Single-cell lineage analysis reveals genetic and epigenetic interplay in glioblastoma drug resistance
Ontology highlight
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: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.
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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
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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.
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