Clinical relevance of tumour-associated macrophages

Nature Reviews Clinical Oncology, Journal Year: 2022, Volume and Issue: 19(6), P. 402 - 421

Published: March 30, 2022

Language: Английский

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Liver tumour immune microenvironment subtypes and neutrophil heterogeneity

Nature, Journal Year: 2022, Volume and Issue: 612(7938), P. 141 - 147

Published: Nov. 9, 2022

Language: Английский

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Macrophage diversity in cancer revisited in the era of single-cell omics

Trends in Immunology, Journal Year: 2022, Volume and Issue: 43(7), P. 546 - 563

Published: June 9, 2022

TAMs have diverse functions in cancer, reflecting the heterogenous nature of these immune cells. Here, we propose a new nomenclature to identify TAM subsets.Recent single cell multi-omics technologies, which allow clustering subsets an unbiased manner, significantly advanced our understanding molecular diversity mice and humans.Novel mechanisms potential therapeutic targets been identified that might regulate tumor-promoting function different subsets.TAM opens promising opportunities for envisaging putative cancer treatments. Tumor-associated macrophages (TAMs) multiple potent and, thus, represent important targets. These highlight TAMs. Recent omics technologies However, unifying annotation their signatures is lacking. review recent major studies transcriptome, epigenome, metabolome, spatial with specific focus on We also consensus model present avenues future research. one most abundant types tumors [1.Cassetta L. Pollard J.W. Targeting macrophages: approaches cancer.Nat. Rev. Drug Discov. 2018; 17: 887-904Crossref PubMed Scopus (650) Google Scholar]. Since initial decade ago [2.Qian B.Z. Macrophage enhances tumor progression metastasis.Cell. 2010; 141: 39-51Abstract Full Text PDF (3151) Scholar], functional now widely appreciated, many seminal field [3.Yang M. et al.Diverse microenvironments.Cancer Res. 78: 5492-5503Crossref (202) Scholar, 4.DeNardo D.G. Ruffell B. Macrophages as regulators tumour immunity immunotherapy.Nat. Immunol. 2019; 19: 369-382Crossref (643) 5.Lopez-Yrigoyen al.Macrophage targeting cancer.Ann. N. Y. Acad. Sci. 2021; 1499: 18-41Crossref (25) This array includes promotion growth, lineage plasticity, invasion, remodeling extracellular matrix, crosstalk endothelial, mesenchymal stromal cells, other cells; effects can result progression, metastasis (see Glossary), therapy resistance [6.Mantovani A. al.Tumour-associated treatment oncology.Nat. Clin. Oncol. 2017; 14: 399-416Crossref (1675) Scholar,7.Guc E. Redefining macrophage neutrophil biology metastatic cascade.Immunity. 54: 885-902Abstract (13) With wide application years seen explosion data illustrating cellular heterogeneity resulting unprecedented amount information TAMs, regardless main studies. Links between are emerging. terminology lacking, making direct comparisons full utilization sets difficult. In this review, summarize human data; include traditional nomenclatures, at levels single-cell transcriptomic, epigenomic, metabolic multi-omics, opportunities, directions. subsets. hope will serve starting point help build complete picture dynamic interactions tumor, well microenvironment (TME). A used describe has now-obsolete M1/M2 model, proposed ~20 ago; it separated into two distinct arms: M1 or 'classically' activated; M2 'alternatively' activated, largely based vitro stimulating type 1 2 cytokines [8.Mills C.D. al.M-1/M-2 Th1/Th2 paradigm.J. 2000; 164: 6166-6173Crossref The newer