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

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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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-015-0692-3) contains supplementary material, which is available to authorized users.

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          Most cited references 24

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          Gene-expression profiles predict survival of patients with lung adenocarcinoma.

          Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.
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            Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft

            Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumor progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis, and a xenograft derived from the primary tumor. The metastasis contained two de novo mutations and a large deletion not present in the primary tumor, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumor mutations, and displayed a mutation enrichment pattern that paralleled the metastasis (16 of 20 genes). Two overlapping large deletions, encompassing CTNNA1, were present in all three tumor samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared to the primary tumor suggest that secondary tumors may arise from a minority of cells within the primary.
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              MicroRNA signature predicts survival and relapse in lung cancer.

              We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.
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                Author and article information

                Contributors
                kimqtae@snu.ac.kr
                nsproper@naver.com
                haeock.lee@samsung.com
                sang.cheol.kim@samsung.com
                yunjee.seo@gmail.com
                cws1021@skku.edu
                smflsdkdl@snu.ac.kr
                nsnam@skku.edu
                junhyong@sas.upenn.edu
                kmjoo@skku.edu
                woongyang.park@samsung.com
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                19 June 2015
                19 June 2015
                2015
                : 16
                : 1
                Affiliations
                [ ]Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
                [ ]Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, South Korea
                [ ]Department of Urology, Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
                [ ]Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
                [ ]Department of Anatomy and Cell Biology, Sungkyunkwan University School of Medicine, Seoul, South Korea
                [ ]Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Seoul, South Korea
                [ ]Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
                [ ]Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, South Korea
                [ ]Department of Biology, University of Pennsylvania, Philadelphia, PA 19104 USA
                [ ]Penn Program in Single Cell Biology, University of Pennsylvania, Philadelphia, PA 19104 USA
                Article
                692
                10.1186/s13059-015-0692-3
                4506401
                26084335
                © Kim et al. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Research
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                © The Author(s) 2015

                Genetics

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