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• W2611596972 abstract "•Paired analysis reveals a unique tumor-immune signature independent of TNM stage•Depletion of CD141+ DC and enrichment of PPARγhi macrophages in early stage lung cancer•Reduced and impaired NK cells in early stage lung cancer•Harnessing TIM compartment may help enhance T cell immunotherapies To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies.Video Abstracthttps://www.cell.com/cms/asset/529568b8-1f54-4826-aa28-ca673e4fe8f8/mmc6.mp4Loading ...(mp4, 38.91 MB) Download video To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Current checkpoint blockade therapies mainly function to rescue T cells from exhaustion or deplete T regulatory cells (Treg). Studies have begun to dissect the details of T cell function and distribution in advanced tumor lesions to identify novel strategies to further strengthen anti-tumor T cell immunity. Myeloid cells, through their ability to present tumor-associated antigens to T cells and to produce critical T cell differentiation cytokines, have a unique ability to control T cell function at the tumor site. Yet, much less is known about the diversity of the myeloid compartment at the tumor site. Myeloid cells are a diverse population of immune cells that share the ability to sense and respond to tissue injuries by clearing damaged cells and by promoting the recruitment of immune effector cells that will help restore tissue integrity (Lavin and Merad, 2013Lavin Y. Merad M. Macrophages: gatekeepers of tissue integrity.Cancer Immunol. Res. 2013; 1: 201-209Crossref PubMed Scopus (55) Google Scholar, Pham, 2006Pham C.T. Neutrophil serine proteases: specific regulators of inflammation.Nat. Rev. Immunol. 2006; 6: 541-550Crossref PubMed Scopus (739) Google Scholar). Tumor-infiltrating myeloid cells (TIM) consist of granulocytes and mononuclear phagocytes at varying stages of differentiation and have been shown to contribute to shaping tumor progression and response to treatment (Engblom et al., 2016Engblom C. Pfirschke C. Pittet M.J. The role of myeloid cells in cancer therapies.Nat. Rev. Cancer. 2016; 16: 447-462Crossref PubMed Scopus (407) Google Scholar, Gabrilovich et al., 2012Gabrilovich D.I. Ostrand-Rosenberg S. Bronte V. Coordinated regulation of myeloid cells by tumours.Nat. Rev. Immunol. 2012; 12: 253-268Crossref PubMed Scopus (2524) Google Scholar). Of these, mononuclear phagocytes refer to monocytes, macrophages, and dendritic cells (DC), which, in addition to their innate immune function, share the ability to present tumor-associated antigens to T cells (Ginhoux and Jung, 2014Ginhoux F. Jung S. Monocytes and macrophages: developmental pathways and tissue homeostasis.Nat. Rev. Immunol. 2014; 14: 392-404Crossref PubMed Scopus (1134) Google Scholar). Among TIM, DC are the best equipped to drive T cell activation (Merad et al., 2013Merad M. Sathe P. Helft J. Miller J. Mortha A. The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting.Annu. Rev. Immunol. 2013; 31: 563-604Crossref PubMed Scopus (1520) Google Scholar), and a subset of DC, named CD103+ DC, was shown to control local CD8+ T cell activation (Broz et al., 2014Broz M.L. Binnewies M. Boldajipour B. Nelson A.E. Pollack J.L. Erle D.J. Barczak A. Rosenblum M.D. Daud A. Barber D.L. et al.Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity.Cancer Cell. 2014; 26: 638-652Abstract Full Text Full Text PDF PubMed Scopus (602) Google Scholar, Hildner et al., 2008Hildner K. Edelson B.T. Purtha W.E. Diamond M. Matsushita H. Kohyama M. Calderon B. Schraml B.U. Unanue E.R. Diamond M.S. et al.Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity.Science. 2008; 322: 1097-1100Crossref PubMed Scopus (1348) Google Scholar, Salmon et al., 2016Salmon H. Idoyaga J. Rahman A. Leboeuf M. Remark R. Jordan S. Casanova-Acebes M. Khudoynazarova M. Agudo J. Tung N. et al.Expansion and activation of CD103(+) dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition.Immunity. 2016; 44: 924-938Abstract Full Text Full Text PDF PubMed Scopus (630) Google Scholar, Sánchez-Paulete et al., 2016Sánchez-Paulete A.R. Cueto F.J. Martínez-López M. Labiano S. Morales-Kastresana A. Rodríguez-Ruiz M.E. Jure-Kunkel M. Azpilikueta A. Aznar M.A. Quetglas J.I. et al.Cancer immunotherapy with immunomodulatory anti-CD137 and anti-PD-1 monoclonal antibodies requires BATF3-dependent dendritic cells.Cancer Discov. 