Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma

Abstract

Although human tumours are shaped by the genetic evolution of cancer cells, evidence also suggests that they display hierarchies related to developmental pathways and epigenetic programs in which cancer stem cells (CSCs) can drive tumour growth and give rise to differentiated progeny1. Yet, unbiased evidence for CSCs in solid human malignancies remains elusive. Here we profile 4,347 single cells from six IDH1 or IDH2 mutant human oligodendrogliomas by RNA sequencing (RNA-seq) and reconstruct their developmental programs from genome-wide expression signatures. We infer that most cancer cells are differentiated along two specialized glial programs, whereas a rare subpopulation of cells is undifferentiated and associated with a neural stem cell expression program. Cells with expression signatures for proliferation are highly enriched in this rare subpopulation, consistent with a model in which CSCs are primarily responsible for fuelling the growth of oligodendroglioma in humans. Analysis of copy number variation (CNV) shows that distinct CNV sub-clones within tumours display similar cellular hierarchies, suggesting that the architecture of oligodendroglioma is primarily dictated by developmental programs. Subclonal point mutation analysis supports a similar model, although a full phylogenetic tree would be required to definitively determine the effect of genetic evolution on the inferred hierarchies. Our single-cell analyses provide insight into the cellular architecture of oligodendrogliomas at single-cell resolution and support the cancer stem cell model, with substantial implications for disease management.

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

“…Distinct genetic or epigenetic subclones may compete or collaborate for better fitness in response to microenvironmental or developmental changes (Tirosh et al 2016b). Our observation that distinct subclones within the same CLL sample had similar transcriptome profiles suggests multiple levels of heterogeneity contribute to phenotype, consistent with other published findings (Tirosh et al 2016b). Although our study is limited to only five CLL samples, our findings are in line with accumulating evidence across cancers.…”

“…In contrast, we could detect 203 of 288 single cells (70.0%) with ATM mutation using targeted qPCR. Similarly, Tirosh et al (2016b) found a particular CIC mutation in seven of 1056 single cells (0.66%) using RNAseq reads but improved sensitivity to 28 of 467 (3.9%) using a targeted qPCR assay. Such large discrepancies in detection frequency cannot be made up by merely sequencing deeper, without even considering the prohibitive cost of sequencing hundreds of single cells to great depth.…”

The Antigen ASB4 on Cancer Stem Cells Serves as a Target for CTL Immunotherapy of Colorectal Cancer

“…The existence of CSCs was first reported in a hematologic tumor, and thereafter in a variety of solid tumors, including colon, breast, head and neck, uterine, brain, pancreas, and prostate cancers (4)(5)(6)(7)(8)(9)(10). Intratumoral heterogeneity and the presence of CSC subsets have been established in primary solid-cancer tissues (11). CSCs are resistant to conventional chemotherapy and radiotherapy, most likely due to their quiescent status.…”

Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations

“…As a proxy for true CNV profiles, we generated inferred genomic copy number data from the RNA-seq profiles following a computational method previously established (Patel et al 2014;Tirosh et al 2016). To ensure that the CNV profiles we inferred using this method match what would have been found as directly measured from the DNA, we additionally isolated DNA from bulk cell populations from each of the parental and resistant populations and generated DNA libraries directly (Supplemental Table S4, and supplemental methods).…”

“…The inferCNV algorithm (Tirosh et al 2016) was used to generated inferred copy number profiles from the scRNA-seq data in order to estimate genetic relatedness of the thousands of cells in our 10x datasets, using sliding windows containing 100 consecutive genes. Please see the supplemental methods for additional details.…”