I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment - PubMed (original) (raw)
I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment
Stelios Pavlidis et al. PLoS Comput Biol. 2019.
Abstract
Crohn's disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions. We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment. We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn's disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn's disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.
Conflict of interest statement
S.P., C.M., M.J.L, P.B., A.R., and F.B. are employees of Janssen R&D and shareholders of Johnson and Johnson. The remaining authors have declared that no competing interests exist.
Figures
Fig 1. Workflow chart used to identify patient level disease driving biology.
Disease related gene expression data representing the differential between either diseased to healthy or pretreated to post-treated samples for either CD or UC were assembled. Also assembled was a gene expression signature library representing various pathways or cell types. Gene set variation analysis (GSVA) was then applied to generate an enrichment score (ES) matrix in each disease which then underwent general linear model-based clustering to enable interpretation.
Fig 2. Differential signatures in Crohn’s disease and ulcerative colitis across various patient group comparisons.
Venn diagrams of upregulated signatures significantly enriched using a general linear model analysis on GSVA ES comparing at baseline (BL) either all (A) or clinical responder (R) (B) or clinical non-responder (NR) (C) participant samples in CD and UC to healthy volunteers. Also shown are the results comparing R vs NR at BL (D), post-treatment (PT) vs BL in R (E) and NR (F) respectively. In A, B and C, and in D, E and F the number of signatures positively and negatively enriched are listed respectively.
Fig 3. Hierarchical clustering heat map of gene set variation analysis enrichment scores of Crohn’s Disease participant samples.
Shown is the heat map resulting from the hierarchical clustering of the gene set variation (GSVA) enrichment scores (ES) of Crohn’s disease (CD) participant samples (GSE16879) using all signatures significantly enriched from comparing post-treatment (PT) vs baseline (BL) in clinical responders (R) and non-responders (NR) as well as comparing R to NR at BL from Fig 2.
Fig 4. Hierarchical clustering heat map of gene set variation analysis enrichment scores of ulcerative colitis participant samples.
Shown is the heat map resulting from the hierarchical clustering of the gene set variation (GSVA) enrichment scores (ES) of ulcerative colitis (UC) participant samples (GSE16879) using all signatures significantly enriched from comparing CD post-treatment (PT) vs baseline (BL) in clinical responders (R) and non-responders (NR) as well as comparing R to NR at BL from Fig 2.
Fig 5. T cell, B cell and monocyte scatterplot representation of gene set variation analysis enrichment scores for the Crohn’s disease and ulcerative colitis participant samples.
Shown are the scatterplots of the gene set variation (GSVA) enrichment scores (ES) of Crohn’s disease (CD) and ulcerative colitis (UC) participant samples (GSE16879) from signature #1_Tcell.activated.HS.IVS (A, D), #2_Bcell.activated.HS.IVS (B, E) and #3_Monocyte.activated.HS.IVS (C, F). Samples have been classified into normal healthy volunteer (NHV), clinical responders at baseline (R BL) or post-treatment (R PT) and clinical non-responders at baseline (NR BL) or post-treatment (NR PT). Panels A, B and C show the scores in UC while D, E and F show the scores in CD. Pair-wise T-test statistics are listed in S4 Table.
Fig 6. Hierarchical clustering heat map of gene set variation analysis enrichment scores of Crohn’s disease and ulcerative colitis participant baseline samples.
Shown is the heat map resulting from the hierarchical clustering of the gene set variation (GSVA) enrichment scores (ES) of the Crohn’s disease (CD) and ulcerative colitis (UC) participant baseline (BL) samples (GSE16879) using all signatures significantly enriched from comparing CD or UC post-treatment (PT) vs baseline (BL) samples in clinical non-responders (NR) from Fig 2A.
Fig 7. Inflammatory bowel disease molecular activity score classification of patient samples.
(A) Shown are the scatterplots of the sum of gene set variation (GSVA) enrichment scores (ES) using all 58 upregulated signatures in Fig 2A common for Crohn’s disease (CD) and ulcerative colitis (UC) participant samples (GSE16879) using the following formula: I_MDS score = ∑i=1nES(CDandUC)−1n*∑i=1nES(NHV). Samples have been classified into normal healthy volunteer (NHV), clinical responders at baseline (R BL) or post-treatment (R PT) and clinical non-responders at baseline (NR BL) or post-treatment (NR PT). Pair-wise T-test statistics are listed in S5 Table. (B) and (C) show the I_MDS score outputs identifying clinical responders and non-responders before treatment with 100% specificity and 78.8% sensitivity (B, GSE16879) and confirmation in an independent dataset [37, 38, 39] with 87.5% specificity and 85.7% sensitivity.
References
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