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Genome Res. 2017 Aug;27(8):1300-1311. doi: 10.1101/gr.217331.116. Epub 2017 Jul 5.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia.

Author information

1
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
2
Harvard Medical School, Boston, Massachusetts 02115, USA.
3
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA.
4
Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
5
Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 4HN, United Kingdom.
6
Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
7
Suzhou Precision Medicine Scientific Ltd, Suzhou, China, 215006.
8
Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
9
Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
10
Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
11
Fluidigm Corporation, South San Francisco, California 94080, USA.

Abstract

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

PMID:
28679620
PMCID:
PMC5538547
DOI:
10.1101/gr.217331.116
[Indexed for MEDLINE]
Free PMC Article

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