {PhD/Stromal cells},
month = {feb},
number = {4},
pages = {643--644},
pmid = {29425485},
publisher = {Cell Press},
title = {{A Subset of Cancer-Associated Fibroblasts Determines Therapy Resistance}},
volume = {172},
year = {2018}
}
@Comment{jabref-meta: databaseType:bibtex;}