A noninvasive multianalytical approach establishment for risk assessment and gastric cancer screening.

Effective screening and early detection are critical to improve the prognosis of gastric cancer (GC). Our study aims to explore noninvasive multianalytical biomarkers and construct integrative models for preliminary risk assessment and GC detection. Whole genomewide methylation marker discovery was conducted with CpG tandems target amplification (CTTA) in cfDNA from large asymptomatic screening participants in a high-risk area of GC. The methylation and mutation candidates were validated simultaneously using one plasma from patients at various gastric lesion stages by multiplex profiling with Mutation Capsule Plus (MCP). Helicobacter pylori specific antibodies were detected with a recomLine assay. Integrated models were constructed and validated by the combination of multianalytical biomarkers. A total of 146 and 120 novel methylation markers were found in CpG islands and promoter regions across the genome with CTTA. The methylation markers together with the candidate mutations were validated with MCP and used to establish a 133-methylation-marker panel for risk assessment of suspicious precancerous lesions and GC cases and a 49-methylation-marker panel as well as a 144-amplicon-mutation panel for GC detection. An integrated model comprising both methylation and specific antibody panels performed better for risk assessment than a traditional model (AUC, 0.83 and 0.63, P < .001). A second model for GC detection integrating methylation and mutation panels also outperformed the traditional model (AUC, 0.82 and 0.68, P = .005). Our study established methylation, mutation and H. pylori-specific antibody panels and constructed two integrated models for risk assessment and GC screening. Our findings provide new insights for a more precise GC screening strategy in the future.

Targeting PGLYRP1 promotes antitumor immunity while inhibiting autoimmune neuroinflammation.

Co-inhibitory and checkpoint molecules suppress T cell function in the tumor microenvironment, thereby rendering T cells dysfunctional. Although immune checkpoint blockade is a successful treatment option for multiple human cancers, severe autoimmune-like adverse effects can limit its application. Here, we show that the gene encoding peptidoglycan recognition protein 1 (PGLYRP1) is highly coexpressed with genes encoding co-inhibitory molecules, indicating that it might be a promising target for cancer immunotherapy. Genetic deletion of Pglyrp1 in mice led to decreased tumor growth and an increased activation/effector phenotype in CD8+ T cells, suggesting an inhibitory function of PGLYRP1 in CD8+ T cells. Surprisingly, genetic deletion of Pglyrp1 protected against the development of experimental autoimmune encephalomyelitis, a model of autoimmune disease in the central nervous system. PGLYRP1-deficient myeloid cells had a defect in antigen presentation and T cell activation, indicating that PGLYRP1 might function as a proinflammatory molecule in myeloid cells during autoimmunity. These results highlight PGLYRP1 as a promising target for immunotherapy that, when targeted, elicits a potent antitumor immune response while protecting against some forms of tissue inflammation and autoimmunity.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Resistance to immune checkpoint therapies by tumour-induced T-cell desertification and exclusion: key mechanisms, prognostication and new therapeutic opportunities.

Immune checkpoint therapies (ICT) can reinvigorate the effector functions of anti-tumour T cells, improving cancer patient outcomes. Anti-tumour T cells are initially formed during their first contact (priming) with tumour antigens by antigen-presenting cells (APCs). Unfortunately, many patients are refractory to ICT because their tumours are considered to be 'cold' tumours-i.e., they do not allow the generation of T cells (so-called 'desert' tumours) or the infiltration of existing anti-tumour T cells (T-cell-excluded tumours). Desert tumours disturb antigen processing and priming of T cells by targeting APCs with suppressive tumour factors derived from their genetic instabilities. In contrast, T-cell-excluded tumours are characterised by blocking effective anti-tumour T lymphocytes infiltrating cancer masses by obstacles, such as fibrosis and tumour-cell-induced immunosuppression. This review delves into critical mechanisms by which cancer cells induce T-cell 'desertification' and 'exclusion' in ICT refractory tumours. Filling the gaps in our knowledge regarding these pro-tumoral mechanisms will aid researchers in developing novel class immunotherapies that aim at restoring T-cell generation with more efficient priming by APCs and leukocyte tumour trafficking. Such developments are expected to unleash the clinical benefit of ICT in refractory patients.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas.

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient ß2-microglobulin (ß2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative ß2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human ß2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that ß2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact ß2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of ß2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT.

Neutralizing Anti-Granulocyte Macrophage-Colony Stimulating Factor Autoantibodies Recognize Post-Translational Glycosylations on Granulocyte Macrophage-Colony Stimulating Factor Years Before Diagnosis and Predict Complicated Crohn's Disease.

