Optimizing the Approach to Identify Cancer-Associated Myositis

项目总结 虽然特发性炎症性肌病(IIM)的发病机制在很大程度上尚不清楚，但已有数据出现 描述癌症与IIM发病之间的关系。在IIM患者的一部分中，存在着 在肌炎发作前后的癌症风险，称为癌症相关性肌炎(CAM)。几个 肌炎特异性自身抗体在IIM患者的临床表型中被证明是有用的，包括 与癌症有关。然而，哪些患者患癌症的风险最高，其程度 风险、癌症类型和最佳癌症检测策略都是未知的。我们的预赛 数据表明，这些肌炎特异性自身抗体在确定 关于癌症风险的分组，以及对患者可能患有的癌症类型的洞察 风险最高的。此外，我们证明，尽管广泛使用了多种癌症- 在美国，临床医生使用的筛查测试并不是所有的测试在IIM患者中都有同等的价值。这个 拟议的研究将利用世界上最大的IIM患者队列之一来定义和验证 与癌症风险增加相关的自身抗体，并评估它们在量化癌症风险方面的有效性 疾病发作。在目标1中，我们将确定相对于普通人群患癌症相关性肌炎的风险。 在我们的队列中，总体上和不同的自身抗体亚组中。我们将证明癌症的风险和类型 与IIM相关的疾病相对于一般人群会根据患者产生的自身抗体而有所不同。 AIM 2将提供关于IIM执行的当前癌症评估标准的有用性的数据 患者，并产生了一个更有选择性，危害更小的检测策略的论点。最后，在目标3中，我们将 得出一种预测性工具，用于为临床决策提供信息，以制定评估IIM的最佳策略 恶性疾病的患者。这项工作将允许开发一种具有临床相关性和循证的 IIM发病时癌症检测方法的探讨及自身抗体在IIM患者诊断中的作用 子集。

PROJECT SUMMARY Although the pathogenesis of idiopathic inflammatory myopathies (IIM) is largely unknown, data has emerged describing a relationship between cancer and IIM onset. In a subset of IIM patients, there exists an increased risk of cancer around the time of myositis onset, referred to as cancer-associated myositis (CAM). Several myositis-specific autoantibodies have proven useful in the clinical phenotyping of patients with IIM, including associating with cancer. However, which patients are at highest risk for developing cancer, the magnitude of the risk, the type of cancer, and the optimal cancer detection strategy are all unknown. Our preliminary data demonstrate that these myositis-specific autoantibodies can serve a useful role in the ability to define subgroups with regards to cancer risk as well as provide insight into the type of cancer a patient may be at highest risk for. Furthermore, we demonstrate that despite the widespread use of a large variety of cancer- screening tests employed by clinicians in the United States, not all tests have equal value in IIM patients. The proposed studies will utilize one of the largest cohorts of IIM patients in the world to define and validate autoantibodies associated with increased cancer risk and to assess their utility in quantifying the cancer risk at disease onset. In Aim 1 we will determine the risk of cancer-associated myositis relative to the general population in our cohort overall and in distinct autoantibody subgroups. We will demonstrate that the risk and type of cancer associated with IIM relative to the general population will vary based on the autoantibody the patient produces. Aim 2 will provide data on the usefulness of the current standard of cancer assessment that is performed in IIM patients, and generate an argument for a more selective, less harmful detection strategy. Lastly, in Aim 3 we will derive a predictive tool that will be used to inform clinical decision-making for optimal strategies to assess IIM patients for malignancy. This work will allow the development of a clinically relevant and evidence-based approach to cancer detection at IIM onset and establish the role for defining IIM patients by autoantibody subsets.