Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells

Kyu‐Tae Kim(Samsung Medical Center), Hye Won Lee(Samsung Medical Center), Hae‐Ock Lee(Samsung Medical Center), Sang Cheol Kim(Samsung Medical Center), Yun Jee Seo(Samsung Medical Center), Woosung Chung(Samsung Medical Center), Hye Hyeon Eum(Samsung Medical Center), Do‐Hyun Nam(Samsung Medical Center), Junhyong Kim(Penn Center for AIDS Research), Kyeung Min Joo(Samsung Medical Center), Woong‐Yang Park(Samsung Medical Center)
Genome Biology
June 18, 2015
Cited by 287Open Access
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Abstract

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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