Single-cell lineage analysis reveals genetic and epigenetic interplay in glioblastoma drug resistance
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ABSTRACT:
PROVIDER: PRJNA595940 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10704200_1.fastq.gz | Fastqsanger.gz | |||
| SRR10704200_2.fastq.gz | Fastqsanger.gz | |||
| SRR10704201_1.fastq.gz | Fastqsanger.gz | |||
| SRR10704201_2.fastq.gz | Fastqsanger.gz | |||
| SRR10704202_1.fastq.gz | Fastqsanger.gz |
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Project description:Single-cell lineage analysis reveals genetic and epigenetic interplay in glioblastoma drug resistance
| PRJNA595940 | ENA
Project description:Drug resistance is a central problem in cancer therapy, yet the underlying mechanisms remain obscure. Cancer cells typically acquire resistance via genetic mutations, but may also adopt reversible epigenetic phenotypes that allow them to persist through drug exposure. In glioblastoma, poor efficacy of receptor tyrosine kinase (RTK) therapies has been alternatively ascribed to genetic heterogeneity or to epigenetic transitions that circumvent signaling blockade.
2020-06-18 | GSE142119 | GEO
Project description:BackgroundTumors can evolve and adapt to therapeutic pressure by acquiring genetic and epigenetic alterations that may be transient or stable. A precise understanding of how such events contribute to intratumoral heterogeneity, dynamic subpopulations, and overall tumor fitness will require experimental approaches to prospectively label, track, and characterize resistant or otherwise adaptive populations at the single-cell level. In glioblastoma, poor efficacy of receptor tyrosine kinase (RTK) therapies has been alternatively ascribed to genetic heterogeneity or to epigenetic transitions that circumvent signaling blockade.ResultsWe combine cell lineage barcoding and single-cell transcriptomics to trace the emergence of drug resistance in stem-like glioblastoma cells treated with RTK inhibitors. Whereas a broad variety of barcoded lineages adopt a Notch-dependent persister phenotype that sustains them through early drug exposure, rare subclones acquire genetic changes that enable their rapid outgrowth over time. Single-cell analyses reveal that these genetic subclones gain copy number amplifications of the insulin receptor substrate-1 and substrate-2 (IRS1 or IRS2) loci, which activate insulin and AKT signaling programs. Persister-like cells and genomic amplifications of IRS2 and other loci are evident in primary glioblastomas and may underlie the inefficacy of targeted therapies in this disease.ConclusionsA method for combined lineage tracing and scRNA-seq reveals the interplay between complementary genetic and epigenetic mechanisms of resistance in a heterogeneous glioblastoma tumor model.
| S-EPMC7364565 | biostudies-literature
Project description:Combined model of in vitro and in vivo resistance to alkylating agents (BCNU and Temozolomide) in glioblastoma multiforme. Three matched pairs of surgical specimens from initial (DI, ME, LX) and repeat (DIR, MER, LXR) tumor resection were used to establish cell lines. Patients received BCNU chemotherapy between the two operations, which rendered the sublines from repeat surgery more resistant to BCNU than those from initial surgery. Each the pre-chemotherapeutic set and the post-chemotherapeutic set of cell lines was selected in vitro for resistance to either BCNU or Temozolomide. 9 in vitro selected sublines were established. Of those 4 (2 selected for BCNU [ME-BCNU, DI-BCNU] and 2 selected for Temozolomide [ME-TMZ, LX-TMZ) were generated from the parental/sensitive pre-chemotherapeutic set and 5 (2 selected for BCNU [DIR-BCNU, MER-BCNU] and 3 selected for Temozolomide [DIR-TMZ, MER-TMZ, LXR-TMZ) from the in vivo resistant post-chemotherapeutic set. The model included a total of 15 sublines. Genome-wide high-resolution gene copy number profiling was performed on each subline using 42,000-feature cDNA microarrays. Utilizing the same microarray platform genome-wide gene expression profiles were generated. Biostatistical tools were employed to identify signatures of resistance to alkylating agents both at the genetic as well as at the transcriptomic levels. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Series_type = Logical Set Keywords: other
2006-01-27 | GSE2221 | GEO
Project description:Drug resistance poses a significant clinical challenge, and comprehending the mechanisms underlying this resistance can facilitate the design of novel inhibitors and advance cancer treatment. The REPLACE system was employed to examine resistance mutations in MEK1 during the administration of 3 allosteric inhibitors. To explore the accumulation of mutations in various RNAs during the MEK1 evolution experiment, we established a barcoded library and conducted lineage tracing of replicative RNAs carrying MEK1 throughout the drug resistance evolution process.
