RNA-seq identifies autophagy as the most prevalent upregulated pathway in dormant breast cancer cells. RNA-seq identifies autophagy as the most prevalent upregulated pathway in dormant breast cancer cells

Purpose: The goal of this study is to evaluate the transcriptome profiling (RNA-seq) of dormant and proliferating breast cancer cells using an in vitro 3D model Methods: mRNA profiles of D2.0R cells growing either on basal membrane extracts (BME) (dormant phase) or BME + Collagen (COL) (proliferative phase) at days 1 or 5 of culture were generated by deep sequencing in triplicate with Ilumina HiSeq2500 using Illumina TruSeq V4. Aligned reads (BAM files) were analysed using PartekFlow software for differential expression and gene enrichment analysis. Comparisons used Partek Gene Specific Analysis (GSA) algorithm and multiple comparisons were corrected using False Discovery Rate (FDR), which was set at 0.05 Results: Using an optimized data analysis workflow, we mapped about 118 – 133 million reads per sample to the mouse genome (build mm9). Total alignment with reference genome is between 81-90%. RNA-seq identified 5,524 transcripts showing differential expression between the D2.0R cells cultured on BME + COL vs D2.0R cells cultured on BME matrices at day 5, with a fold change ≥1.5 or ≤-1.5 and p value <0.05. On the other hand, only 1,097 were found to be differentially expressed between D2.0R cells growing on BME matrices at day 5 and day 1, with a fold change ≥1.5 or ≤-1.5 and p value <0.05. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to breast cancer dormancy and identifies autophagy as a top biological process activated in dormant D2.0R cells. Conclusions: Our study represents a detailed analysis of the transcriptomes of dormant and proliferating D2.0R cells, with three biologic replicates, generated by RNA-seq technology. RNA-seq based transcriptome characterization identifies autophagy as the most prevalent upregulated pathway in dormant breast cancer cells. Overall design: mRNA profiles of D2.0R cells after culture on BME or BME + COL matrices for 1 or 5 days were generated by deep sequencing in triplicate with Ilumina HiSeq2500 using Illumina TruSeq V4.

研究目的：本研究旨在借助体外三维（3D）模型，对休眠与增殖状态乳腺癌细胞的转录组谱（transcriptome profiling）开展RNA测序（RNA-seq）分析与评估。
方法：本研究对培养第1天或第5天的D2.0R细胞进行分组处理：分别在基底膜提取物（basal membrane extracts, BME）上培养（对应休眠状态），或在BME+胶原蛋白（collagen, COL）上培养（对应增殖状态）；随后采用Illumina TruSeq V4建库试剂盒，依托Illumina HiSeq2500测序平台进行三次生物学重复的深度测序，获取各组细胞的mRNA表达谱。将比对后的测序reads（BAM文件）导入PartekFlow软件，进行差异表达分析与基因富集分析（gene enrichment analysis）。差异比较采用Partek基因特异性分析（Gene Specific Analysis, GSA）算法，多重比较校正采用错误发现率（False Discovery Rate, FDR），校正阈值设为0.05。
结果：通过优化的数据分析流程，我们将每个样本约1.18~1.33亿条测序reads比对至小鼠基因组（版本mm9），基因组总比对率介于81%~90%之间。RNA-seq鉴定出5524个差异表达转录本，对应培养第5天时，在BME+COL基质上培养的D2.0R细胞与仅在BME基质上培养的D2.0R细胞之间的表达差异，其折叠变化（fold change）≥1.5或≤-1.5，且p值<0.05。另一方面，在BME基质上培养的D2.0R细胞中，第5天与第1天相比仅鉴定出1097个差异表达转录本，同样满足折叠变化≥1.5或≤-1.5且p值<0.05的筛选条件。对差异表达基因进行层级聚类分析（hierarchical clustering）后，发现多个尚未被功能注释的基因可能参与乳腺癌休眠过程，并确定自噬（autophagy）为休眠D2.0R细胞中激活的首要生物学过程。
结论：本研究借助RNA测序（RNA-seq）技术，结合三次生物学重复，对休眠与增殖状态D2.0R细胞的转录组开展了详细分析。基于RNA-seq的转录组表征鉴定出，自噬是休眠乳腺癌细胞中最显著上调的生物学通路。
整体实验设计：采用Illumina TruSeq V4建库试剂盒，依托Illumina HiSeq2500测序平台进行三次生物学重复的深度测序，获取在BME或BME+COL基质上分别培养1天或5天的D2.0R细胞的mRNA表达谱。