Abstract
Critical cancer mutations are often regional and mosaic, confounding the efficacy of targeted therapeutics. Single cell mRNA sequencing (scRNA-seq) has enabled unprecedented studies of intra-tumor heterogeneity and its role in cancer progression, metastasis, and treatment resistance. When coupled with DNA sequencing, scRNA-seq allows one to infer the in vivo impact of genomic alterations on gene expression. This combination can be used to reliably distinguish neoplastic from non-neoplastic cells, to correlate paracrine-signaling pathways between neoplastic cells and stroma, and to map expression signatures to inferred clones and phylogenies. Here we review recent advances in scRNA-seq, with a special focus on cancer. We discuss the challenges and prospects of combining scRNA-seq with DNA sequencing to assess intra-tumor heterogeneity.
Highlights
Next-generation sequencing (NGS) based studies have identified critical genetic alterations in a variety of malignancies (Brennan et al, 2013; Hoadley et al, 2014; Bai et al, 2015; Furnari et al, 2015; Ceccarelli et al, 2016; Cancer Genome Atlas Research Network, et al, 2016; Wang J. et al, 2016)
Assessing tumor heterogeneity from bulk RNA or DNA extractions is limited to either inter-tumor comparisons (Cancer Genome Atlas Research Network, 2008; Müller et al, 2015), or comparisons across a small number of stereotactic biopsies (Gerlinger et al, 2012)
We propose separating cells based on four sources of evidence: (1) large-scale copy-number variants (CNVs) that are observed in both platforms; (2) the variant-allele frequencies (VAFs) of germline SNVs, compared between platforms; (3) somatic SNVs found in both platforms; and (4) a clustering of scRNA-seq transcriptional profiles
Summary
Next-generation sequencing (NGS) based studies have identified critical genetic alterations in a variety of malignancies (Brennan et al, 2013; Hoadley et al, 2014; Bai et al, 2015; Furnari et al, 2015; Ceccarelli et al, 2016; Cancer Genome Atlas Research Network, et al, 2016; Wang J. et al, 2016). The most robust approaches to quantifying SNVs in single cells have integrated orthogonal data, to classify cells based on expressed mutations that were called first from DNA sequencing. To distinguish malignant from non-malignant cells, they developed a strategy to quantify the sensitivity of scRNA-seq in detecting somatic SNVs. The authors compare the variant-allele frequencies (VAFs) observed in exome-seq to the cellular frequencies of expressed mutations found in scRNA-seq.
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