BACKGROUND & AIMS: Anti-granulocyte macrophage-colony stimulating factor autoantibodies (aGMAbs) are detected in patients with ileal Crohn's disease (CD). Their induction and mode of action during or before disease are not well understood. We aimed to investigate the underlying mechanisms associated with aGMAb induction, from functional orientation to recognized epitopes, for their impact on intestinal immune homeostasis and use as a predictive biomarker for complicated CD. METHODS: We characterized using enzyme-linked immunosorbent assay naturally occurring aGMAbs in longitudinal serum samples from patients archived before the diagnosis of CD (n = 220) as well as from 400 healthy individuals (matched controls) as part of the US Defense Medical Surveillance System. We used biochemical, cellular, and transcriptional analysis to uncover a mechanism that governs the impaired immune balance in CD mucosa after diagnosis. RESULTS: Neutralizing aGMAbs were found to be specific for post-translational glycosylation on granulocyte macrophage-colony stimulating factor (GM-CSF), detectable years before diagnosis, and associated with complicated CD at presentation. Glycosylation of GM-CSF was altered in patients with CD, and aGMAb affected myeloid homeostasis and promoted group 1 innate lymphoid cells. Perturbations in immune homeostasis preceded the diagnosis in the serum of patients with CD presenting with aGMAb and were detectable in the noninflamed CD mucosa. CONCLUSIONS: Anti-GMAbs predict the diagnosis of complicated CD long before the diagnosis of disease, recognize uniquely glycosylated epitopes, and impair myeloid cell and innate lymphoid cell balance associated with altered intestinal immune homeostasis.

Rgs16 promotes antitumor CD8+ T cell exhaustion.

T cells become functionally exhausted in tumors, limiting T cell-based immunotherapies. Although several transcription factors regulating the exhausted T (Tex) cell differentiation are known, comparatively little is known about the regulators of Tex cell survival. Here, we reported that the regulator of G protein signaling 16 (Rgs-16) suppressed Tex cell survival in tumors. By performing lineage tracing using reporter mice in which mCherry marked Rgs16-expressing cells, we identified that Rgs16+CD8+ tumor-infiltrating lymphocytes (TILs) were terminally differentiated, expressed low levels of T cell factor 1 (Tcf1), and underwent apoptosis as early as 6 days after the onset of Rgs16 expression. Rgs16 deficiency inhibited CD8+ T cell apoptosis and promoted antitumor effector functions of CD8+ T cells. Furthermore, Rgs16 deficiency synergized with programmed cell death protein 1 (PD-1) blockade to enhance antitumor CD8+ T cell responses. Proteomics revealed that Rgs16 interacted with the scaffold protein IQGAP1, suppressed the recruitment of Ras and B-Raf, and inhibited Erk1 activation. Rgs16 deficiency enhanced antitumor CD8+ TIL survival in an Erk1-dependent manner. Loss of function of Erk1 decreased antitumor functions of Rgs16-deficient CD8+ T cells. RGS16 mRNA expression levels in CD8+ TILs of patients with melanoma negatively correlated with genes associated with T cell stemness, such as SELL, TCF7, and IL7R, and predicted low responses to PD-1 blockade. This study uncovers Rgs16 as an inhibitor of Tex cell survival in tumors and has implications for improving T cell-based immunotherapies.

PreservCyt Is an Optimal Fixative that Permits Cytologic and Molecular Analyses of Vitreoretinal Lymphoma Biopsies.

Purpose: Vitreoretinal lymphoma (VRL) is a potentially fatal intraocular malignancy. Diagnosis is hampered by poor preservation of morphology and DNA/RNA integrity, which precludes adjunctive molecular analysis. We aimed to determine the optimum fixative protocol for VRL biopsies that permits cytology, IHC/flow cytometry and molecular analyses.Methods: Six fixatives were compared on cultured Pfeiffer cells used as a cellular model. Cells were fixed and evaluated on cellular morphology, antibody staining, DNA/RNA amount and integrity. VRL clinical cases were used as validation and proof-of-concept.Results: PreservCyt was the best fixative for preserving cellular morphology and high-quality RNA/DNA from vitreous fluid biopsies. Cells from clinical VRL cases fixed with PreservCyt showed adequate cellular morphology and IHC positivity. Sufficient DNA was obtained for IgH clonality and MYD88 mutation detection using remnant cytological fluid.Conclusions: PreservCyt maintains good morphology and RNA/DNA integrity suggesting that it is a suitable fixative for VRL diagnosis and molecular analysis.

