Gene Expression Signatures to Predict Treatment Response in Systemic Sclerosis

描述(申请人提供)：今天，系统性硬化症(SSC)临床试验通常包括所有子集；一些可能受益，另一些不会，混淆了疗效的衡量标准。由于每个表达亚群都有不同的潜在去调控的分子途径，没有一种药物有望使所有患者受益，即需要合理的患者选择来促进药物开发。此外，临床结果和终点的定量测量将使试验有效性的科学测量成为可能，并避免与SSC的周期性相关的困难。例如，对酪氨酸激酶抑制剂伊马替尼(Gleevec(R))和淋巴细胞增殖衰减剂霉酚酸酯(Cellcept)的反应可以通过基因表达来定量衡量。最后，对定义每个亚型的分子途径的洞察将使我们能够识别并潜在地设计新药。除了药物开发，分型还将通过允许根据每个患者的亚型提供个性化的治疗计划来帮助个别患者和他们的医生。总而言之，这些好处既令人兴奋又令人信服，并正在从根本上改变被诊断为SSC的意义。这项工作将为了解这种疾病的发病机制提供见解，这些疾病可能会影响其他组织或制药公司的新疗法的开发。这项研究的直接结果是在预测SSC治疗反应的新平台上验证和前瞻性临床测试基因表达生物标记物。将这项技术发展成为临床诊断工具和服务将显著改善SSc患者的管理，并最终改善患者的健康。

DESCRIPTION (provided by applicant): Today, systemic sclerosis (SSc) clinical trials generally include all subsets; some may benefit, others do not, confounding measures of efficacy. Because each expression subset has a different underlying deregulated molecular pathway, no single drug is expected to benefit all patients i.e. rational patient selection is required to facilitate drug development. Further, a quantitative measure of clinical outcome and endpoints will enable a scientific measure of trial effectiveness and avoid the difficulties associated with the cyclic nature of SSc. For example, response to imatinib mesylate (Gleevec(R)), a tyrosine kinase inhibitor and to mycophenolate mofetil (Cellcept), an attenuator of lymphocyte proliferation, can be quantitatively measured by gene expression. Finally, insights into the molecular pathways defining each subtype will enable us to identify and potentially design new drugs. Beyond drug development, subtyping will help individual patients and their doctors by allowing an individualized treatment plan informed by each patient's subtype. Together, these benefits are both exciting and compelling, and are fundamentally changing what it means to be diagnosed with SSc. This work will provide insights into the pathogenesis of the disease that may influence the development of new treatments by other groups or pharmaceutical companies. The immediate result of this study is the validation and prospective clinical testing of gene expression biomarkers on a new platform for predicting treatment response in SSc. Development of this technology into a clinical diagnostic tool and service will significantly improve the management and ultimately the health of patients with SSc.