Early cellular mechanisms of type I interferon-driven susceptibility to tuberculosis.

Mycobacterium tuberculosis (Mtb) causes 1.6 million deaths annually. Active tuberculosis correlates with a neutrophil-driven type I interferon (IFN) signature, but the cellular mechanisms underlying tuberculosis pathogenesis remain poorly understood. We found that interstitial macrophages (IMs) and plasmacytoid dendritic cells (pDCs) are dominant producers of type I IFN during Mtb infection in mice and non-human primates, and pDCs localize near human Mtb granulomas. Depletion of pDCs reduces Mtb burdens, implicating pDCs in tuberculosis pathogenesis. During IFN-driven disease, we observe abundant DNA-containing neutrophil extracellular traps (NETs) described to activate pDCs. Cell-type-specific disruption of the type I IFN receptor suggests that IFNs act on IMs to inhibit Mtb control. Single-cell RNA sequencing (scRNA-seq) indicates that type I IFN-responsive cells are defective in their response to IFNγ, a cytokine critical for Mtb control. We propose that pDC-derived type I IFNs act on IMs to permit bacterial replication, driving further neutrophil recruitment and active tuberculosis disease.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes.

To address the current and long-term unmet health needs of the growing population of non-Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18-99). Participants came from 49 US states and included 538 Black/African-Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population-based SEER data (2015-2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population-based SEER rates for indolent B-cell (follicular and marginal zone) and T-cell lymphomas, but were 10%-15% higher than SEER rates for aggressive B-cell subtypes (diffuse large B-cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.

The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

Venetoclax sensitivity in multiple myeloma is associated with B-cell gene expression.

Venetoclax is a highly potent, selective BCL2 inhibitor capable of inducing apoptosis in cells dependent on BCL2 for survival. Most myeloma is MCL1-dependent; however, a subset of myeloma enriched for translocation t(11;14) is codependent on BCL2 and thus sensitive to venetoclax. The biology underlying this heterogeneity remains poorly understood. We show that knockdown of cyclin D1 does not induce resistance to venetoclax, arguing against a direct role for cyclin D1 in venetoclax sensitivity. To identify other factors contributing to venetoclax response, we studied a panel of 31 myeloma cell lines and 25 patient samples tested for venetoclax sensitivity. In cell lines, we corroborated our previous observation that BIM binding to BCL2 correlates with venetoclax response and further showed that knockout of BIM results in decreased venetoclax sensitivity. RNA-sequencing analysis identified expression of B-cell genes as enriched in venetoclax-sensitive myeloma, although no single gene consistently delineated sensitive and resistant cells. However, a panel of cell surface makers correlated well with ex vivo prediction of venetoclax response in 21 patient samples and may serve as a biomarker independent of t(11;14). Assay for transposase-accessible chromatin sequencing of myeloma cell lines also identified an epigenetic program in venetoclax-sensitive cells that was more similar to B cells than that of venetoclax-resistant cells, as well as enrichment for basic leucine zipper domain-binding motifs such as BATF. Together, these data indicate that remnants of B-cell biology are associated with BCL2 dependency and point to novel biomarkers of venetoclax-sensitive myeloma independent of t(11;14).

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.

Flt3L Treatment of Bone Marrow Donors Increases Graft Plasmacytoid Dendritic Cell Content and Improves Allogeneic Transplantation Outcomes.

A higher number of donor plasmacytoid dendritic cells (pDCs) is associated with increased survival and reduced graft-versus-host disease (GVHD) in human recipients of unrelated donor bone marrow (BM) grafts, but not granulocyte colony-stimulating factor (G-CSF)-mobilized peripheral blood grafts. We show that in murine models, donor BM pDCs are associated with increased survival and decreased GVHD compared with G-CSF-mobilized pDCs. To increase the content of pDCs in BM grafts, we studied the effect of FMS-like tyrosine kinase 3 ligand (Flt3L) treatment of murine BM donors on transplantation outcomes. Flt3L treatment (300 µg/kg/day) resulted in a schedule-dependent increase in the content of pDCs in the BM. Mice treated on days -4 and -1 had a >5-fold increase in pDC content without significant changes in numbers of HSCs, T cells, B cells, and natural killer cells in the BM graft. In an MHC-mismatched murine transplant model, recipients of Flt3L-treated T cell-depleted (TCD) BM (TCD F-BM) and cytokine-untreated T cells had increased survival and decreased GVHD scores with fewer Th1 and Th17 polarized T cells post-transplantation compared with recipients of equivalent numbers of untreated donor TCD BM and T cells. Gene array analyses of pDCs from Flt3L-treated human and murine donors showed up-regulation of adaptive immune pathways and immunoregulatory checkpoints compared with pDCs from untreated BM donors. Transplantation of TCD F-BM plus T cells resulted in no loss of the graft-versus-leukemia (GVL) effect compared with grafts from untreated donors in 2 murine GVL models. Thus, Flt3L treatment of BM donors is a novel method for increasing the pDC content in allografts, improving survival, and decreasing GVHD without diminishing the GVL effect.

