Single cell RNA sequencing and Whole Genome Sequencing on different cells from the same sample for a triple negative patient derived xenograft and ovarian cancer cell lines.

Study ID Alternative Stable ID Type
EGAS00001003387 Other

Study Description

Measuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we present clonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell population. We apply clonealign to triple-negative breast cancer patient derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either DNA-Seq or RNA-Seq alone.

Study Datasets 1 dataset.

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Dataset ID Description Technology Samples
10X genomics chromium single-cell RNA-sequencing of (i) patient derived triple negative breast cancer xenograft (ii) primary tumour and ascites ovarian cancer cell lines at tumour recurrence.
NextSeq 550 3

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