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Breast cancer: The translation of big genomic data to cancer precision medicine

机译:乳腺癌:将大基因组数据转化为癌症精密医学

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摘要

Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single‐gene to whole‐genome screening by using genome‐wide association study and next‐generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high‐throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative “−omics” data to refine the current medical practice to be applied in the near future.
机译:癌症是一种复杂的遗传疾病,它是由基因组变化的积累而发展而来的,其中种系变异使个体容易患上癌症,而体细胞变化则引发并触发了癌症的发展。在过去的20年中,基因组学研究取得了显着进展,通过使用全基因组关联研究和有助于大基因组数据的下一代测序技术,从单基因筛选发展到全基因组筛选。国际合作努力有助于整理这些数据,以识别可用于临床环境的具有临床意义的改变。本文以乳腺癌为重点,总结了通过高通量筛选进行基因组变异的鉴定,以及在将癌症患者与治疗方案相匹配的临床试验中使用基因组信息,从而进一步开发出癌症精密医学。此外,通过使用液体活检显着增强了癌症筛查和监测。随着数据复杂性和规模的不断增长,人们期望利用深度机器学习和人工智能来管理集成的“-组学”数据,以完善将在不久的将来应用的当前医学实践。

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