scholarly journals ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy

2019 ◽  
Author(s):  
Ya-Ru Miao ◽  
Qiong Zhang ◽  
Qian Lei ◽  
Mei Luo ◽  
Gui-Yan Xie ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Response Prediction ◽  
Cell Types ◽  
Cancer Immunology ◽  
T Cell Subsets ◽  
Expression Data ◽  
Sequencing Data ◽  
Pre Treatment

AbstractThe distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. There are many T-cell subsets with specific function, however current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here we introduce Immune Cell Abundance Identifier (ImmuCellAI), a novel gene set signature-based method, for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both our sequencing data with flow cytometry results and public expression data indicated that ImmuCellAI can estimate immune cells with superior accuracy than other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets revealed that the abundance of dendritic cells (DC), cytotoxic T, and gamma delta T cells was significantly higher both in comparisons of on-treatment vs. pre-treatment and responders vs. non-responders. Meanwhile, we built an ImmuCellAI result-based model for predicting the immunotherapy response with high accuracy (AUC 0.80~0.91). These results demonstrated the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. The ImmuCellAI online server is freely available at http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/.

scholarly journals Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Guohe Song ◽  
Yang Shi ◽  
Meiying Zhang ◽  
Shyamal Goswami ◽  
Saifullah Afridi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
T Cell Subsets ◽  
M2 Macrophage ◽  
Advanced Hcc ◽  
Immune Cell Subsets

AbstractDiverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

scholarly journals Higher cytolytic score correlates with an immunosuppressive tumor microenvironment and reduced survival in glioblastoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander F. Haddad ◽  
Jia-Shu Chen ◽  
Taemin Oh ◽  
Matheus P. Pereira ◽  
Rushikesh S. Joshi ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cd8 T Cell ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Sequencing Data ◽  
Gene Score ◽  
T Cell Infiltration ◽  
The Relationship

Abstract Cytolytic score (CYT), calculated from mRNA expression levels of granzyme and perforin, positively correlates with CD8+ T cell infiltration/activity in a variety of cancers. Unlike other cancers, higher CYT has been associated with worse prognosis in glioblastoma (GBM). To address this discrepancy, we sought to investigate the relationship between CYT and immune checkpoint gene score (ICGscore), as well as their correlation with patient survival and tumor immune cell infiltration. Clinical and RNA-sequencing data for patients with newly diagnosed GBM were obtained from The Cancer Genome Atlas. Maximally-selected rank statistics was used to dichotomize subgroups. CIBERSORT was used to estimate abudence of immune cell-types. Spearman correlation was used to characterize the relationship between CYT and ICGscore. Kaplan–Meier curves were generated for survival analysis. Overall, 28/151 patients had high CYT. High CYT was associated with a mesenchymal subtype (p < 0.001) and worse survival (7.45 vs. 12.2 months, p < 0.001). There were no differences in patient demographics, IDH/MGMT mutation status, or treatment. On subgroup analysis, patients with high CYT/ICGscore had significantly increased CD8+ infiltration (p < 0.001), as expected, and worse survival (HR 0.445, p < 0.01). Furthermore, CYT strongly correlated with ICGscore (RS = 0.675, p < 0.001). The high CYT/ICGscore subgroup was associated with greater infiltration of M2 macrophages (p = 0.011) and neutrophils (p = 0.055). Our study highlights a multidimensional immunosuppressive GBM microenvironment in patients with higher CYT and potentially identifies patients with high CYT/ICGscore as a subgroup that may particularly benefit from multi-faceted immunotherapies, given their already elevated tumor CD8+ T cell levels.

scholarly journals Infino: a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironment

2017 ◽  
Author(s):  
Maxim Zaslavsky ◽  
Jacqueline Buros Novik ◽  
Eliza Chang ◽  
Jeffrey Hammerbacher
Keyword(s):  
T Cell ◽  
Tumor Microenvironment ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Training Data ◽  
Cell Type ◽  
Sequencing Data ◽  
Effective Response ◽  
Immune Infiltration