2024-09-30 | GSE271875 | GEO
Project description:Oxamniquine resistance evolved in the human blood fluke (Schistosoma mansoni) in Brazil in the 1970s. We crossed parental parasites differing ~500-fold in drug response, determined drug sensitivity and marker segregation in clonally-derived F2s, and identified a single QTL (LOD=31) on chromosome 6. A sulfotransferase was identified as the causative gene using RNAi knockdown and biochemical complementation assays and we subsequently demonstrated independent origins of loss-of-function mutations in field-derived and laboratory-selected resistant parasites. These results demonstrate the utility of linkage mapping in a human helminth parasite, while crystallographic analyses of protein-drug interactions illuminate the mode of drug action and provide a framework for rational design of oxamniquine derivatives that kill both S. mansoni and S. haematobium, the two species responsible for >99% of schistosomiasis cases worldwide. mRNA profiles from adult worms resistant (HR, 3 replicates) and susceptible (LE, 2 replicates) to the oxamniquine drug were compared to identify differential expression in genes related to drug resistance
2013-12-05 | E-GEOD-51847 | biostudies-arrayexpress
Project description:We identified three novel chromatin-driven subtypes of glioblastoma stem cells, regulated by specific essential transcription factors and with specific drug sensitivities.
2019-11-14 | GSE109398 | GEO
Project description:Background
Development of drug resistance is often dynamic and requires time series experiments to understand these processes. The most abundant source of information regarding such progressive activity is tracking single cell gene expression data. Our understanding of the imminent role of phenotype switching in modifying therapeutic response has considerably increased.The analysis of single cell transcriptomic data along with machine learning models offers a constructive tool to describe dynamic cellular processes to elucidate cell-to-cell variability within the population after drug exposure. Here we studied how genomic and non-genomic basis for transcriptome differences can influence the developing drug resistance and clonal fitness.
Results: This study describes the scRNA-seq analysis of untreated, treated and drug holiday PDX transplants of TNBC exposed to platinum. This builds from the materials and findings of our paper by Salehi, Kabeer et al, Nature 2021 (PMID: 3416307), in which we established the measurement of fitness in this time series. In the new work, we have mapped single cell transcription in conjunction, allowing a dissection of copy number driven (in-cis) clonal transcription and non-clonal, ie non-genomic transcription. We developed an experimental and computational platform consisting of four major steps: (1) timeseries sampling of scRNA-seq 126,556 TNBC PDX cells >2.5 years; (2) clustering the tumor cells into groups reflecting their drug sensitivity, resistance and other characteristics; (3) performing trajectory inference to identify lineages of transitional processes along with drug and tumor progression; (4) estimating the longitudinal gene expression of each lineage; and identifying significant genes between-lineage differential expression modules. Interestingly, we identified a small group of genes (18.9%) that display a new cell state that is not towards the untreated (Un-Rx) nor towards treated Rx which are distant from untreated cell states. The pathway results are shared between two different quantitative measurements of clone-aware differential expression and pseudotime analysis.
| EGAS00001007242 | EGA
Project description:Lineage tracing of patient-derived glioblastoma cells to reconstruct plasticity
| PRJEB39224 | ENA