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.
The goal of this proposal is to validate a next generation diagnostic tool, the ScleroType test, that uses nanoString based gene expression analyses of skin to subtype scleroderma patients, and to further assess its ability to predict disease course and clinical improvement during treatment. Identification of gene expression signatures that identify patients most likely to benefit from specific therapies would provide a vast improvement to patient management by reducing exposure to side effects, reducing costs and importantly, enabling identification and selection of effective therapies for appropriate patients.