term 'M1-like' phenotype typically described proinflammatory induced by Toll-like receptor (TLR) ligands cytokines, namely IFN-γ TNF-α. Conversely, 'M2-like' having anti-inflammatory characteristics, being activated interleukin (IL)-4 IL-13, producing TGF-β profibrotic factors. nomenclature, albeit used, remains oversimplified [9.Martinez F.O. Gordon S. paradigm activation: time reassessment.F1000Prime Rep. 2014; 6: 13Crossref (2673) Scholar,10.Nahrendorf Swirski F.K. Abandoning network function.Circ. 2016; 119: 414-417Crossref (195) Indeed, significant morphology, function, surface marker expression observed resident-tissue (RTMs) from organs [11.Bleriot C. al.Determinants resident tissue identity function.Immunity. 2020; 52: 957-970Abstract (94) Scholar]; moreover, co-expression both gene almost all [12.Mulder K. al.Cross-tissue landscape monocytes health disease.Immunity. 1883-1900Abstract Therefore, spectrum polarization relates represents more sensible approach describing [10.Nahrendorf Scholar,13.Mosser D.M. Edwards J.P. Exploring activation.Nat. 2008; 8: 958-969Crossref (5864) normal homeostasis, tightly regulated niche-like local environment, recently [14.Guilliams al.Establishment maintenance niche.Immunity. 434-451Abstract (138) Another layer derives origin. Using lineage-tracing mice, illustrated mouse RTMs derived early erythromyeloid progenitors formed either yolk sac fetal liver [15.Geissmann F. al.Blood consist principal migratory properties.Immunity. 2003; 71-82Abstract (2514) Scholar,16.Gomez Perdiguero al.Tissue-resident originate yolk-sac-derived erythro-myeloid progenitors.Nature. 2015; 518: 547-551Crossref (1236) Additionally, adult may derive circulating monocytic precursors (monocytes) bone marrow [17.Cox al.Origins, biology, diseases macrophages.Annu. 39: 313-344Crossref (1) monocyte contribution varies among organs. For example, steady state, microglia central nervous system (CNS) solely [18.Hoeffel G. al.C-Myb(+) progenitor-derived give rise tissue-resident macrophages.Immunity. 42: 665-678Abstract (611) while dermal embryonic origin [19.Kolter J. al.A subset skin contributes surveillance regeneration nerves.Immunity. 50: 1482-1497Abstract (69) appreciated repeatedly reviewed [20.Pathria P. al.Targeting tumor-associated cancer.Trends 40: 310-327Abstract (382) Scholar,21.Guerriero J.L. Macrophages: road less traveled, changing anticancer therapy.Trends Mol. Med. 24: 472-489Abstract (143) Similar counterparts not only its ontogeny, but cues, including type, organ, subanatomic Identifying basis over past [5.Lopez-Yrigoyen advancements unveiling multidimensional complexity manner. research, oncology eventually fully understand cells hopefully use improve precision diagnosis therapy. Single RNA sequencing (scRNA-seq) technology revolutionized providing in-depth transcriptome level [22.Giladi al.Single-cell characterization haematopoietic trajectories homeostasis perturbed haematopoiesis.Nat. Cell Biol. 20: 836-846Crossref (139) substantial advances available experimental techniques bioinformatics pipelines years, scRNA-seq investigate [23.Lawson D.A. al.Tumour resolution.Nat. 1349-1360Crossref (230) Scholar,24.Ren X. al.Insights gained analysis microenvironment.Annu. 583-609Crossref (15) transcriptomic remain Two large-scale pan-cancer provided valuable regarding diversity. One study analyzed myeloid 380 samples across 15 210 patients through combination newly collected eight published [25.Cheng transcriptional atlas infiltrating cells.Cell. 184: 792-809Abstract (111) Comparison consistent presence CD14+ CD16+ tumor-infiltrating (TIMs), LYVE1+ interstitial non-cancer tissues, seven clusters: INHBA+ C1QC+ ISG15+ LNRP3+ SPP1+ compiled mononuclear phagocytes (MNPs) isolated 41 13 types, six common universe, termed MNP-VERSE. Monocyte clusters were then extracted reintegrated generate MoMac-VERSEi. regulatory inference (SCENIC) [26.Aibar al.SCENIC: clustering.Nat. Methods. 