Systemic sclerosis (SSc) is an autoimmune disease that presents with a heterogeneous and complex phenotype. Major manifestations of the disease are skin fibrosis, vascular dysfunction, and immune system activation. There are no validated diagnostic markers. There are no curative treatments. One in three patients dies within 10 years of diagnosis. Some drugs (developed for other indications) are currently in clinical trials however, outcome measures such as the modified Rodnan skin score are inadequate, and heterogeneity in patient populations hinders trial effectiveness since only a subset of patients is likely to respond to any given drug. Celdara Medical is segmenting this heterogeneity or """"""""subtyping"""""""" SSc by associating gene signatures with clinical phenotypes to provide a quantitative measure for patient prognosis, assessment of efficacy during clinical trials, and insight into disease mechanisms which will lead to drugs designed for each SSc subtype. Because SSc is a rare disease, skin biopsies are in high demand and longitudinal accrual of fresh biopsy samples for DNA microarray analysis is slow, which severely hampers clinical validation of the test. A retrospective analysis of archived formalin fixed paraffin embedded (FFPE) tissue blocks from a single clinical site is one solution to this problem, however, the degree of RNA degradation in these samples (vis- `-vis fresh frozen (FF) samples) makes them unsuitable for DNA microarray analysis. We therefore propose to develop a robust SSc subsetting method using measurement of gene expression by short read ultra high-throughput (UHTP) sequencing from FFPE and FF skin biopsies to predict SSc subtype. In Phase I we will accomplish the following Aims:
Aim 1. Compare the utility of UHTS to NanoString technology for the measurement of gene expression profiles from FFPE and FF samples.
Aim 2. Increase the quality and sample size of clinical longitudinal data using either UHTS or Nanostring and analyze the clinical covariates associated with the intrinsic subsets. Successful completion of the work described herein will: 1. allow for clinical validation, 2. simplify clinical implementation, 3. reduce the cost of patient subtyping, and 4. dramatically and quickly expand the gene expression sample database through inclusion of archived samples, potentially leading to novel genomics- based insights into this terrible and complex disease.
Scleroderma is a poorly-understood disease which affects 300,000 Americans, and kills 1/3 of those afflicted within 10 years of diagnosis. Genetic analyses of patient samples have revealed subtypes of the disease, each with different prognoses, and each suggesting different potential therapeutics. The goal of this project is to simplify the currently complex sample preparation protocol while improving the robustness and decreasing the cost of the genetic analyses.
