Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
暂无分享,去创建一个
Jeong Eon Lee | W. Han | Z. Kan | J. Lee | Yeon-Hee Park | W. Park | H. Ryu | Woosung Chung | Hye Hyeon Eum | Hae-Ock Lee | Kyung-Min Lee | Han-Byoel Lee | Kyu-Tae Kim | Sangmin Kim | Kyung-min Lee | Kyu‐Tae Kim | Zhengyan Kan
[1] R. Sutherland,et al. c-Myc or Cyclin D1 Mimics Estrogen Effects on Cyclin E-Cdk2 Activation and Cell Cycle Reentry , 1998, Molecular and Cellular Biology.
[2] Ash A. Alizadeh,et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays , 1999, Nature Genetics.
[3] C K Osborne,et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[4] Carsten O. Peterson,et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.
[5] Andrea Califano,et al. Transcriptional analysis of the B cell germinal center reaction , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[6] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[7] Alberto Mantovani,et al. Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression1 , 2006, The Journal of Immunology.
[8] C. Liu,et al. Targeting tumor-associated macrophages as a novel strategy against breast cancer. , 2006, The Journal of clinical investigation.
[9] Z. Trajanoski,et al. Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome , 2006, Science.
[10] M. Probst-Kepper,et al. Signatures of human regulatory T cells: an encounter with old friends and new players , 2006, Genome Biology.
[11] Gianluca Bontempi,et al. Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes , 2008, Clinical Cancer Research.
[12] C. Sotiriou,et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures , 2007, Breast Cancer Research.
[13] L. V. van't Veer,et al. Clinical application of the 70-gene profile: the MINDACT trial. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[14] S. Paik,et al. Development of the 21-gene assay and its application in clinical practice and clinical trials. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[15] F. Pépin,et al. Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.
[16] Thomas D. Wu,et al. Genetic Alterations and Oncogenic Pathways Associated with Breast Cancer Subtypes , 2009, Molecular Cancer Research.
[17] Richard Durbin,et al. Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .
[18] Renaud Gaujoux,et al. A flexible R package for nonnegative matrix factorization , 2010, BMC Bioinformatics.
[19] I. Ellis,et al. Triple-Negative Breast Cancer: Distinguishing between Basal and Nonbasal Subtypes , 2009, Clinical Cancer Research.
[20] Peter Olson,et al. Cancer-Associated Fibroblasts Are Activated in Incipient Neoplasia to Orchestrate Tumor-Promoting Inflammation in an NF-kappaB-Dependent Manner. , 2010, Cancer cell.
[21] M. DePristo,et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.
[22] J. Baselga,et al. Targeted therapies for breast cancer. , 2011, The Journal of clinical investigation.
[23] Rachel Schiff,et al. Mechanisms of endocrine resistance in breast cancer. , 2011, Annual review of medicine.
[24] X. Chen,et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. , 2011, The Journal of clinical investigation.
[25] I. Ellis,et al. The prognostic significance of B lymphocytes in invasive carcinoma of the breast , 2012, Breast Cancer Research and Treatment.
[26] Colin N. Dewey,et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.
[27] Justin Guinney,et al. GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.
[28] Xi Chen,et al. TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer , 2012, Cancer informatics.
[29] Tatiana Popova,et al. Supplementary Methods , 2012, Acta Neuropsychiatrica.
[30] F. Markowetz,et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.
[31] Chris Williams,et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..
[32] John Quackenbush,et al. A three-gene model to robustly identify breast cancer molecular subtypes. , 2012, Journal of the National Cancer Institute.
[33] A. Sivachenko,et al. Sequence analysis of mutations and translocations across breast cancer subtypes , 2012, Nature.
[34] Steven J. M. Jones,et al. Comprehensive molecular portraits of human breast tumors , 2012, Nature.
[35] G. Getz,et al. Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.
[36] R. Clarke,et al. Recent advances reveal IL-8 signaling as a potential key to targeting breast cancer stem cells , 2013, Breast Cancer Research.
[37] Z. Trajanoski,et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. , 2013, Immunity.
[38] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[39] H. Schreiber,et al. Innate and adaptive immune cells in the tumor microenvironment , 2013, Nature Immunology.
[40] A. Sivachenko,et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.
[41] N. Navin,et al. Clonal Evolution in Breast Cancer Revealed by Single Nucleus Genome Sequencing , 2014, Nature.
[42] J. Hackney,et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8(+) T cell effector function. , 2014, Cancer cell.
[43] C. Sotiriou,et al. Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology , 2014, BMC Genomics.
[44] Jeffrey W Pollard,et al. Tumor-associated macrophages: from mechanisms to therapy. , 2014, Immunity.
[45] L. Nguyen,et al. Clinical blockade of PD1 and LAG3 — potential mechanisms of action , 2014, Nature Reviews Immunology.
[46] N. Neff,et al. Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.
[47] Shawn M. Gillespie,et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.
[48] Do-Hyun Nam,et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells , 2015, Genome Biology.
[49] B. Zhu,et al. T-cell exhaustion in the tumor microenvironment , 2015, Cell Death and Disease.
[50] N. Navin,et al. The first five years of single-cell cancer genomics and beyond , 2015, Genome research.
[51] Evan Z. Macosko,et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.
[52] Anne Vincent-Salomon,et al. Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification , 2015, Genome Biology.
[53] J. Wolchok,et al. Immune Checkpoint Blockade in Cancer Therapy. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[54] Daniel E. Speiser,et al. Inhibitory Receptors Beyond T Cell Exhaustion , 2015, Front. Immunol..
[55] Chih-Yang Wang,et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells , 2015, Nature.
[56] Kun Zhang,et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues , 2015, Nature Protocols.
[57] T. Nielsen,et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.
[58] N. Navin. Delineating cancer evolution with single-cell sequencing , 2015, Science Translational Medicine.
[59] J. Rosenblatt,et al. B cell regulation of the anti-tumor response and role in carcinogenesis , 2016, Journal of Immunotherapy for Cancer.
[60] Charles H. Yoon,et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.