Servant Nicolas, Roméjon Julien, Gestraud Pierre, La Rosa Philippe, Lucotte Georges, Lair Séverine, Bernard Virginie, Zeitouni Bruno, Coffin Fanny, Jules-Clément Gérôme, Yvon Florent, Lermine Alban, Poullet Patrick, Liva Stéphane, Pook Stuart, Popova Tatiana, Barette Camille, Prud'homme François, Dick Jean-Gabriel, Kamal Maud, Le Tourneau Christophe, Barillot Emmanuel, Hupé Philippe
Institut Curie, Paris France ; INSERM U900, Paris France ; Mines ParisTech, Fontainebleau France.
INSERM U932, Paris France.
Front Genet. 2014 May 30;5:152. doi: 10.3389/fgene.2014.00152. eCollection 2014.
Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.
精准医学（PM）要求根据每位患者及其肿瘤的基因特征提供个性化的医疗服务。过去十年见证了诸如微阵列和下一代测序等高通量技术的发展，这些技术为肿瘤学领域的精准医学铺平了道路。随着这些技术成本的降低，我们面临着所产生数据量的指数级增长。我们在日常实践中使用这些信息的能力在很大程度上依赖于高效生物信息学系统的可用性，该系统有助于将实验室知识转化为分子靶向和诊断。临床试验和常规诊断构成了不同的方法，两者都需要强大的生物信息学环境，该环境能够（i）保证数据的整合和可追溯性，（ii）确保基因组数据的正确处理和分析，以及（iii）应用定义明确且可重复的程序进行工作流程管理和决策。为了解决这些问题，居里研究所开发了一个无缝信息系统，该系统有助于数据整合并实时跟踪单个样本的处理过程。此外，还开发了计算流程，以从每位患者的分子谱中可靠地识别基因组改变和突变。经过严格的质量控制后，会向临床医生和生物学家提供一份有意义的报告，以供治疗决策参考。完整的生物信息学环境及其实施要点将在SHIVA临床试验的背景下呈现，该试验是一项多中心随机II期试验，比较了难治性癌症患者基于肿瘤分子谱的靶向治疗与传统治疗。作为精准医学应用的一个例证，讨论了在该试验的设立和进行过程中实际面临的众多挑战。
