Intratumoral Transcriptional States of Lymphoma
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ABSTRACT:
PROVIDER: PRJNA389979 | ENA |
REPOSITORIES: ENA
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Project description:Follicular lymphoma (FL) shows heterogenous expression of the cell surface B-cell marker, CD20. In order to investigate whether this heterogeneity also marks underlying transcriptional heterogeneity, we sorted tumor B-cells from 8 FL specimens based upon their intermediate or high expression of CD20 and transcriptionally profiled them. CD20 intermediate and CD20 high tumor B-cells were sorted by FACS, RNA extracted, and profiled using Affymetrix U133 plus 2.0 microarrays.
2013-01-01 | E-GEOD-38816 | biostudies-arrayexpress
Project description:Follicular lymphoma (FL) shows heterogenous expression of the cell surface B-cell marker, CD20. In order to investigate whether this heterogeneity also marks underlying transcriptional heterogeneity, we sorted tumor B-cells from 8 FL specimens based upon their intermediate or high expression of CD20 and transcriptionally profiled them.
2013-01-01 | GSE38816 | GEO
Project description:The goal of this study was to investigate the effect of intratumoral injection of GLA-SE, a TLR4 agonist in stable emulsion (SE), in Balb/c mice with established A20 lymphoma.
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Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182434 | GEO
Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182435 | GEO
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Project description:Copy number analyses of regionally separated intratumoral biopsies of prostate cancers. Intratumoral heterogeneity (ITH) leads to regional biases of the mutational landscape in a single tumor and may influence the single biopsy-based clinical diagnosis and treatment decision. To evaluate the extent of ITH in unifocal prostate cancers (PCAs) that had not been sought, we analyzed multiple regional biopsies from three PCAs using DNA copy number analyses. DNA copy number showed ITH including regional biases in the presentation of a well-known driver of TMPRSS2-ERG fusion. Our analyses identified a substantial level of genetic ITH in unifocal PCAs at the genomic levels, which should be taken into account for the curation of biomarkers in the clinical setting. Four intratumoral biopsies were obtained per tumor for three prostate cancers. Radical prostatectomy tissue from three patients with prostate cancers were obtained. Board-certified pathologists reviewed the hematoxylin&eosin stained sections and identified tumor-rich regions (> 80% purity). We selected four different areas for biopsy that were at least 5mm apart and were comprised of the most common Gleason pattern (the most common histologic patterns with minimal histologic differences). Copy number profiling was performed using Agilent 180K platform according to the manufacturer's protocol.
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Project description:Single cell RNA-sequencing revealed extensive transcriptional cell state diversity in cancer, often observed independently from genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, we performed multi-omics single-cell profiling – integrating DNA methylation, transcriptome, and genotyping within the same cells – of diffuse gliomas, tumors governed by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories, and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to measure cell state heritability and transition dynamics based on high resolution lineage trees directly in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal vs. plastic cell state architectures in IDH-mutant glioma vs. IDH-wildtype glioblastoma, respectively. This work provides a novel framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.
| EGAS00001005472 | EGA