Oligoclonal T Cells Transiently Expand and Express Tim-3 and PD-1 Following Anti-CD19 CAR T Cell Therapy: A Case Report.

Clinical trials of chimeric antigen receptor (CAR) T cells in hematologic malignancy associate remissions with two profiles of CAR T cell proliferation kinetics, which differ based upon costimulatory domain. Additional T cell intrinsic factors that influence or predict clinical response remain unclear. To address this gap, we report the case of a 68-year-old woman with refractory/relapsed diffuse large B cell lymphoma (DLBCL), treated with tisagenlecleucel (anti-CD19), with a CD137 costimulatory domain (4-1BB) on an investigational new drug application (#16944). For two months post-infusion, the patient experienced dramatic regression of subcutaneous nodules of DLBCL. Unfortunately, her CAR T exhibited kinetics unassociated with remission, and she died of DLBCL-related sequelae. Serial phenotypic analysis of peripheral blood alongside sequencing of the ß-peptide variable region of the T cell receptor (TCRß) revealed distinct waves of oligoclonal T cell expansion with dynamic expression of immune checkpoint molecules. One week prior to CAR T cell contraction, T cell immunoglobulin mucin domain 3 (Tim-3) and programmed cell death protein 1 (PD-1) exhibited peak expressions on both the CD8 T cell (Tim-3 ≈ 50%; PD-1 ≈ 17%) and CAR T cell subsets (Tim-3 ≈ 78%; PD-1 ≈ 40%). These correlative observations draw attention to Tim-3 and PD-1 signaling pathways in context of CAR T cell exhaustion.

DNA methylation profiling in human B cells reveals immune regulatory elements and epigenetic plasticity at Alu elements during B-cell activation.

Memory is a hallmark of adaptive immunity, wherein lymphocytes mount a superior response to a previously encountered antigen. It has been speculated that epigenetic alterations in memory lymphocytes contribute to their functional distinction from their naive counterparts. However, the nature and extent of epigenetic alterations in memory compartments remain poorly characterized. Here we profile the DNA methylome and the transcriptome of B-lymphocyte subsets representing stages of the humoral immune response before and after antigen exposure in vivo from multiple humans. A significant percentage of activation-induced losses of DNA methylation mapped to transcription factor binding sites. An additional class of demethylated loci mapped to Alu elements across the genome and accompanied repression of DNA methyltransferase 3A. The activation-dependent DNA methylation changes were largely retained in the progeny of activated B cells, generating a similar epigenetic signature in downstream memory B cells and plasma cells with distinct transcriptional programs. These findings provide insights into the methylation dynamics of the genome during cellular differentiation in an immune response.

Successful treatment of severe immune hemolytic anemia after allogeneic stem cell transplantation with bortezomib: report of a case and review of literature.

BACKGROUND: Immune hemolytic anemia is a well-known complication after allogeneic hematopoietic stem cell transplantation (HSCT). Posttransplant hemolytic anemia results in increased red blood cell transfusions and medical sequelae including iron overload. CASE REPORT: We present a case report of immune hemolytic anemia that occurred after allogeneic HSCT from an ABO major-mismatched, HLA-matched unrelated donor. The patient had high anti-donor A type antibodies that were unresponsive to treatment with steroids and rituximab, resulting in persistent transfusion dependence. A detailed time course of anti-A titers, plasma cell content of the marrow, and B-cell content of the blood is presented. Treatment with bortezomib, a protease inhibitor, eliminated residual host-type plasma cells secreting anti-A and restored normal donor-derived erythropoiesis. CONCLUSION: This report, and a review of literature for treatment of immune hemolytic anemia after allogeneic HSCT, supports the utility of bortezomib as plasma cell-targeted therapy in this setting.