Single B-Cell Genomic Analyses Differentiate Vitreoretinal Lymphoma from Chronic Inflammation.

PURPOSE: To test whether analyzing DEPArray (Menarini Silicon Biosystems) isolated single B cells from the vitreous fluid can reveal crucial genomic and clinicopathological features to distinguish patients with vitreoretinal lymphoma (VRL) from those with chronic inflammation using immunoglobulin heavy chain (IGH), disease biomarker myeloid differentiation primary response 88 (MYD88)L265P mutation, and copy number profiling. DESIGN: A single-center, retrospective study. PARTICIPANTS: Remnant vitreous biopsies from 7 patients with VRL and 4 patients with chronic inflammation were acquired for molecular analysis. METHODS: Vitreous fluid samples were prefixed in PreservCyt (Hologic) and underwent cytologic analysis and immunohistochemistry examination. Single cells were isolated using the DEPArray NxT system, followed by downstream genomic analysis. MAIN OUTCOME MEASURES: The frequencies of the dominant IGH and MYD88L265P mutation and the genome-wide copy number aberration (CNA) profiles of individual vitreous-isolated B cells were characterized. RESULTS: An average of 10 to 13 vitreous B cells were used in the single-cell IGH and MYD88 analyses. Higher frequencies of dominant IGH (88.8% ± 13.2%) and MYD88L265P mutations (35.0% ± 31.3%) were detected in patients with VRL than in patients with chronic inflammation (65.9% ± 13.4% and 1.5% ± 2.6% for IGH and MYD88L265P, respectively). In a cytology-proven VRL case, all 15 vitreous isolated B cells were derived from the same clone with 100% paired IGH: immunoglobulin light chain (IGK) sequences. Genome-wide copy number profiling revealed a high degree of similarity between B cells from the same patient with VRL, with extensive gains and losses at the same areas across the whole genome. In addition, 14 of 15 B cells showed a BCL2/JH t(14;18) translocation, confirming cellular malignancy with a clonal origin. Clustering analysis of the copy number profiles revealed that malignant B cells derived from different patients with VRL had no common genome-wide signatures. CONCLUSIONS: Single B-cell genomic characterization of the IGH, MYD88L265P mutation, and copy number profile enables VRL diagnosis. Because our study involved only a small cohort, these meaningful proof-of-concept data now warrant further investigation in a larger patient cohort.

Cytologic and Molecular Diagnostics for Vitreoretinal Lymphoma: Current Approaches and Emerging Single-Cell Analyses.

Vitreoretinal lymphoma (VRL) is a rare ocular malignancy that manifests as diffuse large B-cell lymphoma. Early and accurate diagnosis is essential to prevent mistreatment and to reduce the high morbidity and mortality associated with VRL. The disease can be diagnosed using various methods, including cytology, immunohistochemistry, cytokine analysis, flow cytometry, and molecular analysis of bulk vitreous aspirates. Despite these options, VRL diagnosis remains challenging, as samples are often confounded by low cellularity, the presence of debris and non-target immunoreactive cells, and poor cytological preservation. As such, VRL diagnostic accuracy is limited by both false-positive and false-negative outcomes. Missed or inappropriate diagnosis may cause delays in treatment, which can have life-threatening consequences for patients with VRL. In this review, we summarize current knowledge and the diagnostic modalities used for VRL diagnosis. We also highlight several emerging molecular techniques, including high-resolution single cell-based analyses, which may enable more comprehensive and precise VRL diagnoses.

Applying Single-Cell Technology in Uveal Melanomas: Current Trends and Perspectives for Improving Uveal Melanoma Metastasis Surveillance and Tumor Profiling.

Uveal melanoma (UM) is the most common primary adult intraocular malignancy. This rare but devastating cancer causes vision loss and confers a poor survival rate due to distant metastases. Identifying clinical and molecular features that portend a metastatic risk is an important part of UM workup and prognostication. Current UM prognostication tools are based on determining the tumor size, gene expression profile, and chromosomal rearrangements. Although we can predict the risk of metastasis fairly accurately, we cannot obtain preclinical evidence of metastasis or identify biomarkers that might form the basis of targeted therapy. These gaps in UM research might be addressed by single-cell research. Indeed, single-cell technologies are being increasingly used to identify circulating tumor cells and profile transcriptomic signatures in single, drug-resistant tumor cells. Such advances have led to the identification of suitable biomarkers for targeted treatment. Here, we review the approaches used in cutaneous melanomas and other cancers to isolate single cells and profile them at the transcriptomic and/or genomic level. We discuss how these approaches might enhance our current approach to UM management and review the emerging data from single-cell analyses in UM.