ABSTRACT
BACKGROUND AND AIMS: This study assessed whether baseline triggering receptor expressed on myeloid cells [TREM-1] whole blood gene expression predicts response to anti-TNF therapy in patients with UC or CD. METHODS: TREM-1 whole blood gene expression was analysed by RNA sequencing [RNA-seq] in patients with moderately to severely active UC or CD treated with adalimumab in the Phase 3 SERENE-UC and SERENE-CD clinical trials. The predictive value of baseline TREM-1 expression was evaluated and compared according to endoscopic and clinical response vs non-response, and remission vs non-remission, at Weeks 8 and 52 [SERENE-UC], and Weeks 12 and 56 [SERENE-CD]. RESULTS: TREM-1 expression was analysed in 95 and 106 patients with UC and CD, respectively, receiving standard-dose adalimumab induction treatment. In SERENE-UC, baseline TREM-1 expression was not predictive of endoscopic response [p=0.48], endoscopic remission [p=0.53], clinical response [p=0.58] or clinical remission [p=0.79] at Week 8, or clinical response [p=0.60] at Week 52. However, an association was observed with endoscopic response [p=0.01], endoscopic remission [p=0.048], and clinical remission [p=0.04997] at Week 52. For SERENE-CD, baseline TREM-1 expression was not predictive of endoscopic response [p=0.56], endoscopic remission [p=0.33], clinical response [p=0.07], clinical remission [p=0.65] at Week 12, or endoscopic response [p=0.61], endoscopic remission [p=0.51], clinical response [p=0.62] or clinical remission [p=0.97] at Week 56. CONCLUSIONS: Baseline TREM-1 gene expression did not uniformly predict adalimumab response in SERENE clinical trials. Further research is needed to identify potential blood-based biomarkers predictive of response to anti-TNF therapy in patients with IBD.
ABSTRACT
The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein , the source code at: https://github.com/uc-bd2k/grein , and the Docker container at: https://hub.docker.com/r/ucbd2k/grein .