Translational applications of flow cytometry in clinical practice.

Flow cytometry has evolved over the past 30 y from a niche laboratory technique to a routine tool used by clinical pathologists and immunologists for diagnosis and monitoring of patients with cancer and immune deficiencies. Identification of novel patterns of expressed Ags has led to the recognition of cancers with unique pathophysiologies and treatment strategies. FACS had permitted the isolation of tumor-free populations of hematopoietic stem cells for cancer patients undergoing stem cell transplantation. Adaptation of flow cytometry to the analysis of multiplex arrays of fluorescent beads that selectively capture proteins and specific DNA sequences has produced highly sensitive and rapid methods for high through-put analysis of cytokines, Abs, and HLA genotypes. Automated data analysis has contributed to the development of a "cytomics" field that integrates cellular physiology, genomics, and proteomics. In this article, we review the impact of the flow cytometer in these areas of medical practice.

The unnecessary use of short tandem repeat testing on bone marrow samples in patients after 1 year following allogeneic hematopoietic stem cell transplant.

OBJECTIVES: To determine whether the information provided by short tandem repeat (STR) testing and bone marrow (BM) biopsy specimens following hematopoietic stem cell transplant (HSCT) provides redundant information, leading to test overutilization, without additional clinical benefit. METHODS: Cases with synchronous STR and flow cytometric immunophenotyping (FCI) testing, as part of the BM evaluation, were assessed for STR/FCI concordance. RESULTS: Of 1199 cases (410 patients), we found the overall concordance between STR and FCI was 93%, with most cases (1063) classified as STR-/FCI-. Of all discordant cases, 75 (6%) were STR+/FCI-, with only 5 (6.7%) cases best explained as identification of disease relapse. Eight cases were STR-/FCI+, representing relapsed/residual disease. Analysis of cases 1 year or more from transplant (54% of all cases) indicated only 9 (1.5%) were STR+/FCI-, and none uniquely identified relapse. CONCLUSIONS: These data suggest that STR analysis performed 1 year or more post-HSCT does not identify unknown cases of relapse. Furthermore, while STR testing is critical for identifying graft failure/rejection within the first year posttransplant, FCI appears superior to STR at detecting late relapses with low-level disease. Therefore, STR testing from patients 1 year or more post-HSCT may be unnecessary, as BM biopsy evaluation is sufficient to identify disease relapse.

Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. METHODS: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. RESULTS: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). CONCLUSIONS: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

SAMHD1 expression contributes to doxorubicin resistance and predicts survival outcomes in diffuse large B-cell lymphoma patients.

Diffuse large B-cell lymphoma (DLBCL) is a commonly diagnosed, aggressive non-Hodgkin's lymphoma. While R-CHOP chemoimmunotherapy is potentially curative, about 40% of DLBCL patients will fail, highlighting the need to identify biomarkers to optimize management. SAMHD1 has a dNTPase-independent role in promoting resection to facilitate DNA double-strand break (DSB) repair by homologous recombination. We evaluated the relationship of SAMHD1 levels with sensitivity to DSB-sensitizing agents in DLBCL cells and the association of SAMHD1 expression with clinical outcomes in 79 DLBCL patients treated with definitive therapy and an independent cohort dataset of 234 DLBCL patients. Low SAMHD1 expression, Vpx-mediated, or siRNA-mediated degradation/depletion in DLBCL cells was associated with greater sensitivity to doxorubicin and PARP inhibitors. On Kaplan-Meier log-rank survival analysis, low SAMHD1 expression was associated with improved overall survival (OS), which on subset analysis remained significant only in patients with advanced stage (III-IV) and moderate to high risk (2-5 International Prognostic Index (IPI)). The association of low SAMHD1 expression with improved OS remained significant on multivariate analysis independent of other adverse factors, including IPI, and was validated in an independent cohort. Our findings suggest that SAMHD1 expression mediates doxorubicin resistance and may be an important prognostic biomarker in advanced, higher-risk DLBCL patients.

Flagellin, a TLR5 agonist, reduces graft-versus-host disease in allogeneic hematopoietic stem cell transplantation recipients while enhancing antiviral immunity.