AbstractRobust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of immune cell content around a tumor by immunohistochemistry, flow cytometry, or mass cytometry allows measurement of only up to a few dozen markers at a time, limiting the number of immune cell types identified. Immune cell type abundances may instead be estimated in silico by deconvolving gene expression mixtures from bulk RNA sequencing of tumor tissue. By measuring tens of thousands of transcripts at once, bulk RNA-seq provides a rich input to algorithms that quantify cell type abundances in the tumor microenvironment, affording the potential to quantify the states of a greater number of immune cell types (given adequate training data). Here, we first review existing methods for deconvolution and evaluate their performance on synthetic mixtures. Then we develop a Bayesian inference approach, named infino, that learns to distinguish immune cell expression phenotypes and deconvolve mixtures. In contrast to earlier approaches, infino accepts RNA sequencing data, models transcript expression variability, and exploits the relationships between cell types to improve deconvolution accuracy and allow interrogation from the level of broad categories to the level of finest granularity. The resulting probability distributions of immune infiltration could be applied to numerous questions concerning the diverse ecology of immune cell types, including assessment of the association of immune infiltration with response to immunotherapy, and study of the expression profile and presence of elusive T cell subcompartments, such as T cell exhaustion.

scholarly journals Single-cell landscape of immunological responses in COVID-19 patients

2020 ◽  
Author(s):  
Ji-Yuan Zhang ◽  
Xiang-Ming Wang ◽  
Xudong Xing ◽  
Zhe Xu ◽  
Chao Zhang ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Immune Cells ◽  
Cell Types ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Interferon Response ◽  
Immunological Responses ◽  
Receptor Repertoire

AbstractIn COVID-19 caused by SARS-CoV-2 infection, the relationship between disease severity and the host immune response is not fully understood. Here we performed single-cell RNA sequencing in peripheral blood samples of five healthy donors and 13 COVID-19 patients including moderate, severe and convalescent cases. Through determining the transcriptional profiles of immune cells, coupled with assembled T cell receptor and B cell receptor sequences, we analyzed the functional properties of immune cells. Most cell types in COVID-19 patients showed a strong interferon-alpha response, and an overall acute inflammatory response. Moreover, intensive expansion of highly cytotoxic effector T cell subsets, such as CD4+ Effector-GNLY (Granulysin), CD8+ Effector-GNLY and NKT CD160, was associated with convalescence in moderate patients. In severe patients, the immune landscape featured a deranged interferon response, profound immune exhaustion with skewed T cell receptor repertoire and broad T cell expansion. These findings illustrate the dynamic nature of immune responses during the disease progression.

scholarly journals Hijacked Immune Cells in the Tumor Microenvironment: Molecular Mechanisms of Immunosuppression and Cues to Improve T Cell-Based Immunotherapy of Solid Tumors

2021 ◽  
Vol 22 (11) ◽  
pp. 5736
Author(s):  
Emre Balta ◽  
Guido H. Wabnitz ◽  
Yvonne Samstag
Keyword(s):  
T Cell ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Soluble Factors ◽  
Recent Developments ◽  
Fundamental Reason

The understanding of the tumor microenvironment (TME) has been expanding in recent years in the context of interactions among different cell types, through direct cell–cell communication as well as through soluble factors. It has become evident that the development of a successful antitumor response depends on several TME factors. In this context, the number, type, and subsets of immune cells, as well as the functionality, memory, and exhaustion state of leukocytes are key factors of the TME. Both the presence and functionality of immune cells, in particular T cells, are regulated by cellular and soluble factors of the TME. In this regard, one fundamental reason for failure of antitumor responses is hijacked immune cells, which contribute to the immunosuppressive TME in multiple ways. Specifically, reactive oxygen species (ROS), metabolites, and anti-inflammatory cytokines have central roles in generating an immunosuppressive TME. In this review, we focused on recent developments in the immune cell constituents of the TME, and the micromilieu control of antitumor responses. Furthermore, we highlighted the current challenges of T cell-based immunotherapies and potential future strategies to consider for strengthening their effectiveness.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Abstract 095: CD74 Deficient Mice are Resistant to Toll-Like Receptor-Induced Preeclampsia

