Gene Expression Signatures to Predict Treatment Response in Systemic Sclerosis

描述（由申请人提供）：今天，系统性硬化症（SSc）临床试验通常包括所有亚群；有些人可能受益，有些人则没有，这混淆了疗效的衡量标准。因为每个表达亚群都有不同的潜在的不受调控的分子途径，所以没有一种药物有望使所有患者受益，也就是说，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的循环性质相关的困难。例如，对甲磺酸伊马替尼（格列卫），一种酪氨酸激酶抑制剂和霉酚酸酯（Cellcept），一种淋巴细胞增殖衰减剂的反应，可以通过基因表达定量测量。最后，深入了解定义每种亚型的分子途径将使我们能够识别和潜在地设计新药。除了药物开发之外，分型还可以根据每个患者的亚型制定个性化的治疗计划，从而帮助患者和他们的医生。总之，这些好处既令人兴奋又令人信服，并且从根本上改变了被诊断为SSc的意义。这项工作将提供对疾病发病机制的见解，这可能会影响其他团体或制药公司开发新的治疗方法。这项研究的直接结果是基因表达生物标志物在预测SSc治疗反应的新平台上的验证和前瞻性临床试验。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其健康状况。

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.