Shukla Hem D
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD 21201, USA.
Department of Biology, Notre Dame of Maryland University, Baltimore, MD 21210, USA.
Proteomes. 2017 Oct 25;5(4):28. doi: 10.3390/proteomes5040028.
During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein-protein interaction, and pharmacogenomics data, are indispensable to glean into the cancer genome and proteome and these approaches have generated multidimensional universal studies of genes and proteins (OMICS) data which has the potential to facilitate precision medicine. However, due to slow progress in computational technologies, the translation of big omics data into their clinical aspects have been slow. In this review, attempts have been made to describe the role of high-throughput genomic and proteomic technologies in identifying a panel of biomarkers which could be used for the early diagnosis and prognosis of cancer.
在过去的一个世纪里，我们对癌症诊断和治疗的理解基于单基因方法，因此我们对癌症临床遗传基础的认识并不完整。自2003年人类基因组完成以来，它引领我们进入治疗靶点发现阶段，使我们能够使用前沿的蛋白质基因组学工具挖掘基因组。基因组计划已产生了一些用于诊断、治疗和预后标志物的新颖且有前景的癌症靶点，这些靶点正被用于监测癌症治疗的反应。癌症的异质性阻碍了我们对导致细胞异常生长的潜在机制的理解。自癌症基因组图谱（TCGA）和国际基因组联盟项目启动以来，基因组测序取得了巨大进展，大量癌症基因组已完成测序，这种方法改变了我们对不同类型癌症诊断和治疗的理解。通过应用基因组学和蛋白质组学技术，正在生成大量关于临床肿瘤的基因组数据，这改变了癌症格局，并有可能改变癌症诊断和预后。全面了解癌症格局的分子视图对于理解癌症发生的潜在机制以改善诊断和预后是必要的，这最终将导致个性化治疗。有趣的是，癌症蛋白质组分析还使我们能够识别生物标志物，以监测接受癌症治疗患者的药物和辐射抗性。此外，由TCGA资助的研究已实现了对特定癌症的基因组和转录组特征分析，这种分析有助于开发针对高度致命恶性肿瘤的靶向疗法。高通量技术，如完整蛋白质组、表观基因组、蛋白质 - 蛋白质相互作用和药物基因组学数据，对于深入了解癌症基因组和蛋白质组不可或缺，这些方法已产生了对基因和蛋白质的多维度通用研究（组学）数据，有可能推动精准医学发展。然而，由于计算技术进展缓慢，大量组学数据向临床应用的转化一直很缓慢。在本综述中，我们试图描述高通量基因组学和蛋白质组学技术在识别一组可用于癌症早期诊断和预后的生物标志物方面的作用。
