Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA276026 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Clinicogenetic Risk Prognostication for First-Line Treatment of Symptomatic Follicular Lymphoma
| PRJNA488476 | ENA
Project description:Background: Follicular lymphoma (FL) is a clinically and genetically heterogeneous disease. The prognostic value of somatic mutations has not been systematically evaluated and no genetic factors have been identified that distinguish patients at highest risk of treatment failure. Methods: We performed deep sequencing of 74 genes in 151 patients treated with R-CHOP from a phase III trial. Mutations and clinical factors were incorporated into a risk model using multivariable L1-penalized Cox regression. We validated the risk model in an independent cohort of 107 patients treated with R-CVP. Results: The final clinicogenetic index, the “m7-FLIPI”, contained the FL International Prognostic Index (FLIPI), ECOG performance status, and mutation status of 7 genes (EZH2, ARID1A, MEF2B, EP300, FOXO1, CREBBP and CARD11). In the training cohort, the m7-FLIPI defined a high-risk group (28% of patients) with 5-year failure-free survival (FFS) of 38% vs 77% for the low-risk group (Hazard ratio (HR) 4.1, confidence Interval (CI)=[2.5;6.9], p<0.0001). In the validation cohort, the m7-FLIPI defined a high-risk group (22% of patients) with 5-year FFS of 25% versus 68% (HR 3.6, CI=[2.0;6.4], p<0.0001) in the low-risk group. In both cohorts, the m7-FLIPI outperformed the FLIPI alone. Strikingly, all 66 patients with EZH2 mutations in both cohorts were categorized as low-risk m7-FLIPI, and EZH2 mutations defined a distinct transcriptional profile. Conclusions: The combination of clinical factors and mutations in 7 genes can identify the subset of patients with FL at highest risk of treatment failure. The m7-FLIPI can be widely utilized for risk prognostication and patient stratification.
2015-07-16 | GSE66166 | GEO
Project description:Follicular lymphoma (FL) is an indolent, but incurable subtype of non-Hodgkin lymphoma. These tumor harbor t (14;18) translocation in at least 90% of patients. Recently, activating EZH2 mutations have been Follicular lymphoma (FL) is an indolent, but incurable subtype of non-Hodgkin lymphoma. These tumor harbor t (14;18) translocation in at least 90% of patients. Recently, activating EZH2 mutations have been found in a significant number of patients with FL. Gene expression profiling (GEP) was performed to determine differential gene-expression between the EZH2 mutated vs unmutated subgroups in FL.
2014-07-01 | GSE55267 | GEO
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
2024-10-03 | GSE252687 | GEO
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
2024-10-03 | GSE252642 | GEO
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
2024-10-03 | GSE252416 | GEO
Project description:Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.
2024-10-03 | GSE252344 | GEO
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.
2020-01-06 | MODEL1912170005 | BioModels
Project description:Nodal marginal zone lymphoma is a poorly defined entity in the WHO classification, largely based on criteria by exclusion and the diagnosis often remains subjective. Follicular Lymphoma lacking t(14;18), have similar characteristics which results in a major potential diagnostic overlap which this study aims to dissect. Four subgroups of lymphoma samples (n=56) were analyzed with high-resolution arrayCGH; Nodal marginal zone lymphoma, t(14;18)-negative Follicular Lymphoma, localized t(14:18)-positive Follicular Lymphoma and disseminated t(14;18)-positive Follicular Lymphoma. Gains on chromosomes 7, 8 and 12 were observed in all subgroups. The mean number of aberrations was higher in disseminated t(14;18)-positive Follicular Lymphoma compared to localized t(14:18)-positive Follicular Lymphoma (p<0.01) and the majority of alterations in localized t(14:18)-positive Follicular Lymphoma were also found in disseminated t(14;18)-positive Follicular Lymphoma. Nodal marginal zone lymphoma was marked by 3q gains with amplifications of four genes. A different overall pattern of aberrations was seen in t(14;18)-negative Follicular Lymphoma compared to t(14;18)-positive Follicular Lymphoma. t(14;18)-negative Follicular Lymphoma is marked by specific (focal) gains on chromosome 3 as observed in Nodal marginal zone lymphoma. Our results support the notion that localized t(14:18)-positive Follicular Lymphoma represents an early phase of disseminated t(14;18)-positive Follicular Lymphoma. t(14;18)-negative Follicular Lymphoma bears aberrations that are more alike Nodal marginal zone lymphoma, suggesting a relation between these groups. Four subgroups of follicular lymphoma were analyzed: NMZL (n=14), t-FL (n=12), LOC t+FL (n=16), DIS t+FL (n=14).
2013-07-16 | E-GEOD-40641 | biostudies-arrayexpress