Genome Biology · 2015 · 287 citations · 57 references
MedicineCancer BiologyOncologyMolecular OncologyTranscriptomicsTumor HeterogeneityCancer GenomicsCancer TranscriptomicsPhenotypic HeterogeneitySingle-cell GenomicsSingle-cell TranscriptomicsTranscriptomic HeterogeneityCancer Heterogeneity AnalysisIntra-tumor HeterogeneityLung AdenocarcinomaSingle Nucleotide Variant AnalysisSingle-cell VariabilitySingle-nucleotide VariantsTherapeutic Response HeterogeneityXenograft Survival
BACKGROUND: Intra-tumoral genetic and functional heterogeneity correlates with cancer clinical prognoses. However, the mechanisms by which intra-tumoral heterogeneity impacts therapeutic outcome remain poorly understood. RNA sequencing (RNA-seq) of single tumor cells can provide comprehensive information about gene expression and single-nucleotide variations in individual tumor cells, which may allow for the translation of heterogeneous tumor cell functional responses into customized anti-cancer treatments. RESULTS: We isolated 34 patient-derived xenograft (PDX) tumor cells from a lung adenocarcinoma patient tumor xenograft. Individual tumor cells were subjected to single cell RNA-seq for gene expression profiling and expressed mutation profiling. Fifty tumor-specific single-nucleotide variations, including KRAS(G12D), were observed to be heterogeneous in individual PDX cells. Semi-supervised clustering, based on KRAS(G12D) mutant expression and a risk score representing expression of 69 lung adenocarcinoma-prognostic genes, classified PDX cells into four groups. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRAS(G12D) and low risk score. CONCLUSIONS: Single-cell RNA-seq on viable PDX cells identified a candidate tumor cell subgroup associated with anti-cancer drug resistance. Thus, single-cell RNA-seq is a powerful approach for identifying unique tumor cell-specific gene expression profiles which could facilitate the development of optimized clinical anti-cancer strategies.
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Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li, Richard Durbin · Bioinformatics · 2009 · 62.8K citations · Full text
STAR: ultrafast universal RNA-seq aligner
Alexander Dobin, Carrie Davis, Felix Schlesinger et al. · Bioinformatics · 2012 · 56.2K citations · Full text