Kidd Brian A, Readhead Ben P, Eden Caroline, Parekh Samir, Dudley Joel T
Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics & Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Department of Medicine Hematology & Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Per Med. 2015 Jun 1;12(3):245-257. doi: 10.2217/pme.14.87.
The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology.
从患者身上收集数百万分子测量数据的能力如今已成为临床医学的现实。这一现实带来了一个挑战，即如何将这些海量数据整合到能够准确预测复杂病理生理学并将这种复杂性转化为临床可操作输出的模型中。整合信息学和数据驱动方法提供了一个分析大规模数据集并将其组合成多尺度模型的框架，这些模型可用于确定疾病的关键驱动因素并识别治疗肿瘤的最佳疗法。在这篇观点文章中，我们讨论了整合建模方法如何用于指导个体治疗决策，并重点介绍了一份近期的病例报告，该报告阐述了个性化肿瘤学面临的挑战与机遇。