2016; 6: 71-79Crossref PubMed Scopus (277) Google Scholar). Thus, TIM composition appears to control tumor-infiltrating lymphocyte (TIL) composition, activation, and anti-tumor function, and harnessing the TIM compartment may provide a powerful synergistic strategy to potentiate T cell targeting immunotherapies. Currently, the majority of lung cancer cases are diagnosed at advanced stage. However, this is likely to change, as low-dose CT screening programs in populations at risk have shown benefits and are being widely implemented (Aberle et al., 2011Aberle D.R. Adams A.M. Berg C.D. Black W.C. Clapp J.D. Fagerstrom R.M. Gareen I.F. Gatsonis C. Marcus P.M. Sicks J.D. National Lung Screening Trial Research TeamReduced lung-cancer mortality with low-dose computed tomographic screening.N. Engl. J. Med. 2011; 365: 395-409Crossref PubMed Scopus (6878) Google Scholar, Black et al., 2015Black W.C. Keeler E.B. Soneji S.S. Cost-effectiveness of CT screening in the National Lung Screening Trial.N. Engl. J. Med. 2015; 372: 388PubMed Google Scholar). Five-year survival rates for patients with pathologic stage IA and IB non-small cell lung cancer (NSCLC) are only 83% and 71%, respectively, and these numbers drop to 50% for stage II disease (Goldstraw et al., 2016Goldstraw P. Chansky K. Crowley J. Rami-Porta R. Asamura H. Eberhardt W.E.E. Nicholson A.G. Groome P. Mitchell A. Bolejack V. International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee, Advisory Boards, and Participating InstitutionsInternational Association for the Study of Lung Cancer Staging and Prognostic Factors Committee Advisory Boards and Participating InstitutionsThe IASLC Lung Cancer Staging Project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer.J. Thorac. Oncol. 2016; 11: 39-51Abstract Full Text Full Text PDF PubMed Scopus (2299) Google Scholar) with minimal improvement from adjuvant chemotherapy (Pignon et al., 2008Pignon J.P. Tribodet H. Scagliotti G.V. Douillard J.Y. Shepherd F.A. Stephens R.J. Dunant A. Torri V. Rosell R. Seymour L. et al.LACE Collaborative GroupLung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group.J. Clin. Oncol. 2008; 26: 3552-3559Crossref PubMed Scopus (1786) Google Scholar). Preliminary data suggest impressive activity of neoadjuvant immunotherapy in a small number of early stage resectable lung NSCLC lesions treated with anti-PD-1 mAb blockade (Forde et al., 2016Forde P.M. Smith K.N. Chaft J.E. Hellmann M. Merghoub T. Wolchok J.D. Yang S.C. Battafarano R.J. Gabrielson E. Georgiades C.S. et al.Neoadjuvant anti-PD1, nivolumab, in early stage resectable non-small-cell lung cancer.ESMO 2016 Congress. 2016; 27: 1-36Google Scholar). Although these studies need to be confirmed in a larger cohort, these results are consistent with the notion that immunotherapy agents are most efficient at low tumor burden and in patients naïve of immunomodulatory chemotherapy agents. The design of immunomodulatory strategies for the treatment of NSCLC will, however, tremendously benefit from a detailed understanding of the immune cell landscape that develops specifically in response to tumor cues. To this end, we developed a multiscale immune profiling strategy to map the immune microenvironment of early lung adenocarcinoma lesions. As tissue cues significantly impact the biology of tissue-resident immune cells and specifically innate cells (Lavin et al., 2014Lavin Y. Winter D. Blecher-Gonen R. David E. Keren-Shaul H. Merad M. Jung S. Amit I. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment.Cell. 2014; 159: 1312-1326Abstract Full Text Full Text PDF PubMed Scopus (1324) Google Scholar), we designed a barcoding strategy that allowed the simultaneous single-cell analysis of the distinct immune cell compartments that reside in the tumor site, non-involved lung tissue (nLung), and blood of each patient to distinguish tumor-driven immune signatures from those caused by normal tissue-imprinting cues. Using mass cytometry by time-of-flight (CyTOF) combined with single-cell transcriptomics and multiplex tissue imaging of the lung tumor, we identify NK and myeloid cell responses that are unique to tumor lesions and absent from nLung or blood from the same patients. We show that these changes are present as early as in stage I tumors and likely compromise anti-tumor immunity. These data suggest that neoadjuvant immunotherapy strategies targeting innate immune cells in early lung adenocarcinoma lesions have the potential to reactivate the TIL microenvironment and transform tumor response to checkpoint blockade. To map the immune microenvironment of lung adenocarcinoma