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

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

19yo woman with Systemic Sclerosis (SSc)/Scleroderma

Patient attempting to make fist

Patient attempting to straighten fingers

modified Rodnan skin score

Problems: confounding, time to change, inter- and intra-relater variability

Limited cutaneous/lcSSc Diffuse cutaneous/dcSSc

distal intermediate

Scleroderma: Classification and Clinical Subsets

Slide courtesy of Francesco Boin, MD

• 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

Skin biopsies

• Performed before treatment

• 4mm punch of skin removed

• RNA isolated from the biopsy

DNA:Your genes

RNA:The code to make proteins

DNA= the recipe

RNA= your order: Gene expression

Proteins= the meal

Gene Expression

Measuring RNA levels=measuring gene expression

NEJM 2006 354; 23

Excel spreadsheet of 100s of gene expression values

Question 1: Can we use skin gene expression in place of the modified Rodnan skin score to assess treatment response?

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

Study pipeline

Results

415 differentially expressed genes between healthy and SSc skin

Anybody can tell the difference between an SSc patient and a healthy person

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

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.

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

Distance to Health-Responder

Purple/green orb: healthyGreen: healthy control biopsiesBlue: SSc patients with MRSS<11Red: SSc patients with MRSS>11

PC1

PC2

Purple/green orb: healthyGreen: healthy control biopsiesBlue: SSc patients with MRSS<11Red: SSc patients with MRSS>11

PC1

PC2

DTH Non-Responder

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

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

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

Thank you

Cohort GSE ID

UCSF1 GSE9285

Boston GSE32413

Houston GSE58095

Stanford

UCSF2

HSS GSE65405