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

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.

The inflammasome of circulatory collapse: single cell analysis of survival on extracorporeal life support

“…This technology holds the promise to uncover new information about the pathophysiology of complex disease states in high detail. Initial efforts in this direction with human material have focused on cancer (4,(19)(20)(21), although examples in other diseases are emerging (22,23) including one example of identification of a PBMC repertoire associated tuberculosis progression (2) and a second related to survival in acute infectious disease based on analysis of ~100 cells from 2 patients (3).…”

Unraveling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA-sequencing

“…As a result, up to now, these techniques have not been widely used for parallel mutation and scRNA-seq analysis in cancer. Methods that combine targeted single cell gene expression and mutation analysis have also been reported (Cheow et al, 2016; Wang et al, 2017), but these approaches have the limitation that only the expression of a limited number of pre-selected genes can be analyzed per cell.…”

A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference

“…Our method could potentially be improved by including violations of the perfect phylogeny. Tumor phylogeny studies [30,13,26] show possible violations of ISA in single-cell sequencing datasets. More comprehensive evaluation of these violations could be performed by analyzing the read counts in conjunction with tumor phylogeny inference.…”