Graft-versus-host disease (GVHD) is a major cause of morbidity and mortality in patients treated with allogeneic hematopoietic stem cell transplantation (HSCT). Posttransplant immunosuppressive drugs incompletely control GVHD and increase susceptibility to opportunistic infections. In this study, we used flagellin, a TLR5 agonist protein (∼50 kDa) extracted from bacterial flagella, as a novel experimental treatment strategy to reduce both acute and chronic GVHD in allogeneic HSCT recipients. On the basis of the radioprotective effects of flagellin, we hypothesized that flagellin could ameliorate GVHD in lethally irradiated murine models of allogeneic HSCT. Two doses of highly purified flagellin (administered 3 h before irradiation and 24 h after HSCT) reduced GVHD and led to better survival in both H-2(b) → CB6F1 and H-2(K) → B6 allogeneic HSCT models while preserving >99% donor T cell chimerism. Flagellin treatment preserved long-term posttransplant immune reconstitution characterized by more donor thymic-derived CD4(+)CD25(+)Foxp3(+) regulatory T cells and significantly enhanced antiviral immunity after murine CMV infection. The proliferation index and activation status of donor spleen-derived T cells and serum concentration of proinflammatory cytokines in flagellin-treated recipients were reduced significantly within 4 d posttransplant compared with those of the PBS-treated control recipients. Allogeneic transplantation of radiation chimeras previously engrafted with TLR5 knockout hematopoietic cells showed that interactions between flagellin and TLR5 expressed on both donor hematopoietic and host nonhematopoietic cells were required to reduce GVHD. Thus, the peritransplant administration of flagellin is a novel therapeutic approach to control GVHD while preserving posttransplant donor immunity.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry.

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T-lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Immunosuppression is a conserved driver of tuberculosis susceptibility.

Mycobacterium tuberculosis ( Mtb ) causes 1.6 million deaths a year 1 . However, no individual mouse model fully recapitulates the hallmarks of human tuberculosis disease. Here we report that a comparison across three different susceptible mouse models identifies Mtb -induced gene signatures that predict active TB disease in humans significantly better than a signature from the standard C57BL/6 mouse model. An increase in lung myeloid cells, including neutrophils, was conserved across the susceptible mouse models, mimicking the neutrophilic inflammation observed in humans 2,3 . Myeloid cells in the susceptible models and non-human primates exhibited high expression of immunosuppressive molecules including the IL-1 receptor antagonist, which inhibits IL-1 signaling. Prior reports have suggested that excessive IL-1 signaling impairs Mtb control 4-6 . By contrast, we found that enhancement of IL-1 signaling via deletion of IL-1 receptor antagonist promoted bacterial control in all three susceptible mouse models. IL-1 signaling enhanced cytokine production by lymphoid and stromal cells, suggesting a mechanism for IL-1 signaling in promoting Mtb control. Thus, we propose that myeloid cell expression of immunosuppressive molecules is a conserved mechanism exacerbating Mtb disease in mice, non-human primates, and humans.

Profiling endogenous, environmental, and infectious disease mutational signatures in blastic plasmacytoid dendritic cell neoplasms.

BACKGROUND: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematopoietic disease derived from plasmacytoid dendritic lineage cells. The disease typically shows skin as well as frequent bone marrow and peripheral blood involvement. However, the pathogenesis of this disease is still not well understood. While somatic point mutations and genetic rearrangements have been described in BPDCN, the types and origins of these mutations and relationships to other cancer types is not well understood. MATERIALS AND METHODS: To probe the origins of BPDCN, we analyzed the exome sequence data of 9 tumor-normal pair cases of BPDCN. We utilized SignatureAnalyzer, SigProfiler and a custom microbial analysis pipeline to understand the relevance of endogenous and environmental mutagenic processes. RESULTS: Our results identified a significant tobacco exposure and aging genetic signature as well as signatures related to nucleotide excision repair deficiency, ultra violet (UV) exposure, and endogenous deamination in BPDCN. We also assessed the samples for microbial infectious disease organisms but did not find a link to a microbial etiology. CONCLUSION: The identification of a tobacco exposure and aging genetic signature in patients with BPDCN suggests that environmental and endogenous genetic changes may be central to the oncogenesis of BPDCN.