Gene expression profiling of newly-diagnosed DLBCL samples with the NanoString nCounter technology
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ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin, genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using highly-sensitive transcriptome profiling of untreated DLBCL tissues, we here assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells, in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.
Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin, genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using highly-sensitive transcriptome profiling of untreated DLBCL tissues, we here assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells, in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.
Project description:Diffuse large B-cell lymphoma (DLBCL) exhibits heterogeneous clinical outcomes even in tumors of the same stage and with similar pathological characteristics. A substantial number of patients with DLBCL still fail to be cured despite recent improvements in therapy. In this study, we used formalin fixed paraffin embedded (FFPE) tumor samples for microarray gene expression profiling to develop robust prognostic profiles for DLBCL.
Project description:Diffuse large B-cell lymphoma (DLBCL) exhibits heterogeneous clinical outcomes even in tumors of the same stage and with similar pathological characteristics. A substantial number of patients with DLBCL still fail to be cured despite recent improvements in therapy. In this study, we used formalin fixed paraffin embedded (FFPE) tumor samples for microarray gene expression profiling to develop robust prognostic profiles for DLBCL. We performed a retrospective microarray gene expression profiling study of FFPE from a cohort of DLBCL patients using the whole genome cDNA mediated Annealing, Selection and Ligation (WG-DASL) assay. After removing poor-quality samples, data from 164 patients were used for statistical analyses to develop and validate a prognostic gene expression signature using a gradient lasso and leave-one-out cross-validation process.
Project description:Elevated level of circulating cell-free DNA (cfDNA) has been associated with poor prognosis and relapse in patients with diffuse large B-cell lymphoma (DLBCL), but the tumor-specific molecular alterations in cfDNA with prognostic significance remain unclear. We investigated the association between 5-hydroxymethylcytosines (5hmC), a mark of active demethylation and gene activation, in cfDNA from blood plasma and prognosis in DLBCL. We used the 5hmC-Seal, a highly sensitive chemical labeling technique, to profile genome-wide 5hmC in plasma cfDNA samples from 48 newly diagnosed patients with DLBCL at the University of Chicago between 2010 and 2012. Patients were followed through December 31, 2017. We found a distinct genomic distribution of 5hmC in cfDNA marking tissue-specific enhancers, consistent with their potential roles in gene regulation. The 5hmC profiles in cfDNA differed by cell-of-origin, and were associated with clinical prognostic features including Ann Arbor stage, serum lactate dehydrogenase (LDH) level, and the International Prognostic Index (IPI). We developed a 29 gene-based weighted prognostic score (wp-score) for predicting event-free survival (EFS) and overall survival (OS), by applying the elastic net regularization on the Cox proportional hazard model. The wp-scores showed significantly improved prognostic accuracy and/or sensitivity/specificity than the established prognostic factors. In multivariate Cox models, patients with high wp-scores were associated with worse event-free survival (Hazard Ratio [HR] = 9.17, 95% confidence interval [CI] = 2.01- 41.89, p = 0.004), compared with those in the low risk group. Our findings suggest that the 5hmC signatures in cfDNA at the time of diagnosis are associated with clinical outcomes in DLBCL and may provide a novel non-invasive prognostic approach for DLBCL.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention. golub-00095 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Tissue Sites: Lymphoid tissue Material Types: synthetic_DNA, synthetic_RNA, organism_part Cell Types: B-Lymphocyte Disease States: Diffuse large B-cell Lymphoma, Follicular Lymphoma
Project description:Diffuse large B-cell lymphoma (DLBCL) represents the most common subtype of malignant lymphoma and is heterogeneous with respect to morphology, biology, and clinical presentation.However, a robust prognostic factor based on cell biology of DLBCL has not yet been determined.To find the biomarker which may associate with clinical outcome in patients with DLBCL, microarray analysis was performed to screen a novel biomarker.
Project description:The aggressive B cell lymphoma diffuse large B cell lymphoma (DLBCL) is a heterogeneous entity that requires more precise monitoring and prognosis molecular tools. Extracellular vesicles that are secreted by all cell types are currently recognized as serving as a proxy for the cell of origin. Utilizing cutting-edge mass spectrometry, the current study described and assessed the plasma small extracellular vesicles (sEVs) proteome's diagnostic and prognostic potential. The presence of DLBCL has a significant impact on the sEV proteome, and several proteins substantially correlate with DLBCL.Nevertheless, no proteins that highly correlated with non-GCB or GCB were found.
Project description:Mathematical modeling of immune modulation by glucocorticoids
Konstantin Yakimchuk
https://doi.org/10.1016/j.biosystems.2019.104066
Abstract
The cellular and molecular mechanisms of immunomodulatory actions of glucocorticoids (GC) remain to be identified. Using our experimental findings, a mathematical model based on a system of ordinary differential equations for characterization of the regulation of anti-tumor immune activity by the both direct and indirect GC effects was generated to study the effects of GC treatment on effector CD8+ T cells, GC-generated tolerogenic dendritic cells (DC), regulatory T cells and the growth of lymphoma cells. In addition, we compared the data from in vivo and in silico experiments. The mathematical simulations indicated that treatment with GCs may suppress anti-tumor immune response in a dose-dependent manner. The model simulations were in line with earlier experimental observations of inhibitory effects of GCs on T and NK cells and DCs. The results of this study might be useful for predicting clinical outcomes in patients receiving GC therapy.
Project description:Gastric cancer (GC) is a one of the most common gastroenterological carcinoma, but its prognostic and immunological characteristics is not fully illustrated. Here we build an immune-associated gene prognostic model to predict the prognosis of GC. We performed RNA sequencing of 33 GC patients to verified the prognostic model.