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Mohamad Hatahet ◽  
Olga Y Gasheva ◽  
Valorie L Chiasson ◽  
Piyali Chatterjee ◽  
Kelsey R Bounds ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Endothelial Dysfunction ◽  
Mhc Class Ii ◽  
Immune Cells ◽  
Immune Cell ◽  
Class Ii ◽  
Poly I:C ◽  
Hypertensive Disorder ◽  
Toll Like Receptor

Preeclampsia (PE) is a pregnancy-specific hypertensive disorder characterized by vascular endothelial dysfunction and excessive immunity and inflammation. Activation of the dsRNA receptor Toll-like receptor 3 (TLR3) or the ssRNA receptor TLR7 elicits a pregnancy-dependent PE-like syndrome in mice by inducing a pro-inflammatory immune response. CD74 (MHC Class II invariant chain) acts as a chaperone for MHC Class II surface expression on immune cells during antigen presentation and is cleaved into Class II-Associated Invariant Peptide (CLIP) following polyclonal activation of immune cell TLRs. The presence of CLIP in the groove of MHC Class II prevents T cell-dependent death leading to persistent immune cell activation. We hypothesized that genetic deletion of CD74 and subsequent depletion of CLIP on immune cells prevents TLR-induced immune responses and the development of PE in mice. Pregnant WT and CD74 KO mice were given i.p. injections of normal saline (P), poly I:C (TLR3 agonist; P-PIC), or R837 (TLR7 agonist; P-R837) on gestational days 13, 15, and 17 and euthanized on day 18. P-PIC and P-R837 WT mice had significantly increased splenic levels of pro-inflammatory CD3+/gd T cells and plasma levels of the gd T cell-derived cytokines IFNg, TNFa, and IL-17 compared to P WT mice whereas P-PIC and P-R837 CD74 KO mice had significantly increased anti-inflammatory CD3+/gd T cells and no significant increases in plasma IFNg, TNFa, and IL-17 levels. P-PIC and P-R837 CD74 KO mice did not develop the hypertension (gd17 SBP in mmHg: P WT=102±3, P CD74 KO=100±3, P-PIC WT=147±4*, P-PIC CD74 KO=95±3, P-R837 WT=133±2*, P-R837 CD74 KO=97±1; *p<0.05 vs. P WT), endothelial dysfunction, proteinuria, or placental necrosis seen in P-PIC and P-R837 WT mice. In conclusion, CD74 is crucial for the development of TLR-induced PE-like symptoms in mice and CD74/CLIP depletion may be a promising therapeutic target for women with PE.

The Intersection of Metformin and Inflammation

2021 ◽  
Author(s):  
Leena P. Bharath ◽  
Barbara S. Nikolajczyk
Keyword(s):  
Mitochondrial Function ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Remarkable Effect ◽  
Tumor Onset ◽  
Age Related ◽  
Immune Mediated ◽  
Autoimmune Conditions ◽  
Near Term

The biguanide metformin is the most commonly used antidiabetic drug. Recent studies show that metformin not only improves chronic inflammation by improving metabolic parameters but also has a direct anti-inflammatory effect. In light of these findings, it is essential to identify the inflammatory pathways targeted by metformin to develop a comprehensive understanding of the mechanisms of action of this drug. Commonly accepted mechanisms of metformin action include AMPK activation and inhibition of mTOR pathways, which are evaluated in multiple diseases. Additionally, metformin's action on mitochondrial function and cellular homeostasis processes such as autophagy, is of particular interest because of the importance of these mechanisms in maintaining cellular health. Both dysregulated mitochondria and failure of the autophagy pathways, the latter of which impair clearance of dysfunctional, damaged, or excess organelles, affect cellular health drastically and can trigger the onset of metabolic and age-related diseases. Immune cells are the fundamental cell types that govern the health of an organism. Thus, dysregulation of autophagy or mitochondrial function in immune cells has a remarkable effect on susceptibility to infections, response to vaccination, tumor onset, and the development of inflammatory and autoimmune conditions. Here we summarize the latest research on metformin's regulation of immune cell mitochondrial function and autophagy as evidence that new clinical trials on metformin with primary outcomes related to the immune system should be considered to treat immune-mediated diseases over the near term.

Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

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