February 2024
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30 Reads
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2 Citations
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February 2024
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30 Reads
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2 Citations
October 2023
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45 Reads
Background In contrast to PD-1⁺CD8⁺ T cells, PD-1⁺CD4⁺ T cells and their impact in tumor progression and immunotherapy response remain relatively unexplored. We previously reported that PD-1hiFoxp3⁻CD4⁺ T cells (4PD1hi) from melanoma-bearing mice and patients with melanoma or non-small cell lung cancer (NSCLC) suppress T-cell function and correlate with unfavorable outcomes upon immune checkpoint blockade (ICB) therapy.¹ CD4⁺PD-1⁺ T cells were also found to correlate with poor prognosis in other NSCLC patient cohorts.2–4 We showed that 4PD1hiup-regulate T-follicular-helper-cell(Tfh)-related genes.¹ 4PD1hi cells suppressing immunotherapy responses were recently described in mouse sarcoma models; however, in this setting, 4PD1hi did not over-express Tfh genes.⁵ Here, we sought to deconvolve the lineage commitment of 4PD1hi tumor-infiltrating lymphocytes (TILs) in relationship with their immune function and impact on ICB outcome. Methods Single-cell RNA-sequencing (scRNAseq) was performed in 4PD1hi, PD-1⁻Foxp3⁻CD4⁺ (4Dneg), and Foxp3⁺CD4⁺ T cells (Tregs) FACS-sorted from ICB-treated B16F10-melanoma bearing Foxp3-GFP mice. scRNAseq datasets of TILs from ICB-treated cancer patients6–9 were used to extract 4PD1hi and analyze their profiles. Tfh-deficient SAP knock-out (KO) and CD4KO:CXCR5KO mixed bone marrow (BM) transplanted RAGKO and control mice were implanted with B16F10, treated with ICB, and 4PD1hi, 4Dneg, and Tregs were quantified by flow cytometry and FACS-sorted for functional analyses. Results Using prior-knowledge-based signatures and mutual-information-based cell-type classification,¹⁰ we found that spleen-derived 4PD1hi cells from tumor-bearing mice polarize toward Tfh, Tregs toward a canonical Treg phenotype, and 4Dneg toward Th1. Conversely, tumor-derived 4PD1hi were not significantly skewed toward these phenotypes but gained in Th1 polarization after an effective anti-CTLA-4 treatment. In human primary melanoma,⁷ NSCLC,⁶ and squamous/basal cell carcinoma,⁸ 4PD1hi cells over-expressed Tfh-related genes. This was less clear in 4PD1hi from mixed NSCLC samples, encompassing primary tumors and different metastatic sites.⁹ However, 4PD1hi from ICB-non-responder patients in this study⁹ displayed the greatest Tfh-signature scores. Consistently, 4PD1hi TILs from ICB-non-responders in the other NSCLC and melanoma datasets up-regulated Tfh-related genes. To test 4PD1hi TIL Tfh polarization in ICB response, we used SAPKO and CD4KO:CXCR5KO BM chimera mice. Both Tfh-deficient models showed better tumor responses to a suboptimal anti-CTLA-4 treatment¹; however, 4PD1hi TILs did not substantially decrease. In this setting, 4PD1hi TILs lost suppressive function, down-regulated Pdcd1 and Il10, and up-regulated Ifng, suggesting a Th1 phenotypic switch. Conclusions These results indicate that 4PD1hi TILs are heterogeneous and their Tfh/Th1 polarization influences immunotherapy responses possibly in a tumor-tissue dependent way. In melanoma, 4PD1hi TIL Tfh polarization drives immunosuppression and ICB resistance. Acknowledgements This study was supported in part by the Parker Institute for Cancer Immunotherapy. S. M. and A.O. contributed equally to this work. References • Zappasodi R, Budhu S, Hellmann MD, Postow MA, Senbabaoglu Y, Manne S, et al. Non-conventional Inhibitory CD4(+)Foxp3(-)PD-1(hi) T Cells as a Biomarker of Immune Checkpoint Blockade Activity. Cancer Cell. 2018; 33 (6):1017–32 e7. • Zheng H, Liu X, Zhang J, Rice SJ, Wagman M, Kong Y, et al. Expression of PD-1 on CD4 + T cells in peripheral blood associates with poor clinical outcome in non-small cell lung cancer. Oncotarget. 2016; 7 (35). • Arrieta O, Montes-Servín E, Hernandez-Martinez J-M, Cardona AF, Casas-Ruiz E, Crispín JC, et al. Expression of PD-1/PD-L1 and PD-L2 in peripheral T-cells from non-small cell lung cancer patients. Oncotarget. 2017; 8 (60). • Duchemann B, Naigeon M, Auclin E, Ferrara R, Cassard L, Jouniaux JM, et al. CD8(+)PD-1(+) to CD4(+)PD-1(+) ratio (PERLS) is associated with prognosis of patients with advanced NSCLC treated with PD-(L)1 blockers. J Immunother Cancer. 2022; 10 (2). • Hussein S, Kelly M, Yuang S, Samuel A, Ton S, Yik Andy Y, et al. 578 CD8-targeted IL-2 drives potent anti-tumor efficacy and promotes action of tumor specific vaccines. Journal for ImmunoTherapy of Cancer. 2021; 9 (Suppl 2):A607. • Caushi JX, Zhang J, Ji Z, Vaghasia A, Zhang B, Hsiue EH, et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 2021; 596 (7870):126–32. • Schad SE, Chow A, Mangarin L, Pan H, Zhang J, Ceglia N, et al. Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions. J Exp Med. 2022; 219 (6). • Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019; 25 (8):1251–9. • Liu B, Hu X, Feng K, Gao R, Xue Z, Zhang S, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nature Cancer. 2022; 3 (1):108–21. • Ceglia N, Sethna Z, Freeman SS, Uhlitz F, Bojilova V, Rusk N, et al. GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information. bioRxiv. 2023:2022.04.22.487554.
