A four-gene biomarker predicts skin disease in patients with diffuse cutaneous systemic sclerosis

Abstract

Objective: Improved outcome measures in systemic sclerosis (SSc) are critical to finding active therapeutics for this disease. The modified Rodnan skin thickness score (MRSS) is the current standard for evaluating skin disease in SSc, but it is not commonly used in the clinical setting, in part because it requires specialized training to perform accurately and consistently. The purpose of this study was to investigate whether skin gene expression might serve as a more objective surrogate outcome measure to supplement skin score evaluations.

Methods: Skin RNAs from a group of patients with diffuse cutaneous SSc were studied for expression levels of genes known to be regulated by transforming growth factor beta (TGFbeta) and interferon (IFN). These levels were correlated with the MRSS, using multiple regression analyses to obtain best-fit models.

Results: Skin expression of the TGFbeta-regulated genes cartilage oligomeric matrix protein (COMP) and thrombospondin 1 (TSP-1) correlated moderately well with the MRSS, but the addition of other TGFbeta-regulated genes failed to significantly improve best-fit models. IFN-regulated genes were also found to correlate with the MRSS, and the addition of interferon-inducible 44 (IFI44) and sialoadhesin (Siglec-1) to COMP and TSP-1 in multiple regression analyses significantly improved best-fit models, achieving an R(2) value of 0.89. These results were validated using an independent group of skin biopsy samples. Longitudinal scores using this 4-gene biomarker indicated that it detects change over time that corresponds to changes in the MRSS.

Conclusion: We describe a 4-gene predictor of the MRSS and validate its performance. This objective measure of skin disease could provide a strong surrogate outcome measure for patient care and for clinical trials.

Figure 1. Expression of TGFβ-regulated genes in skin from patients with dcSSc

mRNA expression of TGFβ-regulated genes, CTGF, THS1, COL4 and PAI1, in lesional (33 biopsies) and non-lesional (15 biopsies) skin from dcSSc patients and healthy controls (5 biopsies). Fold-change shown on the graph is normalized to mRNA expression by one of the healthy controls. The average fold-change compared to this control reference sample of CTGF, THS1, PAI1 and COL4 in lesional dcSSc skin (7.52, 4.08, 9.23 and 4.80, respectively) compared to the average fold-change in control, healthy skin compared to the control reference sample (2.31, 1.30, 2.37 and 2.43, respectively) was increased for CTGF (3.25-fold increase, p=0.014), THS1 (3.14-fold increase, p=0.012), COL4 (3.89-fold increase, p=0.028) and PAI1 (1.97-fold increase, p=0.016). Only PAI1 showed statistically significant increased expression in non-lesional skin compared to healthy control skin (5.08-fold increase, p<0.001).

Figure 2. Linear regression in skin from patients with dcSSc

mRNA expression in lesional (CTGF, THS1 and COL4) or non-lesional (PAI1) skin related to the mRSS in subjects with dcSSc (see also Table II).

Figure 3. Expression of IFN-regulated genes in skin from patients with dcSSc

Linear regression of mRNA expression of IFN-regulated genes from lesional skin of patients with dcSSc with the mRSS: IFI44 (R²=0.41, p=0.004), SIG1 (R²=0.17, p=0.068), OAS2 (R²=0.33 p=0.013) and MX1 (R²=0.30, p=0.018).

Figure 4. Multiple linear regression of four-gene biomarker with the MRSS in patients with dcSSc

Panel A: Best-fit model of skin gene expression of COMP, THS1, IFI44 and SIG1 with the MRSS. Panel B: The contribution of expression of each gene, COMP, THS1, IFI44 and SIG1, to the biomarker predicted skin score. The contribution of each gene to the overall skin score was calculated by multiplying the PCR fold-change value for each gene by the constant associated with that value in the best-fit regression equation. For the 4-gene biomarker this equation takes the form: mRSS = 1.49 + 0.200(COMP) + 1.19(THS1) + 0.267(SIG1)+ 1.59(IFI44). Each bar represents the biomarker predicted skin score of one patient.

Figure 5. Validation of the four-gene biomarker for the skin score

Panel A: 4-gene biomarker prediction of the skin score using skin biopsies (n=12), independent from those used to develop the biomarker. The biomarker skin score was calculated from normalized mRNA expression of COMP, THS1, IFI44 and SIG1, using the best-fit equation determined by multiple linear regression shown in Fig. 3. Panel B: Comparison of changes in the MRSS (red lines) with changes in the four-gene biomarker (BSS, blue lines) over time in 5 patients with dcSSc. Patients A and B, and C, D and E show biomarker and MRSS, respectively, at baseline, 6 and 12 months, or at baseline and 6 months.