Data-Driven Methods for Advancing Precision Oncology.
Data-Driven Methods for Advancing Precision Oncology.
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DOI：
10.1007/s40495-018-0127-4
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发表时间：
2018-04
影响因子：
--
通讯作者：
Pillai AB
中科院分区：
文献类型：
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作者：
Nedungadi P;Iyer A;Gutjahr G;Bhaskar J;Pillai AB
本文讨论了数据驱动方法在推进精确肿瘤学用于生物医学研究、药物发现、临床研究和实践方面的进展、方法、挑战和未来方向。精确肿瘤学通过考虑个人的基因构成、临床、环境、社会和生活方式信息，提供个性化的癌症治疗。挑战包括由不同技术生成的海量、异质和不同的数据，这些技术具有多种模式，如Omics、电子健康记录、临床登记和存储库、医学成像、人口统计学、可穿戴设备和传感器。统计和机器学习方法一直在不断适应不断增长的数据大小和复杂性。精确肿瘤学支持分析缩短了生物标记物发现的周转时间，以及新药和重新调整用途药物的应用时间。精确肿瘤学还寻求根据对常规或实验性治疗敏感或耐药的基因组改变来确定目标患者群体。已经开发了用于癌症进展和生存、药物敏感性和耐药性的预测模型，以及针对个别患者情况确定最合适的联合治疗方案。未来，临床决策支持系统需要进行改造，以更好地纳入精确肿瘤学的知识，从而使临床从业者能够提供精确的癌症护理。开放的Omics数据集、机器学习算法和预测模型推动了精确肿瘤学的发展。需要集成电子健康记录和OMICS数据的临床决策支持系统来提供数据驱动的建议，以帮助临床医生进行疾病预防、早期识别和个性化治疗。此外，由于癌症是一种不断演变的疾病，临床决策系统将需要根据更新的知识和数据集不断更新。
This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. Precision oncology provides individually tailored cancer treatment by considering an individual’s genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.