1083-1086Crossref (1003) authors classical monocytes, nonclassical five (HES1 TAM, C1Qhi TREM2 IL4I1 proliferating TAMs) Although nomenclatures studies, others, pattern transcriptomics By reviewing journals, found preserved (Table 1). Based signature genes, enriched pathways, predicated naming interferon-primed (IFN-TAMs), (Reg-TAMs), inflammatory cytokine-enriched (Inflam-TAMs), lipid-associated (LA-TAMs), pro-angiogenic (Angio-TAMs), RTM-like (RTM-TAMs), (Prolif-TAMs) Figure 1, Key figure). Furthermore, three TIMs Box 1).Table 1Mouse various TMEsaBlack font: genes clusters; blue protein markers Underline: CITE-seq; Bold: key reported than paper., bAbbreviations: BRCA, breast cancer; CAF, cancer-associated macrophage; CITE-seq, indexing transcriptomes epitopes sequencing; CRC, colorectal CyTOF, Mass cytometry flight; ECM, matrix; ESCA, esophageal carcinoma; GC, gastric HCC, hepatocellular HNC, head neck i.v., intravenous; IF, immunofluorescent staining; INs-seq, intracellular staining LCM, laser capture microdissection; LYM, lymphoma; MEL, melanoma; Mets, metastasis; mIHC, multiplex immunochemistry MMY, myeloma; N/A, available; NPC, nasopharyngeal NSCLC, nonsmall lung OS, osteosarcoma; OVC, ovarian PDAC, pancreatic ductal adenocarcinoma; PRAC, prostate RCC, renal Reg-TAMs, TAMs; SARC, sarcoma; sc-MS, mass spectrometry; SEPN, spinal ependymomas; SKC, ST, transcriptomics; s.c., subcutaneous; macrophages; THCA, thyroid UCEC, uterine corpus endometrial carcinoma.AnnotationSpeciesSignatureTFCancer typeFunction/enriched pathwayAssayRefsIFN-TAMsHumanCASP1, CASP4, CCL2/3/4/7/8, CD274hi, CD40, CXCL2/3/9/10/11, IDO1, IFI6, IFIT1/2/3, IFITM1/3, IRF1, IRF7, ISG15, LAMP3, PDCD1LG2hi, TNFSF10, C1QA/C, CD38, IL4I1, IFI44LSTAT1 IRF1/7BRCACRCCRC metsGBMHCCHNCLYMMELMMYNPCNSCLCOSPDACSEPNTHCAUCECApoptosis regulatorsEnhance proliferationInflammatory responsesPromote Treg entry tumorT exhaustionImmunosuppressionColocalization exhausted T (ST, IF)Decreased antigen presentation (CyTOF)Suppressed activation (in vitro)IFN-α/γ-IFN response signature; IL2/STAT5; IL6/JAK/STAT3scRNA-seqCITE-seqmIHCSTNanoString GeoMx[12.Mulder Scholar,29.Gubin M.M. al.High-dimensional delineates lymphoid compartment during successful immune-checkpoint therapy.Cell. 175: 1014-1030Abstract (165) Scholar,32.Zavidij O. reveals compromised precursor stages myeloma.Nat. Cancer. 1: 493-506Crossref 33.Zhou intratumoral immunosuppressive osteosarcoma.Nat. Commun. 11: 6322Crossref (74) 34.Zhang Q. al.Interrogation microenvironmental ependymomas dual macrophages.Nat. 12: 6867Crossref (0) Scholar,45.Wu al.Spatiotemporal level.Cancer 134-153Crossref (10) Scholar,52.Pombo Antunes A.R. profiling glioblastoma species disease stage competition specialization.Nat. Neurosci. 595-610Crossref (78) Scholar,\81.Wu S.Z. spatially resolved cancers.Nat. Genet. 