April 2023
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13 Reads
Cancer Research
Immune checkpoint proteins are key regulators of the immune system and drug targets of cancer immunotherapy. Immune checkpoint molecules have been found on the cell surface of various tumor cells and immune cells as immune activating or inhibitory receptors and ligands. Recent studies have shown these immune checkpoint proteins can undergo alternative splicing or proteolytic cleavage leading to soluble isoforms. IFNγ is a pleiotropic and multifunctional cytokine commonly released by cytotoxic immune cells. IFNγ production can be enhanced in response to immunotherapy and further modulates the expression of immune checkpoints in the tumor microenvironment. To better understand the immune checkpoint signatures in tumors, we developed a 28-plex mouse checkpoint protein multiplex immunoassay for quantitative analysis of soluble (secreted) and cellular immune checkpoint proteins. Using this immunoassay, we tested 16 mouse cell lines of tumor (n=13) and normal tissues as control (n=3): 2 melanomas (B16F10, YUMM1.7), 3 mammary carcinoma (4T1, NT5, AT-3), 1 lung carcinoma (LLC), 1 prostate cancer (MycCaP), 1 fibrosarcoma (MCA205), 1 ovarian cancer (ID8), 1 bladder cancer (MB49), 1 glioma (CT-2A), 2 blood cancer (RAW, EL4), 1 dendritic cells (DC2.4), 1 myoblast cells (C2C12), and 1 endothelial cells (bEnd.3). We analyzed changes in immune checkpoint protein expression and secretion in these cell lines cultured in normal conditions or upon exposure to IFNγ to reflect an immune inflamed microenvironment. Cells were cultured in 96-well plate, 100 mm, or 150 mm Petri dishes, and treated with either 10 ng/ml or 100 ng/mL mouse IFNγ. Cell culture supernatants and cell lysates were harvested after 6h, 12h, or 24h incubation with IFNγ and processed for multiplex Luminex bead-based immunoassay. The soluble and cellular checkpoint proteins most substantially modulated by IFNγ treatment in these cell lines included: CD137, BTLA, HVEM, PD-L1. The tumor-specific signature of cellular and soluble checkpoint proteins and its response to IFNγ may have implications in the tumor microenvironment and in the outcome to immunotherapy. Citation Format: Wen-Rong Lie, Ryan Bucktrout, Sanjukta Chakraborty, Andrea Orlando, Inna Serganova, Christine Kornmeier, James Hoberg, Roberta Zappasodi. Quantitative multiplex analysis of soluble and cellular immune checkpoint proteins in response to interferon gamma across multiple murine tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6381.
