Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, India.
Bioinformatics. 2019 May 15;35(10):1701-1711. doi: 10.1093/bioinformatics/bty868.
Traditional cancer therapy is focused on eradicating fast proliferating population of tumor cells. However, existing evidences suggest survival of sub-population of cancer cells that can resist chemotherapy by entering a 'persister' state of minimal growth. These cells eventually survive to produce cells resistant to drugs. The identifying of appropriate targets that can eliminate the drug-tolerant 'persisters' remains a challenge. Hence, a deeper understanding of the distinctive genetic signatures that lead to resistance is of utmost importance to design an appropriate therapy.
In this study, deep-sequencing of mRNA was performed in osteosarcoma (OS) cells, exposed to the widely used drug, cisplatin which is an integral part of current treatment regime for OS. Transcriptomic analysis was performed in (i) untreated OS; (ii) persister sub-population of cells post-drug shock; (iii) cells which evade growth bottleneck and (iv) drug-resistant cells obtained after several rounds of drug shock and revival. The transcriptomic signatures and pathways regulated in each group were compared; the transcriptomic pipeline to the acquisition of resistance was analyzed and the core network of genes altered during the process was delineated. Additionally, our transcriptomic data were compared with OS patient data obtained from Gene Ontology Omnibus. We observed a sub-set of genes to be commonly expressed in both data sets with a high correlation (0.81) in expression pattern. To the best of our knowledge, this study is uniquely designed to understand the series of genetic changes leading to the emergence of drug-resistant cells, and implications from this study have a potential therapeutic impact.
All raw data can be accessed from GEO database (https://www.ncbi.nlm.nih.gov/geo/) under the GEO accession number GSE86053.
Supplementary data are available at Bioinformatics online.
传统的癌症疗法侧重于根除肿瘤细胞快速增殖的群体。然而，现有证据表明，通过进入最小生长的“持久”状态，癌细胞的亚群可以抵抗化疗而存活下来。这些细胞最终会存活下来，产生对药物有抗性的细胞。确定能够消除具有耐药性的“持久”细胞的合适靶点仍然是一个挑战。因此，深入了解导致耐药性的独特遗传特征对于设计适当的治疗方法至关重要。
在这项研究中，对骨肉瘤（OS）细胞进行了 mRNA 的深度测序，这些细胞暴露于广泛使用的药物顺铂中，顺铂是 OS 当前治疗方案的重要组成部分。对未处理的 OS 细胞（ii）药物冲击后持久亚群细胞（iii）逃避生长瓶颈的细胞和（iv）经过几轮药物冲击和复苏获得的耐药细胞进行了转录组分析。比较了每个组中调节的转录组特征和途径；分析了获得耐药性的转录组途径，并描绘了在此过程中改变的核心基因网络。此外，我们的转录组数据与从基因本体论 (GO) 公共数据库获得的 OS 患者数据进行了比较。我们观察到一组基因在两个数据集之间共同表达，表达模式的相关性很高（0.81）。据我们所知，这项研究是专门为了了解导致耐药细胞出现的一系列遗传变化，并对这项研究的结果具有潜在的治疗影响。
所有原始数据都可以从 GEO 数据库（https://www.ncbi.nlm.nih.gov/geo/）中访问，GEO 注册号为 GSE86053。
补充数据可在 Bioinformatics 在线获取。