53: 1334-1347Crossref (47) Scholar,83.Pelka al.Spatially organized multicellular hubs cancer.Cell. 4734-4752Abstract (29) Scholar]CD14+, CD11b+, CD68+, PD-L1hi, PD-L2hi, CD80hi, CD86hi, MHCIIhi, CD86+, MRC1–, SIGLEC1–, HLA-DRlo, CD314+, CD107a+, CD86, TLR4, CD44 (CITE-seq)MouseCcl2/7/8, Cd274, Cxcl9/10/11, Ifit1/2/3, Ifit3, Ifitm1/3, Il7r, Isg15, Nos2, Rsad2, Tnfsf10, Stat1N/ACT26 s.c. CRCCT26 intrasplenic mets modelT3 SARC (s.c.)Orthotopic GL261 GBMIFN signaturescRNA-seqCITE-seqmIHC[29.Gubin Scholar]Inflam-TAMsHumanCCL2/3/4/5/20, CCL3L1, CCL3L3, CCL4L2, CCL4L4, CXCL1/2/3/5/8, G0S2, IL1B, IL1RN, IL6, INHBA, KLF2/6, NEDD9, PMAIP1, S100A8/A9, SPP1EGR3 IKZF1 NFKB1 NFE2L2 RELCRCCRC metsOSSEPNGCRecruiting regulating cellsCNS inflammation-associated chemokinesPromotes inflammationNeutrophil recruitment lumenT interaction (IHC)TNF signaling; WNTImmune check pointsscRNA-seqmIHCNanoString GeoMx[31.Che L.-H. metastases reprogramming preoperative chemotherapy.Cell Discovery. 7: 80Crossref (4) Scholar,33.Zhou Scholar,34.Zhang Scholar,42.Sathe genomic microenvironment.Clin. Cancer 26: 2640-2653Crossref (66) 43.Zhang al.Dissecting underlying premalignant lesions cancer.Cell 27: 1934-1947Abstract (104) 44.Yin H. map development using sequencing.Front. 12728169Crossref 45.Wu Scholar]MouseCxcl1/2/3/5/8, Ccl20, Ccl3l1, Il1rn, Il1b, G0s2, Inhba, Spp1N/ACT26 CRC CT26 modelChemokine productionImmunosuppressionscRNA-seq[45.Wu Scholar]LA-TAMsHumanACP5, AOPE, APOC1, ATF1, C1QA/B/C, CCL18, CD163, CD36, CD63, CHI3L1, CTSB/D/L, F13A1, FABP5, FOLR2, GPNMB, IRF3, LGALS3, LIPA, LPL, MACRO, MerTK, MMP7/9/12, MRC1, NR1H3, NRF1, NUPR1, PLA2G7, RNASE1, SPARC, SPP1, TFDP2, TREM2, ZEB1FOS/JUN HIF1A MAF/MAFB NR1H3 TCF4 TFECBRCACRCCRC metsGBMGCHCCHNCNPCNSCLCOSPDACPhagocytosisPromotion EMTComplement activationECM degradationAntigen processing pathwaysATP biosynthetic processesCanonical M2-like pathwaysFatty acid metabolismImmunosuppressionInflammationIron ion signalingscRNA-seqSMART-seq2CITE-seqmIHCST[12.Mulder Scholar,27.Zilionis R. cancers conserved populations individuals species.Immunity. 1317-1334Abstract (424) Scholar,28.Yang non-small differences sexes.Front. 12756722Google Scholar,30.Zhang analyses inform myeloid-targeted therapies colon 181: 442-459Abstract (246) Scholar,31.Che Scholar,50.Chen Y.P. subtypes associated prognosis carcinoma.Cell 30: 1024-1042Crossref (71) Scholar,81.Wu Scholar]CD9+, CD80+, MAF, CD163lo/-, CD206+/lo, CD71+, CD72+, CD73, ICOSL, CD40LG, Thy-1 (CITE-seq)MouseAcp5, Apoc1, Apoe, C1qa/B/C, Ccl18, Ccl8, Cd163, Cd206, Cd36, Cd63, Ctsb/d/l, Cxcl9, Fabp5, Folr2, Gpnmb, Lgals3, Macro, Mrc1, Trem2MAFCT26 Orthotopic GBM 7940b orthotopic iKras p53 PDAC metsPhagocytosisAntigen presentationFatty metabolismComplement activationscRNA-seqCITE-seqmIHC[45.Wu Scholar,46.Kemp S.B. al.Pancreatic marked complement-high blood tumor–associated macrophages.Life Alliance. 4e202000935Crossref Scholar]Angio-TAMsHumanADAM8, AREG, BNIP3, CCL2/4/20, CD300E, CD44, CD55, CEBPB, CLEC5A, CTSB, EREG, FCN1, FLT1, FN1, HES1, IL8, MIF, OLR1, PPARG, S100A8/9/12, SERPINB2, SLC2A1, SPIC, THBS1, TIMP1, VCAN, VEGFABACH1 CEBPB FOSL2 HIFA KLF5 MAF RUNX1 SPIC TEAD1 ZEB2BRCACRCCRCCRC metsESCAGBMGCHCCMELNPCNPCNSCLCOVCPDACPDAC metsRCCSEPNTHCAUCECAngiogenesisCAF interactionECM proteolysis; ECM interactionPromotion EMTHIF pathway; NF-kB Notch VEGF signalingJuxtaposed PLVAP+/DLL4+ endothelial (IF)scRNA-seqSMART-seq2CITE-seqNanoString GeoMx[25.Cheng Scholar,41.Sharma al.Onco-fetal drives carcinoma.Cell. 183: 377-394Abstract (103) Scholar,49.Raghavan al.Microenvironment drug 6119-6137Abstract Scholar,67.Zhao revealed promoted progression.J. Transl. 454Crossref Scholar]CD52hi, CD163hi, CD206hi, CXCR4+, CD354+, FOSL2, VEGFAMouseArg1, Adam8, Bnip3, Mif, Slc2a1N/AOrthotopic modelHIF signalingAngiogenesisscRNA-seqCITE-seq[52.Pombo Scholar]Reg-TAMsHumanCCL2, CD274, CD80, CHIT1, CX3CR1, HLA-A/C, HLA-DQA1/B1, HLA-DRA/B1/B5, ICOSLG, IL-10, ITGA4, LGALS9, MAC