lesions, we designed a clinical multiscale immune profiling study of freshly resected tumors. Patients selected were treatment naïve at the time of surgery. For each patient, we obtained NSCLC tumor tissues including invasive margins, nLung tissue, and blood cells (Figure 1A; STAR Methods). Of 32 patients, 28 were diagnosed with lung adenocarcinoma. Patients were representative of the lung adenocarcinoma distribution across age, gender, mutational status, and predominant histological subtype (Table S1). To distinguish tumor-specific immune changes from the lung tissue immune environment, we sought to simultaneously map the immune compartment of the lung tumor lesion, the nLung tissue, and the peripheral blood. To this end, we developed a novel barcoding method that allows a simultaneous analysis of cells from all three sample types. In the first 18-patient cohort, immune cells isolated from the tumor lesion, nLung, and blood were barcoded with anti-CD45 antibodies (Ab) conjugated to unique metal isotopes before samples were pooled. Pooled samples were then stained with two panels of more than 30 antibodies each and analyzed by CyTOF, thus allowing the measurement of single-cell expression on immune cells residing in each tissue type of each patient (Figure 1A). The mass cytometry panels were subsequently extended to include cytokine measurement in a cohort of ten additional lung adenocarcinoma patients, as described in the STAR Methods. Using viSNE to visualize high-dimensional data in two dimensions while preserving single-cell resolution (Amir et al., 2013Amir A.D. Davis K.L. Tadmor M.D. Simonds E.F. Levine J.H. Bendall S.C. Shenfeld D.K. Krishnaswamy S. Nolan G.P. Pe’er D. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.Nat. Biotechnol. 2013; 31: 545-552Crossref PubMed Scopus (1098) Google Scholar), we analyzed the distribution of the different immune cell lineages that accumulated in lung tumor lesions, nLung, and blood circulation across patients (Table S1 and Data S1). A higher number of immune cells accumulated in the tumor tissues compared to nLung (Figure S1A). The tumor-resident immune cell compartment comprised all major immune lineages, and the most abundant were the T lymphocytes and mononuclear phagocytes (Figures 1B–1D, and S1B). Mononuclear phagocytes and granulocytes were equally represented in tumor lesions compared to nLung. In contrast, T and B lymphocytes were present at a higher frequency in the tumor microenvironment compared to the nLung, whereas the frequency of NK cells was significantly reduced across all lung adenocarcinoma patients examined (Figures 1C, 1D, and S1C). We also analyzed the cytokines and chemokines produced in the tumor milieu. Whereas many chemokines and cytokines were expressed at much higher levels in the tumors compared to the blood, similar chemokine levels were often detected in tumors and adjacent nLung, which could potentially reflect the local diffusion of tumor-produced soluble molecules to the adjacent lung tissue (Figures S1D and S1E). Interestingly, CX3CL1 (fractalkine) was expressed at slightly higher levels in tumors compared to nLung and correlated with the frequency of the mononuclear phagocyte infiltrate (Figure S1F). Recent studies have shown that, in addition to cell composition, the spatial distribution of immune cells at the tumor site may affect tumor outcome (Germain et al., 2014Germain C. Gnjatic S. Tamzalit F. Knockaert S. Remark R. Goc J. Lepelley A. Becht E. Katsahian S. Bizouard G. et al.Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer.Am. J. Respir. Crit. Care Med. 2014; 189: 832-844Crossref PubMed Scopus (409) Google Scholar). Using a new tissue-profiling method named “multiplexed immunohistochemical consecutive staining on a single slide” (MICSSS) that we recently developed in the laboratory (Remark et al., 2016Remark R. Merghoub T. Grabe N. Litjens G. Damotte D. Wolchok J.D. Merad M. Gnjatic S. In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide.Science Immunology. 2016; 1: aaf6925Crossref PubMed Scopus (96) Google Scholar), we assessed the distribution of immune cells in the tumor and nLung sections. Immune cells accumulated mainly in the stroma and invasive margin surrounding the tumor islets as previously described (Salmon et al., 2012Salmon H. Franciszkiewicz K. Damotte D. Dieu-Nosjean M.C. Validire P. Trautmann A. Mami-Chouaib F. Donnadieu E. Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors.J. Clin. Invest. 