Data-driven cancer genome interpretation for personalised cancer treatment

CGI诊所旨在通过优化基因组数据解释(在测序之后和在建议兼容的靶向治疗之前)来改进肿瘤学中的个性化医学。解释是下一代测序(NGS)在癌症治疗中全面部署和广泛使用的瓶颈。该项目解决了解释癌症突变的三个主要障碍：它不是系统性的；它涉及大多数未知意义的变异；它未能赋予患者权力。目前，对肿瘤基因组数据的解释依赖于专家审查分散的数据库和资源的工作，这一耗时的过程可能导致次优的临床决策。CGI诊所将通过在一站式工具中整合相关的公共和私人数据库，使解释过程系统化，并有可能组织由参考医院共同协助的虚拟分子肿瘤委员会。该项目将分为三个阶段：设置(评估需求)、验证(9个临床合作伙伴的试点)和复制(在欧盟30家医院)。它将实现基因组数据解释的民主化(独立于医院的规模、资源和概况分析技术)，并提供卫生经济学验证。依靠一个系统的自动学习平台，CGI诊所将增加肿瘤中可解释变体的份额(从目前的9-12%增加到至少50%)，以及构成药物反应生物标记物的特征。对于大多数癌症患者来说，解释过程很复杂，使他们与疾病的知识疏远了。CGI诊所将建立EduCGI，这是一个基于网络的工具，帮助患者了解通过解释他们的肿瘤获得的信息，促进与临床医生的知情讨论，并分享他们的数据用于研究。归根结底，该项目是为了向政策制定者提供癌症管理方面的信息，并赋予患者权力。

CGI-Clinics aims at improving personalized medicine in oncology by optimizing genomic data interpretation (after sequencing and before advising on compatible targeted therapies). Interpretation is a bottleneck for the full deployment and broad accessibility of Next Generation Sequencing (NGS) in cancer management. The project tackles the 3 main hurdles in the interpretation of cancer mutations: it is not systematic; it deals with a majority of variants of unknown significance, and it fails to empower patients. Currently the interpretation of tumour genomic data relies on the work of experts reviewing scattered databases and resources, in a time-consuming process that may lead to sub-optimal clinical decisions. CGI-Clinics will systematize the interpretation process by integrating relevant public and private databases in a one-stop shop tool, with the possibility to organize virtual molecular tumour boards co-facilitated by reference hospitals. The project will have three phases: setup (to assess needs), validation (a pilot with the 9 clinical partners) and replication (in 30 hospitals across the EU). It will enable democratization of genomic data interpretation (independent of a hospital’s size, resources, and profiling technology) and provide health economics validation. Relying on a systematic automatic learning platform, CGI-Clinics will increase the share of interpretable variants in tumours (from the current 9-12% to at least 50%), and features that constitute biomarkers of drug response. The interpretation process is complex for most cancer patients, alienating them from knowledge of their illness. CGI-Clinics will build EduCGI, a web-based tool to help patients understand the information gained through interpretation of their tumours, facilitating informed discussions with clinicians, and sharing their data for research. Ultimately, the project is built to inform policymakers on cancer management and to empower patients.