Language: Английский

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Deciphering breast cancer: from biology to the clinic

Cell, Journal Year: 2023, Volume and Issue: 186(8), P. 1708 - 1728

Published: March 16, 2023

Language: Английский

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Ferroptosis heterogeneity in triple-negative breast cancer reveals an innovative immunotherapy combination strategy

Cell Metabolism, Journal Year: 2022, Volume and Issue: 35(1), P. 84 - 100.e8

Published: Oct. 17, 2022

Treatment of triple-negative breast cancer (TNBC) remains challenging. Deciphering the orchestration metabolic pathways in regulating ferroptosis will provide new insights into TNBC therapeutic strategies. Here, we integrated multiomics data our large cohort (n = 465) to develop atlas. We discovered that TNBCs had heterogeneous phenotypes ferroptosis-related metabolites and pathways. The luminal androgen receptor (LAR) subtype was characterized by upregulation oxidized phosphatidylethanolamines glutathione metabolism (especially GPX4), which allowed utilization GPX4 inhibitors induce ferroptosis. Furthermore, verified inhibition not only induced tumor but also enhanced antitumor immunity. combination anti-PD1 possessed greater efficacy than monotherapy. Clinically, higher expression correlated with lower cytolytic scores worse prognosis immunotherapy cohorts. Collectively, this study demonstrated landscape revealed an innovative strategy for refractory LAR tumors.

Language: Английский

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Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 9, 2022

Abstract The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference cell-cell communication. Many computational tools were developed for this purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential communication events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we systematically compare 16 resources 7 methods, plus consensus between methods’ predictions. Among resources, find few unique interactions, varying degree overlap, uneven coverage specific pathways tissue-enriched proteins. We then examine all possible combinations methods show that both strongly influence predicted interactions. Finally, assess agreement with spatial colocalisation, cytokine activities, receptor protein abundance are generally coherent those data modalities. facilitate use described work, provide LIANA, LIgand-receptor ANalysis frAmework as open-source interface methods.

Language: Английский

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Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

Nature Methods, Journal Year: 2022, Volume and Issue: 19(6), P. 662 - 670

Published: May 16, 2022

Language: Английский

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Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery

International Journal of Surgery, Journal Year: 2022, Volume and Issue: 107, P. 106936 - 106936

Published: Sept. 20, 2022

Postoperative progression and chemotherapy resistance is the major cause of treatment failure in patients with triple-negative breast cancer (TNBC). Currently, there a lack an ideal predictive model for drug sensitivity postoperative TNBC patients. Diverse programmed cell death (PCD) patterns play important role tumor progression, which has potential to be prognostic indicator after surgery.Twelve PCD (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic death, netotic parthanatos, lysosome-dependent autophagy-dependent alkaliptosis, oxeiptosis) were analyzed construction. Bulk transcriptome, single-cell genomics, clinical information collected from TCGA-BRCA, METABRIC, GSE58812, GSE21653, GSE176078, GSE75688, KM-plotter cohorts validate model.The machine learning algorithm established index (CDI) 12-gene signature. Validated five independent datasets, high CDI had worse prognosis surgery. Two molecular subtypes distinct vital biological processes identified by unsupervised clustering model. A nomogram performance was constructed incorporating features. Furthermore, associated immune checkpoint genes key microenvironment components integrated analysis bulk transcriptome. are resistant standard adjuvant regimens (docetaxel, oxaliplatin, etc.); however, they might sensitive palbociclib (an FDA-approved luminal cancer).Generally, we novel comprehensively analyzing diverse patterns, can accurately predict user-friendly website created facilitate application this prediction (https://tnbc.shinyapps.io/CDI_Model/).

Language: Английский

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High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Dec. 19, 2023

Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical samples. Current commercially available provide transcriptome single-cell, spatial, or targeted situ analysis. Here, we combine these to explore heterogeneity large, FFPE human breast cancer sections. This integrative approach allowed us differences exist between distinct tumor regions identify biomarkers involved progression towards invasive carcinoma. Further, study cell neighborhoods rare boundary cells sit at critical myoepithelial border confining spread malignant cells. demonstrate each technology alone provides information about signatures relevant understanding heterogeneity; however, it is integration leads deeper insights, ushering discoveries will progress oncology research development diagnostics therapeutics.

Language: Английский

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The dawn of spatial omics

Science, Journal Year: 2023, Volume and Issue: 381(6657)

Published: Aug. 3, 2023

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas biology and eventually revolutionize pathology by measuring physical tissue structure molecular characteristics at same time. Although field came age past 5 years, it still suffers from some growing pains: barriers entry, robustness, unclear best practices for experimental design analysis, lack standardization. In this Review, we present systematic catalog different families spatial technologies; highlight their principles, power, limitations; give perspective suggestions on biggest challenges lay ahead incredibly powerful-but hard navigate-landscape.

Language: Английский

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