Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

Cell - Tập 174 - Trang 1293-1308.e36 - 2018
Elham Azizi1, Ambrose J. Carr1,2, George Plitas3,4,5,6, Andrew E. Cornish1, Catherine Konopacki3,4, Sandhya Prabhakaran1, Juozas Nainys2,7, Kenmin Wu3,4,5, Vaidotas Kiseliovas1,7, Manu Setty1, Kristy Choi2, Rachel M. Fromme6, Phuong Dao1, Peter T. McKenney4,8, Ruby C. Wasti8, Krishna Kadaveru8, Linas Mazutis1, Alexander Y. Rudensky3,4,5, Dana Pe’er1,9
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
2Department of Biological Sciences, Columbia University, New York, NY, USA
3Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
4Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
5Ludwig Center at Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
6Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
7Sector of Microtechnologies, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
8Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, CT 06877, USA
9Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

Tài liệu tham khảo

Amir, 2013, viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nat. Biotechnol., 31, 545, 10.1038/nbt.2594 Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Bhattacharyya, 1990, On a geometrical representation of probability distributions and its use in statistical inference, Calcutta Statist. Assoc. Bull., 40, 23, 10.1177/0008068319900504 Bray, 2016, Near-optimal probabilistic RNA-seq quantification, Nat. Biotechnol., 34, 525, 10.1038/nbt.3519 Chevrier, 2017, An immune atlas of clear cell renal cell carcinoma, Cell, 169, 736, 10.1016/j.cell.2017.04.016 Coifman, 2005, Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps, Proc. Natl. Acad. Sci. USA, 102, 7426, 10.1073/pnas.0500334102 Dobin, 2013, STAR: ultrafast universal RNA-seq aligner, Bioinformatics, 29, 15, 10.1093/bioinformatics/bts635 Dushyanthen, 2015, Relevance of tumor-infiltrating lymphocytes in breast cancer, BMC Med., 13, 202, 10.1186/s12916-015-0431-3 Engblom, 2016, The role of myeloid cells in cancer therapies, Nat. Rev. Cancer, 16, 447, 10.1038/nrc.2016.54 Faircloth, 2012, Not all sequence tags are created equal: designing and validating sequence identification tags robust to indels, PLoS One, 7, e42543, 10.1371/journal.pone.0042543 Fan, 2016, Hallmarks of tissue-resident lymphocytes, Cell, 164, 1198, 10.1016/j.cell.2016.02.048 Finck, 2013, Normalization of mass cytometry data with bead standards, Cytometry A, 83, 483, 10.1002/cyto.a.22271 Finger, 2010, Hypoxia, inflammation, and the tumor microenvironment in metastatic disease, Cancer Metastasis Rev., 29, 285, 10.1007/s10555-010-9224-5 Franklin, 2014, The cellular and molecular origin of tumor-associated macrophages, Science, 344, 921, 10.1126/science.1252510 Gaublomme, 2015, Single-cell genomics unveils critical regulators of Th17 cell pathogenicity, Cell, 163, 1400, 10.1016/j.cell.2015.11.009 Görür, 2010, Dirichlet process gaussian mixture models: choice of the base distribution, J. Comput. Sci. Technol., 25, 653, 10.1007/s11390-010-9355-8 Haghverdi, 2015, Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics, 31, 2989, 10.1093/bioinformatics/btv325 Hartigan, 1985, The dip test of unimodality, Ann. Stat., 13, 70, 10.1214/aos/1176346577 Jaitin, 2014, Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types, Science, 343, 776, 10.1126/science.1247651 Jebara, 2004, Probability product kernels, J. Mach. Learn. Res., 5, 819 Jeffrey, 2006, Positive regulation of immune cell function and inflammatory responses by phosphatase PAC-1, Nat. Immunol., 7, 274, 10.1038/ni1310 Jiménez-Sánchez, 2017, Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient, Cell, 170, 927, 10.1016/j.cell.2017.07.025 Joller, 2014, Treg cells expressing the coinhibitory molecule TIGIT selectively inhibit proinflammatory Th1 and Th17 cell responses, Immunity, 40, 569, 10.1016/j.immuni.2014.02.012 Josefowicz, 2012, Regulatory T cells: