Novel lung imaging biomarkers and skin gene expression subsetting in dasatinib treatment of systemic sclerosis-associated interstitial lung disease

Abstract

Background: There are no effective treatments or validated clinical response markers in systemic sclerosis (SSc). We assessed imaging biomarkers and performed gene expression profiling in a single-arm open-label clinical trial of tyrosine kinase inhibitor dasatinib in patients with SSc-associated interstitial lung disease (SSc-ILD).

Methods: Primary objectives were safety and pharmacokinetics. Secondary outcomes included clinical assessments, quantitative high-resolution computed tomography (HRCT) of the chest, serum biomarker assays and skin biopsy-based gene expression subset assignments. Clinical response was defined as decrease of >5 or >20% from baseline in the modified Rodnan Skin Score (MRSS). Pulmonary function was assessed at baseline and day 169.

Results: Dasatinib was well-tolerated in 31 patients receiving drug for a median of nine months. No significant changes in clinical assessments or serum biomarkers were seen at six months. By quantitative HRCT, 65% of patients showed no progression of lung fibrosis, and 39% showed no progression of total ILD. Among 12 subjects with available baseline and post-treatment skin biopsies, three were improvers and nine were non-improvers. Improvers mapped to the fibroproliferative or normal-like subsets, while seven out of nine non-improvers were in the inflammatory subset (p = 0.0455). Improvers showed stability in forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), while both measures showed a decline in non-improvers (p = 0.1289 and p = 0.0195, respectively). Inflammatory gene expression subset was associated with higher baseline HRCT score (p = 0.0556). Non-improvers showed significant increase in lung fibrosis (p = 0.0313).

Conclusions: In patients with SSc-ILD dasatinib treatment was associated with acceptable safety profile but no significant clinical efficacy. Patients in the inflammatory gene expression subset showed increase in skin fibrosis, decreasing pulmonary function and worsening lung fibrosis during the study. These findings suggest that target tissue-specific gene expression analyses can help match patients and therapeutic interventions in heterogeneous diseases such as SSc, and quantitative HRCT is useful for assessing clinical outcomes.

Trial registration: Clinicaltrials.gov NCT00764309.

Fig 1. CONSORT flowchart for the study.

Fig 2. Drop plots of changes in texture-based quantitative scores from baseline using HRCT.

(A) QLF and (B) QILD for the most severe lobe. (C) QLF and (D) QILD for the whole lung. Dashed lines of ±3% and ±2% are indicators of thresholds for changes in regional analysis in lobe and whole lung, respectively.

Fig 3. Representative pairs of baseline and follow-up HRCT images.

(A) Better case: pulmonary fibrosis (PF) improved and ground glass opacity (GGO) worsened by the visual assessment in whole lung; (B) annotated HRCT of (A) with a CAD system: QLF and QILD in whole lung decreased, by 2.9% (3.9% to 1.0%) and 0.2% (19.9% to 19.7%), respectively. (C) Stable case; (D) annotated CAD of (C): QLF in the worst lobe (lower left) increased by 2% (64% to 66%). In whole lung, QLF and QILD increased, by 2.3% (39.5% to 41.8%) and 1.8% (72.4% to 74.2%), respectively. (E) Worse case: PF worsened and GGO improved; (F) annotated CAD of (E): QLF increased by 6.0% (28% to 34%) in the most severe lobe (lower right) and 1.8% (9.8% to 11.6%) in whole lung. QILD in whole lung was stable (41.3% to 40.5%).

Fig 4. Intrinsic gene expression subset assignment and response status.

(A) Intrinsic gene analysis. 3,207 probes (2,532 unique genes) at FDR<1.1% were used to organize samples into three gene expression-based clusters (intrinsic subsets). Sample color legend: red–fibroproliferative, purple–inflammatory, green–normal-like. (B) Distribution of patients according to their baseline intrinsic subsets and their response status. (C) Comparison between inflammatory and non-inflammatory (fibroproliferative and normal-like) patients in terms of the percentage of improvers.

Fig 5. Baseline and improver gene expression and pathway enrichment analysis.

(A) 1,062 genes were differentially expressed (p<0.05) between improvers and non-improvers at baseline. Sample color legend: blue–improvers, orange–non-improvers. (B) 14 pathways showing differential expression (FDR<10%) at baseline. (C) 454 genes displaying differential expression (p<0.05) between baseline and post-treatment in improvers. Sample color legend: blue–baseline samples, black–post-treatment samples. (D) 13 pathways were differentially expressed (FDR<10%) in improvers.

Fig 6. HRCT trends across improvers and non-improvers.

(A) QGG trends. (B) QLF trends. (C) Baseline [QGG-QLF] marker vs. intrinsic subset. (D) Baseline [QGG-QLF] marker vs. response status.