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The utility of assessing gene expression in skin to evaluate treatment response:
What becomes of all those skin biopsies?
Monique Hinchcliff, MD MSAssociate Clinical Director and
Director of Translational ResearchNorthwestern Scleroderma Program
Patient Education DayOctober 15, 2016
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Overview
• How we currently measure skin disease• Why/how we perform skin biopsies• What have we learned
– Novel gene expression signature• Distinguish between SSc patients and healthy subjects• Correlates with skin score • Provides a metric to assess real-time clinical
improvement
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19yo woman with Systemic Sclerosis (SSc)/Scleroderma
Patient attempting to make fist
Patient attempting to straighten fingers
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modified Rodnan skin score
Problems: confounding, time to change, inter- and intra-relater variability
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Limited cutaneous/lcSSc Diffuse cutaneous/dcSSc
distal intermediate
Scleroderma: Classification and Clinical Subsets
Slide courtesy of Francesco Boin, MD
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• Pros– Quick– Inexpensive– Changes with time– Widely used– Requires minimal
training
• Cons– Imprecise – Variability– Influenced– Requires long interval
between measurements to assess change
modified Rodnan skin score
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Skin biopsies
• Performed before treatment
• 4mm punch of skin removed
• RNA isolated from the biopsy
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DNA:Your genes
RNA:The code to make proteins
DNA= the recipe
RNA= your order: Gene expression
Proteins= the meal
Gene Expression
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Measuring RNA levels=measuring gene expression
NEJM 2006 354; 23
Excel spreadsheet of 100s of gene expression values
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Question 1: Can we use skin gene expression in place of the modified Rodnan skin score to assess treatment response?
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Cohort GSE ID Sample number
SSc Patient number Platform
UCSF1 GSE9285 69 24 Agilent
Boston GSE32413 89 22 Agilent
Houston GSE58095 102 61 Illumina
Northwestern 183 67 Agilent
Stanford 17 13 Affymetrix
UCSF2 33 29 Illumina
HSS GSE65405 22 8 Agilent
Total 515 224 3 platforms
Many use gene expression
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Study pipeline
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Results
415 differentially expressed genes between healthy and SSc skin
Anybody can tell the difference between an SSc patient and a healthy person
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K23 Study Design
• SSc longitudinal skin biopsies
•baseline, 6 mo, 12 mo, and 24 mo, 36mo
•mRSS at each biopsy
Hinchcliff et al. Journal of Investigative Dermatology 2013
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Principal Component Analyses (PCA)
• Statistical procedure commonly used to find patterns in high-dimensional data
• Graphically highlight similarities and differences
• PC1 represents: genes with highest expression variance
• PC2 represents: genes with second highest expression variance……etc.
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Distance to Health-Overview
Purple/green orb: healthy GE centroidGreen: healthy control biopsiesBlue: SSc patients with MRSS<11Red: SSc patients with MRSS>11
PC1
PC2
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Distance to Health-Responder
Purple/green orb: healthyGreen: healthy control biopsiesBlue: SSc patients with MRSS<11Red: SSc patients with MRSS>11
PC1
PC2
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Purple/green orb: healthyGreen: healthy control biopsiesBlue: SSc patients with MRSS<11Red: SSc patients with MRSS>11
PC1
PC2
DTH Non-Responder
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Conclusions
• 415-gene expression signature distinguishes between SSc patients and healthy control skin
• Skin gene expression Distance to Health may be a useful approach to assess skin disease response
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Future Work
• Continue to validate the 415 gene expression signature in additional tissues
• Map the 415 gene signature to publicly available lists of FDA-approved drugs that target specific pathways
• Design clinical trials to test novel therapies in SSc patients
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Thank youNorthwestern University: K 23 Mentors
– Rowland W. Chang, MD MPH – John Varga, MD
Clinical coordinators– Kathleen Aren, MPH– Esperanza Arroyo, BA– Mary Carns, MS– Aileen Hoffman, MS
Stanford University: Bioinformatics
– Purvesh Khatri, PhD– Shane Lofgren, BS
Geisel School of Medicine at Dartmouth:– Michael Whitfield, PhD (mentor)
RNA technician– Tamara A Wood, MA
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Thank you
Cohort GSE ID
UCSF1 GSE9285
Boston GSE32413
Houston GSE58095
Stanford
UCSF2
HSS GSE65405