2012; 122: 899-910Crossref PubMed Scopus (554) Google Scholar, Turley et al., 2015Turley S.J. Cremasco V. Astarita J.L. Immunological hallmarks of stromal cells in the tumour microenvironment.Nat. Rev. Immunol. 2015; 15: 669-682Crossref PubMed Scopus (627) Google Scholar), although some macrophages and T cells were able to infiltrate into the tumors (Figures 1E and S1I). We also observed an abundance of mononuclear phagocytes and T cells at the tumor site across patients supporting the CyTOF results (Figures 1D, 1E, and S1B). Importantly, many patients had tertiary lymphoid structures (TLS), which accumulated near the tumor invasive margin and were absent from nLung (Figures 1F and S1I). Tumor lesions enriched in TLS had significantly more T lymphocytes and fewer mononuclear phagocytes as measured by CyTOF at the tumor site (Figure S1H). To further probe the nature of the immune response induced at the tumor site in an unbiased manner, we analyzed the CyTOF data using the Phenograph algorithm to systematically identify common cellular communities across the three tissues and across all patients (Levine et al., 2015Levine J.H. Simonds E.F. Bendall S.C. Davis K.L. Amir A.D. Tadmor M.D. Litvin O. Fienberg H.G. Jager A. Zunder E.R. et al.Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis.Cell. 2015; 162: 184-197Abstract Full Text Full Text PDF PubMed Scopus (973) Google Scholar). We first clustered single cells based on shared protein expression across tumor lesions, nLung, and peripheral blood mononuclear cells (PBMCs) within each patient then merged clusters from each patient using a secondary clustering analysis to identify metaclusters common across patients and tissues (Figure 1A). Phenograph clustering across tissues and across all patients revealed distinct T lymphoid metaclusters (Figures 2A, 2B, and S2A) that corresponded to known immune cell populations and had a unique distribution across tissue sites (Figures 2C and S2B).Figure S2Tregs Upregulate Immunosuppressive Molecules at the Tumor Site, Related to Figure 2Show full caption(A) viSNE plots of CD3+ single cells across tissues showing normalized expression of 30 indicated markers of a representative patient.(B) Bar plots showing frequencies across tissues for remaining metaclusters from Figure 2B (n=18).(C) Ratio of CD8+ GranzymeB+ T cells to Tregs metacluster frequencies across tissue (n=18).(D) Normalized expression of ICOS, 41BB, and CD38 on tumor CD3+ cells shown on viSNE plots for a representative patient (left) and bar pots showing normalized expression across patients for indicated metaclusters in tumor (n=18) (right).(E) Heatmap of CD8- T cell bulk sequencing normalized UMI counts grouped by tissues across 6 patients (left). Scatter plot showing relative expression level of genes differentially expressed between nLung and tumor, with genes significantly different between tissue colored red (p<0.01, log2|FC|>1).(F) Heat map illustrating single cell marker expression on Tregs from mass cytometry in a representative patient. Each row represents a single Treg cell and are grouped by tissue (left). Bar plots stratifying the normalized expression of indicated proteins in Tregs across tissue (right; n=18).(G) Bar plots show the normalized expression of Granzyme B (top) and IFNγ (bottom) on indicated T cell metaclusters for 9 patient stratified by tissue type upon stimulation.(H) Bar plots showing normalized expression of PD-1 found on CD4+ and CD8+ T cells in 10 lung adenocarcinoma patients.(I) MICSSS for CD8 and CD20 showing a TLS in tumor of a representative patient.(J) Frequency of CD8+ PD-1+ T cells in nLung to overall TCR repertoire clonality correlation plot (Spearman’s rank-based correlation).Bar plots show mean ± SEM; ∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) viSNE plots of CD3+ single cells across tissues showing normalized expression of 30 indicated markers of a representative patient. (B) Bar plots showing frequencies across tissues for remaining metaclusters from Figure 2B (n=18). (C) Ratio of CD8+ GranzymeB+ T cells to Tregs metacluster frequencies across tissue (n=18). (D) Normalized expression of ICOS, 41BB, and CD38 on tumor CD3+ cells shown on viSNE plots for a representative patient (left) and bar pots showing normalized expression across patients for indicated metaclusters in tumor (n=18) (right). (E) Heatmap of CD8- T cell bulk sequencing normalized UMI counts grouped by tissues across 6 patients (left). Scatter plot showing relative expression level of genes differentially