mechanisms of differentiation and function, Annu. Rev. Immunol., 30, 531, 10.1146/annurev.immunol.25.022106.141623 Klein, 2015, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell, 161, 1187, 10.1016/j.cell.2015.04.044 Lavin, 2017, Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses, Cell, 169, 750, 10.1016/j.cell.2017.04.014 Levine, 2015, Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis, Cell, 162, 184, 10.1016/j.cell.2015.05.047 Li, 2011, RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome, BMC Bioinformatics, 12, 323, 10.1186/1471-2105-12-323 Mantovani, 2013, Tumor-associated macrophages as a paradigm of macrophage plasticity, diversity, and polarization: lessons and open questions, Arterioscler. Thromb. Vasc. Biol., 33, 1478, 10.1161/ATVBAHA.113.300168 Martinez, 2014, The M1 and M2 paradigm of macrophage activation: time for reassessment, F1000Prime Rep., 6, 13, 10.12703/P6-13 Müller, 2017, Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment, Genome Biol., 18, 234, 10.1186/s13059-017-1362-4 Murphy, 2007, Conjugate Bayesian analysis of the Gaussian distribution, def, 1, 16 Novershtern, 2011, Densely interconnected transcriptional circuits control cell states in human hematopoiesis, Cell, 144, 296, 10.1016/j.cell.2011.01.004 Pauken, 2015, Overcoming T cell exhaustion in infection and cancer, Trends Immunol., 36, 265, 10.1016/j.it.2015.02.008 Perdiguero, 2016, The development and maintenance of resident macrophages, Nat. Immunol., 17, 2, 10.1038/ni.3341 Philip, 2017, Chromatin states define tumour-specific T cell dysfunction and reprogramming, Nature, 545, 452, 10.1038/nature22367 Pitman, J. (2002). Combinatorial stochastic processes. http://statistics.berkeley.edu/tech-reports/621. Plitas, 2016, Regulatory T cells exhibit distinct features in human breast cancer, Immunity, 45, 1122, 10.1016/j.immuni.2016.10.032 Prabhakaran, S., Azizi, E., Carr, A., and Pe’er, D. (2016). Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. In Proceedings of the 33rd International Conference on Machine Learning, B. Maria Florina, and Q.W. Kilian, eds. (Proceedings of Machine Learning Research: PMLR), pp. 1070–1079. Şenbabaoğlu, 2016, Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures, Genome Biol., 17, 231, 10.1186/s13059-016-1092-z Singer, 2016, A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells, Cell, 166, 1500, 10.1016/j.cell.2016.08.052 Sun, 2017, Between-region genetic divergence reflects the mode and tempo of tumor evolution, Nat. Genet., 49, 1015, 10.1038/ng.3891 Tanaka, 2017, Regulatory T cells in cancer immunotherapy, Cell Res., 27, 109, 10.1038/cr.2016.151 Tao, 2006, On random±1 matrices: singularity and determinant, Random Structures Algorithms, 28, 1, 10.1002/rsa.20109 Tirosh, 2016, Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq, Science, 352, 189, 10.1126/science.aad0501 Topalian, 2015, Immune checkpoint blockade: a common denominator approach to cancer therapy, Cancer Cell, 27, 450, 10.1016/j.ccell.2015.03.001 van der Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579 Verdegaal, 2016, Neoantigen landscape dynamics during human melanoma-T cell interactions, Nature, 536, 91, 10.1038/nature18945 Wherry, 2015, Molecular and cellular insights into T cell exhaustion, Nat. Rev. Immunol., 15, 486, 10.1038/nri3862 Yates, 2015, Subclonal diversification of primary breast cancer revealed by multiregion sequencing, Nat. Med., 21, 751, 10.1038/nm.3886 Zemmour, 2018, Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR, Nat. Immunol., 19, 291, 10.1038/s41590-018-0051-0 Zheng, 2017, Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing, Cell, 169, 1342, 10.1016/j.cell.2017.05.035 Zilionis, 2017, Single-cell barcoding and sequencing using droplet microfluidics, Nat. Protoc., 12, 44, 10.1038/nprot.2016.154 Zunder, 2015, Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm, Nat Protoc, 10, 316, 10.1038/nprot.2015.020