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Application of blood transcriptomics to identify three novel biomarkers for monitoring anti-TGFbeta therapy

ABSTRACT: Background: Development of target specific therapeutics greatly benefits from simultaneous identification of biomarkers to determine aspects of bioactivity, drug safety and efficacy or even treatment outcome. This is particularly important when targeting pleiotropic factors such as the TGFbeta system. TGFbeta has become a prime target for cancer therapeutics since inhibition of TGFbeta signaling simultaneously attacks the tumor and its microenvironment. Methods: Here we introduce blood transcriptomics followed by a defined set of validation assays as a promising approach to identify novel biomarkers for monitoring TGFbeta therapy. Findings: Our initial genome-wide analysis of transcription in peripheral blood revealed 12 candidate genes specifically regulated in peripheral blood by the TGFbeta receptor I kinase inhibitor LY2109761. In subsequent in vitro and in vivo molecular and immunological analyses, the combined monitoring of gene regulation of three genes, namely TMEPAI, OCIAD2, and SMAD7 was established as novel biomarkers for anti-TGFbeta based therapies. Interpretation: Overall, the proposed algorithm of biomarker identification is easily adapted towards other drug candidates for which gene regulation can be established in peripheral blood.

ORGANISM(S): Homo sapiens

PROVIDER: GSE15468 | GEO | 2010/07/20

SECONDARY ACCESSION(S): PRJNA115905

REPOSITORIES: GEO

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Project description:Background: Unbiased studies using different genome-wide methods have identified several promising novel biomarkers for diagnosis and treatment response in Rheumatoid Arthritis (RA). However, clinical translation has proven difficult. Here, we hypothesized that one reason could be that inflammatory responses in peripheral blood are different from those in the arthritic joint. Methods: We performed meta-analysis of gene expression microarray data from synovium, whole blood cells (WBC), peripheral blood mononuclear cells (PBMC), and CD4+ T cells from patients with RA and healthy control in order to identify overlapping pathways, predicted upstream regulators and potential biomarkers. We also analyzed single cell RNA-sequencing (scRNA-seq) data from peripheral blood and whole joints from a mouse model of antigen-induced arthritis. Results: Analyses of two profiling data sets from synovium from RA patients and healthy controls all showed significant activation of pathways of known pathogenic relevance, such as Th1 pathway, role of NFAT in regulation of the immune response, dendritic cell maturation, iCOS-iCOSL signaling in T helper cells, Fc? receptor-mediated phagocytosis, interferon signaling, Cdc42 signaling, and cytotoxic T lymphocyte-mediated apoptosis. The most activated upstream regulators included TNF, an important drug target, as well as IFN-gamma and CD40LG, all of which are known to play important pathogenic roles in RA. The differentially expressed genes from synovium included several potential biomarkers, such as CCL5, CCL13, CCL18, CX3CL1, CXCL6, CXCL9, CXCL10, CXCL13, IL15, IL32, IL1RN, SPP1, and TNFSF11. By contrast, microarray studies of WBC, PBMC and CD4 + T cells showed variable pathways and limited pathway overlap with synovium. Similarly, scRNA-seq data from a mouse model of arthritis did not support that inflammatory responses in peripheral blood reflect those in the arthritic joints. These data showed pathway overlap between mouse joint cells and synovium from patients with RA, but not with cells in peripheral blood. Conclusions: Our findings indicate a dichotomy between gene expression changes, pathways, upstream regulators and biomarkers in synovium and cell types in peripheral blood, which complicates identification of biomarkers in blood.

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Application of blood transcriptomics to identify three novel biomarkers for monitoring anti-TGFbeta therapy

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