Comparison of Molecular Signatures from Multiple Skin Diseases Identifies Mechanisms of Immunopathogenesis

Abstract

The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease and common cellular and molecular pathways. Disease specific signatures were leveraged to build a multi-disease classifier which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of interferon (IFN) regulated gene programs with the skin database revealed a significant inverse correlation between IFN–β and IFN–γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. Additionally, these studies provide a framework for developing tools for personalized medicine towards the precise prediction, prevention, and treatment of disease on an individual level.

Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy

“…Lesional biopsy samples (n = 29) from patients with the following leprosy subtypes were obtained for determination of gene expression profiles and analysis of gene subtype profiles: L-lep (n = 6), T-lep (n = 10), RR (n = 7), and ENL (n = 6) ( Tables 1 and 2 and Supplemental Figure 1; supplemental material available online with this article; doi:10.1172/jci.insight.88843DS1). Although obtained simultaneously with the ENL profiles, the T-lep, L-lep, and RR gene expression profiles were previously published (15). All T-lep and L-lep lesions were obtained before the onset of chemotherapy.…”

“…In order to identify genes that were highly expressed in one subtype relative to all others, we calculated proportional median values for all filtered probe sets in every subtype (Supplemental Table 1). Briefly, the proportional median is a measure for comparing three or more conditions (15), and it is calculated for each probe set in each disease by dividing the median expression of that probe in that disease by the median expression of that same probe across the disease subtypes. Thus, ranking probes by their proportional median measures the relative expression in one subtype compared with all others.…”

“…Previously, investigation of the gene expression profiles from leprosy lesions has been limited to the comparison of specific subgroups or the grouping of all leprosy patients in comparison to 15 other infectious, inflammatory, and neoplastic skin diseases (15). Here, we investigated the specific genes and pathways associated with immune cell types that contribute to host defense and tissue injury at the site of infection in the different subtypes of leprosy.…”

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

“…To calculate single-sample gene set enrichment, we used the GSVA program (Hanzelmann et al, 2013) to derive the absolute enrichment scores of previously experimentally validated gene signatures as follow: i) the C2 CGP (chemical and genetic perturbation sets), ii) the C6 and C7 subset of the Molecular Signature Database (Subramanian et al, 2005), iii) selfcurated MAPK inhibitor-induced gene signatures using cell lines and patient-derived tumors , iv) post-operation wound signature (Inkeles et al, 2015), and v) melanoma invasive/proliferative signatures (Hoek et al, 2008). To derive the GSVA score of each signature in each tumor sample, we computed from raw RNASeq read counts by HTSEQ COUNT program and then normalized them to log 2 CPM values using EdgeR (McCarthy et al, 2012).…”

NOS2 and CCL27: clinical implications for psoriasis and eczema diagnosis and management

“…Based on these findings, a class prediction with 13 significantly differently expressed genes of keratinocyte terminal differentiation was suggested to predict the correct diagnosis for eczema and psoriasis patients on microarray level [4]. Recently, Pellegrini et al harnessed publicly available microarray data from 16 different skin conditions and more than 300 specimens to establish a multidisease classifier that not only diagnosed with 93% overall accuracy but also predicted the eventual diagnosis during disease progress in an undifferentiated patient [5].…”