November 2022
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63 Reads
Background Despite the success of immunotherapy, clinical responses remain difficult to predict, likely due to diverging tumor immune cell composition and function. Advances in single-cell analysis have revealed heterogeneous immune cell activity within and across individuals with cancer. While CD8+ ?tumor-infiltrating lymphocytes (TILs) have been extensively studied,1–4 a pan-cancer consensus annotation of CD4+ TIL in immunotherapy is lacking. Robust identification of CD4+ T-cells from admixed single-cell transcriptomes is challenging due to low CD4 transcript expression and CD4+CD8+ T cells. Poor harmonization of CD4+ T-cell annotations across datasets compromises reproducibility and generalization. Here, we present the Cancer Immunotherapy T-cell Atlas (CITA), a harmonized, metadata-rich, pan-cancer, single-cell omics resource, spanning over 1.3M T cells, aimed at discovering CD4+ T-cell related features impacting immunotherapy response. Methods Publicly available single-cell RNA sequencing (scRNAseq) data were used to generate the CD4+ T-cell consensus re-annotation and the CITA. Raw count data and metadata were obtained from the Gene Expression Omnibus (GEO) or manuscript supplementary data. Individual datasets were processed using standardized bioinformatics workflow for quality control, integration, normalization, and batch correction. Results We collected scRNAseq data and clinical metadata from 23 published datasets from 320 donors, across 30 different cancers, 20 immunotherapies, and from diverse tissue types and sequencing platforms3,5–25 (figure 1). Existing immune cell annotations were harmonized by mapping to our reference cell identity labels, and T cells were subsetted for the CITA. To enable consensus-driven annotation, we resolved precise CD4+ T-cell transcriptional profiles from publicly available, FACS-sorted CD4+ T-cell scRNAseq datasets from liver, lung, and colorectal cancers.21,22,26 We found CD4+ T cells homogeneously distributed in 12 main clusters across cancer types (figure 2). Foxp3+ regulatory T cells (Tregs) segregated into circulating/naive, tissue-resident, and effector Tregs, consistent with prior studies.²⁷ Moreover, we resolved naive, central, effector, tissue-resident, activated, and highly proliferating CD4+Foxp3- T cells, as well as Tbet+ Th1, and T follicular helper (Tfh) cells, co-expressing cytotoxic or canonical Tfh genes respectively (figure 2). Conclusions The CITA provides the foundation for pan-cancer, harmonized, metadata-rich compendium of single-cell omics T-cell data from treatment-naive and immunotherapy-treated patients. Our CD4+ T-cell consensus re-annotation in conjunction with existing and new machine-learning-based classification methods automates annotation of new and existing CD4+T-cell datasets. CITA will be a publicly available software and data resource at http://cita.cells.ucsc.edu and will include new datasets as they are released. Acknowledgements We thank SITC Sparkathon for supporting this work. L.M. is supported by the Regents fellowship for the Program in Biomedical Sciences & Engineering, Biomolecular Engineering & Bioinformatics Ph.D. at the University of California, Santa Cruz. R.Z. is supported by the Parker Institute for Cancer Immunotherapy Bridge Fellows Award. R.Z. acknowledges funding from the NCI SPORE (P50-CA192937) and the Leukemia & Lymphoma Society and receives grant support from Bristol Myers Squibb and AstraZeneca. References • Giles JR, Manne S, Freilich E, Oldridge DA, Baxter AE, George S, et al. Human epigenetic and transcriptional T cell differentiation atlas for identifying functional T cell-specific enhancers. Immunity. 2022; 55 : 557–574.e7. • Developmental Relationships of Four Exhausted CD8+ T Cell Subsets Reveals Underlying Transcriptional and Epigenetic Landscape Control Mechanisms. Immunity. 2020; 52 : 825–841.e8. • Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science. 2021; 374 : abe6474. • Leun AM van der, van der Leun AM, Thommen DS, Schumacher TN. CD8 T cell states in human cancer: insights from single-cell analysis. Nature Reviews Cancer. 2020. pp. 218–232. doi:10.1038/s41568-019-0235-4. • Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su M-J, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018. pp. 984–997.e24. doi:10.1016/j.cell.2018.09.006. • Zhang L, Yu X, Zheng L, Zhang Y, Li Y, Fang Q, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018; 564 : 268–272. • Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell. 2018; 174 : 1293–1308.e36. • Borcherding N, Vishwakarma A, Voigt AP, Bellizzi A, Kaplan J, Nepple K, et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun Biol. 2021; 4 : 122. • Li H, van der Leun AM, Yofe I, Lubling Y, Gelbard-Solodkin D, van Akkooi ACJ, et al. Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma. Cell. 2020. p. 747. doi:10.1016/j.cell.2020.04.017. • Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019; 25 : 1251–1259. • Ma L, Hernandez MO, Zhao Y, Mehta M, Tran B, Kelly M, et al. Tumor Cell Biodiversity Drives Microenvironmental Reprogramming in Liver Cancer. Cancer Cell. 2019; 36 : 418–430.e6. • Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D, Saatcioglu HD, et al. Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species. Immunity. 2019; 50 : 1317–1334.e10. • Vieira Braga FA, Kar G, Berg M, Carpaij OA, Polanski K, Simon LM, et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat Med. 2019; 25 : 1153–1163. • Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen Y-JJ, Chitre AS, et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020; 579 : 274–278. • Mahuron KM, Moreau JM, Glasgow JE, Boda DP, Pauli ML, Gouirand V, et al. Layilin augments integrin activation to promote antitumor immunity. J Exp Med. 2020; 217 . doi:10.1084/jem.20192080 • Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell. 2021; 184 : 1281–1298.e26. • Wu SZ, Al-Eryani G, Roden DL, Junankar S, Harvey K, Andersson A, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet. 2021; 53 : 1334–1347. • Liu B, Hu X, Feng K, Gao R, Xue Z, Zhang S, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer. 2022; 3 : 108–121. • Steen CB, Luca BA, Esfahani MS, Azizi A, Sworder BJ, Nabet BY, et al. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell. 2021; 39 : 1422–1437.e10. • Schad SE, Chow A, Mangarin L, Pan H, Zhang J, Ceglia N, et al. Tumor-induced double positive T cells display distinct lineage commitment mechanisms and functions. J Exp Med. 2022; 219 . doi:10.1084/jem.20212169. • Zheng C, Zheng L, Yoo J-K, Guo H, Zhang Y, Guo X, et al. Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell. 2017; 169 : 1342–1356.e16. • Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med. 2018; 24 : 978–985. • Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP, Yizhak K, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature. 2016; 539 : 309–313. • Caushi JX, Zhang J, Ji Z, Vaghasia A, Zhang B, Hsiue EH-C, et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 2021; 596 : 126–132. • Oliveira G, Stromhaug K, Cieri N, Iorgulescu JB, Klaeger S, Wolff JO, et al. Landscape of helper and regulatory antitumour CD4 T cells in melanoma. Nature. 2022; 605 : 532–538. • Zhang Y, Zheng L, Zhang L, Hu X, Ren X, Zhang Z. Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients. Sci Data. 2019; 6 : 131. • Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation. Immunity. 2019; 50 : 493–504.e7. • Download figure • Open in new tab • Download powerpoint Abstract 9 Figure 1 Composition of the CITAA. Pipeline for the Cancer Immunotherapy T-cell Atlas (CITA) integration including the use of consensus annotated CD4+ T-cell states based on FACS-sorted CD4 single-cell datasets. UMAP of 1.3M T cells from 23 single cell datasets from individuals with cancer, for 30 different cancer types and 9 tissue types (center). B,C. CITA harmonized metadata overview, with sampled tissue type per cancer type and treatment type by cancer type. LUAD: lung adenocarcinoma; SKCM, skin cancer melanoma; GBM, glioblastoma; LUSC, lung squamous cell carcinoma; CC/CRC, colorectal carcinoma; LIHC, liver hepatocellular carcinoma; BC/BCC, basal cell carcinoma; THCA, thyroid carcinoma; NPC, nasopharyngeal cancer; CHOL, cholangiocarcinoma; UCEC, uterine corpus endometrial carcinoma; DLBCL, diffuse large B cell lymphoma; ESCA, esophageal cancer; RC, renal cancer; PACA, pancreatic adenocarcinoma; O, oligodendroglioma; HCC, hepatocellular carcinoma; AA, anaplastic astrocytoma; MM, multiple myeloma; SCC, squamous cell carcinoma; BRCA, breast cancer; LUAS, lung adeno/squamous carcinoma; BC, basal cell carcinoma; BCL, B cell lymphoma; OV, ovarian serous cystadenocarcinoma; IV, glioma stage 4; FTC, fallopian tube carcinoma; ASC, ascite; JTNAT, joint tumor normal tissue; LN, lymph node; MET, metastasis; N, normal; NAT, normal adjacent tissue; PB, peripheral blood; PE, pleural effusion; T, tumor; CARBO, carboplatin; PEME, Pemetrexed; PACL, paclitaxel; Adv, adenovirus; RESECT, resection • Download figure • Open in new tab • Download powerpoint Abstract 9 Figure 2 Pan-cancer CD4+ T-cell consensus annotationA. UMAP of consensus annotation of CD4+ sorted cells from peripheral blood (PB), tumor (T) and normal adjacent tissue (NAT) from individuals with lung, colorectal or liver cancer.21,22,26 CD4 RPL: high ribosomal protein, T prolif: proliferating, Tmp: memory precursors; Tcm: central memory, Tact: activated, Tem: effector memory, Tfh: follicular helper, Th1 CTL: T helper 1 cytolytic lymphocytes, Trm: tissue resident memory, cTregs: circulating regulatory T cells, eTregs: effector regulatory T cells, trTregs: tissue resident regulatory T cells. B. Barplot of cancer type and tissue type fractions for each cell annotation. C. UMAP of gene signature scores for cell cycle and a curated T-cell activation/terminal differentiation gene signature (n=26 genes) consisting of terminal differentiation transcription factors (e.g. ID2, RUNX3, PRDM1, TOX), cytolytic markers (e.g. GZMA, GZMB, GZMH, PRF1), co-stimulatory receptors (e.g. ICOS, TNFRSF18, TNFRSF4), and chemokines/chemokine receptors (e.g. CXCR3, CX3CR1, CXCL13) for dataset described in (A). D. Dotplot of the five most significant differentially expressed genes for each cell annotation contrasted against each other cell annotation