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.
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.
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.
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).
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).
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.