Translational Genomics Research Institute, Phoenix, AZ, USA.
Oncogene. 2008 Dec;27 Suppl 2:S58-66. doi: 10.1038/onc.2009.354.
Genomic profiling is beginning to extend beyond the many applications in discovery research toward direct medical applications that hold the promise of more precise and individualized health-care delivery. There are many barriers and challenges that still need to be overcome before 'Precision Medical Genomics' can deliver the promise of more informed patient care, not the least of which is the unmet need for a new conceptual framework for recovering, understanding and translating potentially useful information from a single genome. Although a wide spectrum of scientific strategies, bioinformatic approaches, IT tools and knowledge resources have been developed to support discovery research, the interpretive requirements for recovering clinically useful insights from an individual's genome are different in many ways from those of traditional research goals. In this study, we compare and contrast the fundamental conceptual differences that distinguish 'research' to discover generalized knowledge from 'search' to recover individualized knowledge. We also consider the merits of applying evidence-based medicine and traditional scientific methods when n=1, and consider an alternative perspective based on a translational engineering approach and intelligence for interpreting genomic information from an individual case. Although the general idea of biological intelligence-based knowledge recovery that we introduce here can be broadly applied for personal genomics across many indications in medicine, we make a case that the need for adopting such a paradigm is greatest for supporting the management of complex diseases, and particularly suited for supporting therapeutic decisions in medical oncology. Early concepts for designing and implementing this kind of 'BioIntelligence' solution will be discussed. We also review the anticipated challenges of implementing genomic analysis and biological intelligence-based solutions in the practice of medical oncology by discussing some of the related pragmatic considerations for deploying the first generation of a 'Precision Medical Genomics' solution that can evolve and improve over time.
基因组分析开始超越了在发现研究中的众多应用，朝着直接应用于医疗的方向发展，有望实现更精准和个体化的医疗服务。在“精准医疗基因组学”能够实现更具信息性的患者护理之前，还有许多障碍和挑战需要克服，其中最主要的是缺乏一个新的概念框架来从单个基因组中恢复、理解和转化潜在有用的信息。尽管已经开发了广泛的科学策略、生物信息学方法、信息技术工具和知识资源来支持发现研究，但从个体基因组中恢复临床有用见解的解释要求在许多方面与传统研究目标不同。在本研究中，我们比较和对比了区分“研究”以发现一般性知识与“搜索”以恢复个体化知识的基本概念差异。我们还考虑了当 n=1 时应用循证医学和传统科学方法的优点，并考虑了一种基于转化工程方法和智能的替代观点，用于从个体病例解释基因组信息。虽然我们在这里介绍的基于生物智能的知识恢复的一般思想可以广泛应用于医学中的许多适应症的个人基因组学，但我们认为采用这种范式的必要性最大，因为它支持复杂疾病的管理，特别适合支持医学肿瘤学中的治疗决策。我们将讨论设计和实现这种“生物智能”解决方案的早期概念。我们还通过讨论部署第一代“精准医疗基因组学”解决方案的一些相关实际考虑因素，来回顾在医学肿瘤学实践中实施基因组分析和基于生物智能的解决方案所面临的预期挑战，该解决方案可以随着时间的推移而发展和改进。