expressed between nLung and tumor, with genes significantly different between tissue colored red (p<0.01, log2|FC|>1). (F) Heat map illustrating single cell marker expression on Tregs from mass cytometry in a representative patient. Each row represents a single Treg cell and are grouped by tissue (left). Bar plots stratifying the normalized expression of indicated proteins in Tregs across tissue (right; n=18). (G) Bar plots show the normalized expression of Granzyme B (top) and IFNγ (bottom) on indicated T cell metaclusters for 9 patient stratified by tissue type upon stimulation. (H) Bar plots showing normalized expression of PD-1 found on CD4+ and CD8+ T cells in 10 lung adenocarcinoma patients. (I) MICSSS for CD8 and CD20 showing a TLS in tumor of a representative patient. (J) Frequency of CD8+ PD-1+ T cells in nLung to overall TCR repertoire clonality correlation plot (Spearman’s rank-based correlation). Bar plots show mean ± SEM; ∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test. Paired mass cytometry analysis revealed a distinct composition and phenotype of T cell subsets (Figures 2C and S2B). Specifically, Treg were significantly increased in the tumor lesion across all patients even at early stages (Figure 2C). Importantly, Treg at the tumor sites expressed high levels of CTLA4, CD39, ICOS, and 41BB compared to other T cells (Figures 2D and S2D). They were also clearly distinguishable from Treg that resided in nLung based on higher expression of Foxp3, CTLA4, PD-1, CD39, ICOS, CD38, and 41BB, whereas CCR4 was decreased (Figures 2E, S2E, and S2F). In contrast, cytolytic CD8+ T cells were significantly reduced in frequency in the tumor compared to nLung and blood from the same patients (Figure 2C). Moreover, total CD8+ T cells present in tumors expressed significantly less granzyme B and IFNγ upon stimulation compared to their nLung counterparts (Figure S2G). A reduced T-effector/Treg ratio was thus a strong signature of the lung tumor lesion (Figure S2C). Given the success of anti-PD-1 checkpoint blockade in lung cancer patients, we examined the expression of the checkpoint molecule PD-1 on all T cell metaclusters identified across tissues. PD-1 was distinctly expressed on a small subset of CD4+ and CD8+ T cells that were a unique feature of the tumor (Figures 2C and S2H) and at a lower level on Treg at the tumor site (Figure 2D). CD8+ T cells accumulate at the tumor site and in TLS, where they preferentially expand (Figures 2F and S2I; Goc et al., 2014Goc J. Germain C. Vo-Bourgais T.K. Lupo A. Klein C. Knockaert S. de Chaisemartin L. Ouakrim H. Becht E. Alifano M. et al.Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells.Cancer Res. 2014; 74: 705-715Crossref PubMed Scopus (340) Google Scholar, Joshi et al., 2015Joshi N.S. Akama-Garren E.H. Lu Y. Lee D.Y. Chang G.P. Li A. DuPage M. Tammela T. Kerper N.R. Farago A.F. et al.Regulatory T cells in tumor-associated tertiary lymphoid structures suppress anti-tumor T cell responses.Immunity. 2015; 43: 579-590Abstract Full Text Full Text PDF PubMed Scopus (265) Google Scholar), and clonality of the T cell receptor (TCR) repertoire has been associated with TLS density (Zhu et al., 2015Zhu W. Germain C. Liu Z. Sebastian Y. Devi P. Knockaert S. Brohawn P. Lehmann K. Damotte D. Validire P. et al.A high density of tertiary lymphoid structure B cells in lung tumors is associated with increased CD4(+) T cell receptor repertoire clonality.OncoImmunology. 2015; 4: e1051922Crossref PubMed Scopus (63) Google Scholar). Consistent with these findings, T cells were significantly increased in TLS-enriched tumors (Figure S1H). Moreover, while there was no enrichment in TCR clonality in tumors compared to nLung, the CD8+PD-1+ T cell subset uniquely and significantly correlated with increased TCR clonality at the tumor site but not in nLung (Figures 2G and S2J). These results suggest that CD8+ T cells expressing PD-1 are clonally expanded at the tumor site and that this expansion may preferentially occur in TLS-enriched tumor lesions. In addition to measuring adaptive lymphocyte responses to tumors, we also analyzed the distribution of innate lymphocytes and in particular NK cells in lung tumor lesions. Strikingly, NK cells were the least abundant immune cell lineage in lung adenocarcinoma lesions compared to nLung (Figures 1C and 1D). Importantly, the NK cell subset expressing CD16 was most dramatically reduced in the tumor compared to nLung (Figures 3A and S3A). NK cells that infiltrated the