PO.CL01.11 · 临床研究
整合多组学用于从尿液深入探索前列腺癌的分子特征
Integrated multiomics for deep molecular exploration of prostate cancer from urine
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:组学时代极大地扩展了用于揭示人类健康所蕴含复杂性的方法库,能够从广泛的样本类型中快速表征基因组、表观基因组、转录组、蛋白质组和代谢组。泌尿生殖系统癌症,包括前列腺癌,是男性中最常见的癌症。当前的早期检测方法依赖于对前列腺特异性抗原(PSA)的血液筛查,然而其假阳性率较高,由此引发了对替代生物标志物的探寻。尿液是一种超无创的分析物,是泌尿生殖系统癌症(包括前列腺癌)检测的理想选择。直接来自尿液的游离RNA/DNA(cfRNA/DNA)以及代谢组学,是用于诊断、治疗监测和肿瘤组织起源预测的生物标志物鉴定的理想候选。
方法:在此,我们描述了对一系列前列腺癌受累样本和对照尿液样本进行的整合代谢组学和RNA-Seq分析。首先,采用一种具有高效尿液cfRNA回收率的专门方法从受累样本和对照样本中分离cfRNA。随后对cfRNA进行高灵敏度RNA-Seq,以评估一系列前列腺癌生物标志物。同样的尿液样本还通过多种互补的LC-MS检测进行代谢物谱分析,以提供最高质量的非靶向代谢组学数据。两种数据类型均用于整合多组学分析。
结果:尿液被证明是一种用于生物标志物筛查和检测的理想超无创方法。跨多种数据模态(包括转录组学和代谢组学)的整合分析能够全面呈现受高度影响的通路和过程,其统计学显著性高于任何单一模态。
结论:生物标志物检测和多组学分析对于在治疗药物研发和早期临床试验期间评估治疗前和治疗后的个体至关重要。尿液提供了一种真正无创的方法,当与组学工具结合时,能够为泌尿生殖系统患者队列提供全面的洞察。
查看英文原文 English abstract
Background: The omics era has greatly expanded the repertoire of approaches available to unravel the complexity underpinning human health, with the ability to rapidly characterize genomes, epigenomes, transcriptomes, proteomes and metabolomes from a wide range of sample types. Urogenital cancers, including prostate cancer, is the most prevalent cancer in men. Current early detection methods rely on blood screening of prostate-specific antigen (PSA), however, it has a high rate of false positives, resulting in the search for alternative biomarkers. Urine is an ultra-non-invasive analyte ideal for urogenital cancer detection, including prostate cancer. Cell free RNA/DNA (cfRNA/DNA), along with metabolomics directly from urine, is an ideal candidate for biomarker identification for use in diagnostics, treatment monitoring and tumor tissue of origin prediction.
Methods: Here we describe integrated metabolomics and RNA-Seq analysis from a series of prostate cancer affected and control urine samples. First, cfRNA from affected and control samples were isolated using a specialized method with efficient cfRNA recovery rate from urine. The cfRNA was then subjected to highly sensitive RNA-Seq to evaluate a series of prostate cancer biomarkers. The same urine samples were also subjected to metabolite profiling using multiple complementary LC-MS assays to deliver the highest quality untargeted metabolomics data. Both data types were used for integrated multiomics analysis.
Results: Urine proves to be an ideal ultra-non-invasive method for biomarker screening and detection. Integrated analysis across multiple data modalities, including transcriptomics and metabolomics, allows for holistic views of pathways and processes that are highly impacted, with increased statistical significance than any one modality alone.
Conclusion: Biomarker detection and multiomics analyses are critical to assess individuals in both pre- and post-treatment during therapeutic development and early-stage clinical trials. Urine offers a truly non-invasive approach that, when combined with omics tools, can provide comprehensive insight across urogenital patient cohorts.
利益披露 Disclosure
B. Mehta, None..
A. O’Hara, None..
E. Stancliffe, None..
T. Cohen, None..
D. Corney, None..
H. Latif, None.