tumor lesion expressed higher CXCR3 levels, a molecule shown to be required for NK infiltration of tumors (Figures 3B and 3C; Wendel et al., 2008Wendel M. Galani I.E. Suri-Payer E. Cerwenka A. Natural killer cell accumulation in tumors is dependent on IFN-gamma and CXCR3 ligands.Cancer Res. 2008; 68: 8437-8445Crossref PubMed Scopus (255) Google Scholar). Moreover, NK cells that remained at the tumor site were less cytolytic, as they expressed lower levels of granzyme B and CD57 as well as less IFNγ (Figures 3C–3E and S3C). Conversely, since NK cells function to eliminate MHC class-I-deficient cells, we assessed the distribution of MHC class I throughout the tumor cells using MICSSS (Figure 3F). Tumor lesions with reduced frequency of MHC class-I-expressing tumor cells had higher numbers of tumor-infiltrating CD16+ NK cells, suggesting that the presence of cytolytic NK cells may have led to MHC class I immuno-editing (Figures 3G and S3D).Figure S3NK Cells Correlate with CD16+ Monocytes and Tumor MHC I Expression, Related to Figure 3Show full caption(A) Bar plots of frequency of CD16+ and CD16- NK cell metaclusters stratified by tissue for 10 additional lung adenocarcinoma patients (∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test).(B) Correlation plot showing the relationship of CD16+ monocyte frequency with CD16+ NK cell frequency (Spearman’s rank-based correlation).(C) Bar plots of the normalized expression of granzyme B by CD16+ NK cells across nLung and tumor, upon stimulation (n=9; ∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test).(D) Correlation plot illustrating an indirect relationship of HLA-ABC expression on CD45- CD326+ cells and CD16+ NK cell frequency (n=10; Spearman’s rank-based correlation).Bar plots show mean ± SEM.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Bar plots of frequency of CD16+ and CD16- NK cell metaclusters stratified by tissue for 10 additional lung adenocarcinoma patients (∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test). (B) Correlation plot showing the relationship of CD16+ monocyte frequency with CD16+ NK cell frequency (Spearman’s rank-based correlation). (C) Bar plots of the normalized expression of granzyme B by CD16+ NK cells across nLung and tumor, upon stimulation (n=9; ∗p<0.05, ∗∗p<0.01 and ∗∗∗p<0.001 by paired t-test). (D) Correlation plot illustrating an indirect relationship of HLA-ABC expression on CD45- CD326+ cells and CD16+ NK cell frequency (n=10; Spearman’s rank-based correlation). Bar plots show mean ± SEM. To fully capture the heterogeneity of the TIM compartment, we first performed an unbiased single-cell transcriptomic analysis of non-lymphocyte cells that accumulated in the tumor lesion and in the nLung of a stage I patient using massively parallel single-cell RNA-sequencing (MARS-seq; Figure 4A; Data S2B; STAR Methods; Jaitin et al., 2014Jaitin D.A. Kenigsberg E. Keren-Shaul H. Elefant N. Paul F. Zaretsky I. Mildner A. Cohen N. Jung S. Tanay A. Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.Science. 2014; 343: 776-779Crossref PubMed Scopus (1095) Google Scholar). Single-cell RNA-seq analysis of TIM-enriched cells, using an unbiased expectation-maximization algorithm previously described (STAR Methods; Paul et al., 2015Paul F. Arkin Y. Giladi A. Jaitin D.A. Kenigsberg E. Keren-Shaul H. Winter D. Lara-Astiaso D. Gury M. Weiner A. et al.Transcriptional heterogeneity and lineage commitment in myeloid progenitors.Cell. 2015; 163: 1663-1677Abstract Full Text Full Text PDF PubMed Scopus (599) Google Scholar), revealed several immune cell types distinguished based on characteristic gene expression profiles identified according to highly expressed and differential genes (Figure 4A; Table S3). Specifically, we identified three distinct clusters expressing high levels of MHC class II molecules, CD1C, CCL22, and CD207 (Figure 4A, clusters 2–4) and slightly higher BATF3 and CSF2RA expression levels (Figures 4E and S4A), which we inferred to be DC. Other clusters were identified as macrophages (Figure 4A, clusters 5–8) and monocytes (clusters 9–10) based on the differential expression of CD68, MAFB, and CSF1R (Figures 4E and S4A). A macrophage cluster comprised predominantly of cells from tumor lesions (cluster 7) was distinct from those comprised predominantly of cells from nLung (clusters 5–6), indicating that tumor-associated macrophages were distinct from their nLung-resident macrophage counterparts.Figure S4Differential Transcript and Protein Ma" @default.
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• W2611596972 title "Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses" @default.
• W2611596972 cites W130099911 @default.
• W2611596972 cites W1631320694 @default.
• W2611596972 cites W1631719479 @default.
• W2611596972 cites W1633340712 @default.
• W2611596972 cites W174593482 @default.
• W2611596972 cites W1767508090 @default.
• W2611596972 cites W1908442436 @default.
• W2611596972 cites W1931245889 @default.
• W2611596972 cites W1956142692 @default.
• W2611596972 cites W1963904011 @default.
• W2611596972 cites W1964407888 @default.
• W2611596972 cites W1973988310 @default.
• W2611596972 cites W1976199086 @default.
• W2611596972 cites W1978586979 @default.
• W2611596972 cites W1982729887 @default.
• W2611596972 cites W1998518732 @default.
• W2611596972 cites W2020541351 @default.
• W2611596972 cites W2028544780 @default.
• W2611596972 cites W2037342985 @default.
• W2611596972 cites W2043071551 @default.
• W2611596972 cites W2046505430 @default.
• W2611596972 cites W2046618178 @default.
• W2611596972 cites W2047218894 @default.
• W2611596972 cites W2055620105 @default.
• W2611596972 cites W2060672294 @default.
• W2611596972 cites W2071010503 @default.
• W2611596972 cites W2072322888 @default.
• W2611596972 cites W2074640340 @default.
• W2611596972 cites W2079936875 @default.
• W2611596972 cites W2084338775 @default.
• W2611596972 cites W2099900951 @default.
• W2611596972 cites W2103348420 @default.
• W2611596972 cites W2104504726 @default.
• W2611596972 cites W2107018762 @default.
• W2611596972 cites W2110252240 @default.
• W2611596972 cites W2111240640 @default.
• W2611596972 cites W2120110458 @default.
• W2611596972 cites W2124427232 @default.
• W2611596972 cites W2126811287 @default.
• W2611596972 cites W2127897152 @default.
• W2611596972 cites W2129716877 @default.
• W2611596972 cites W2129741568 @default.
• W2611596972 cites W2131681506 @default.
• W2611596972 cites W2140999940 @default.
• W2611596972 cites W2143689609 @default.
• W2611596972 cites W2146512944 @default.
• W2611596972 cites W2155158870 @default.
• W2611596972 cites W2156953670 @default.
• W2611596972 cites W2168028214 @default.
• W2611596972 cites W2177432730 @default.
• W2611596972 cites W2180091996 @default.
• W2611596972 cites W2228221433 @default.
• W2611596972 cites W2293531514 @default.
• W2611596972 cites W2341604280 @default.
• W2611596972 cites W2408730462 @default.
• W2611596972 cites W2461271870 @default.
• W2611596972 cites W2465158514 @default.
• W2611596972 cites W2484573964 @default.
• W2611596972 cites W2503552011 @default.
• W2611596972 cites W2508205285 @default.
• W2611596972 cites W2522337762 @default.
• W2611596972 cites W2560367415 @default.
• W2611596972 cites W37815363 @default.
• W2611596972 doi "https://doi.org/10.1016/j.cell.2017.04.014" @default.

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