Amrinder Singh

• Singh, Amrinder; Fenton, Christopher Graham; Anderssen, Endre & Paulssen, Ruth H (2022). Identifying predictive signalling networks for Vedolizumab response in ulcerative colitis. International Journal of Colorectal Disease. ISSN 0179-1958. 37(6), s. 1321–1333. doi: 10.1007/s00384-022-04176-w. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Background In ulcerative colitis (UC), the molecular mechanisms that drive disease development and patient response to therapy are not well understood. A signifcant proportion of patients with UC fail to respond adequately to biologic therapy. Therefore, there is an unmet need for biomarkers that can predict patients’ responsiveness to the available UC therapies as well as ascertain the most efective individualised therapy. Our study focused on identifying predictive signalling pathways that predict anti-integrin therapy response in patients with UC. Methods We retrieved and pre-processed two publicly accessible gene expression datasets (GSE73661 and GSE72819) of UC patients treated with anti-integrin therapies: (1) 12 non-IBD controls and 41 UC patients treated with Vedolizumab therapy, and (2) 70 samples with 58 non-responder and 12 responder UC patient samples treated with Etrolizumab therapy without non-IBD controls. We used a difusion-based signalling model which is mainly focused on the T-cell receptor signalling network. The difusion model uses network connectivity between receptors and transcription factors. Results The network difusion scores were able to separate VDZ responder and non-responder patients before treatment better than the original gene expression. On both anti-integrin treatment datasets, the difusion model demonstrated high predictive performance for discriminating responders from non-responders in comparison with ‘nnet’. We have found 48 receptor-TF pairs identifed as the best predictors for VDZ therapy response with AUC≥0.76. Among these receptor-TF predictors pairs, FFAR2-NRF1, FFAR2-RELB, FFAR2-EGR1, and FFAR2-NFKB1 are the top best predictors. For Etrolizumab, we have identifed 40 best receptor-TF pairs and CD40-NFKB2 as the best predictor receptor-TF pair (AUC=0.72). We also identifed subnetworks that highlight the network interactions, connecting receptors and transcription factors involved in cytokine and fatty acid signalling. The fndings suggest that anti-integrin therapy responses in cytokine and fatty acid signalling can stratify UC patient subgroups. Conclusions We identifed signalling pathways that may predict the efcacy of anti-integrin therapy in UC patients and personalised therapy alternatives.

• Singh, Amrinder; Anderssen, Endre; Fenton, Christopher Graham & Paulssen, Ruth H (2021). Identifying anti-TNF response biomarkers in ulcerative colitis using a diffusion-based signalling model. Bioinformatics Advances. 1(1). doi: 10.1093/bioadv/vbab017. Fulltekst i vitenarkiv Vis sammendrag

  <p><i>Motivation:</i> Resistance to anti-TNF therapy in subgroups of ulcerative colitis (UC) patients is a major challenge and incurs significant treatment costs. Identification of patients at risk of nonresponse to anti-TNF is of major clinical importance. To date, no quantitative computational framework exists to develop a complex biomarker for the prognosis of UC treatment. Modelling patient-wise receptor to transcription factor (TF) network connectivity may enable personalized treatment. <p><i>Results:</i> We present an approach for quantitative diffusion analysis between receptors and TFs using gene expression data. Key TFs were identified using pandaR. Network connectivities between immune-specific receptor-TF pairs were quantified using network diffusion in UC patients and controls. The patient-specific network could be considered a complex biomarker that separates anti-TNF treatment-resistant and responder patients both in the gene expression dataset used for model development and separate independent test datasets. The model was further validated in rheumatoid arthritis where it successfully discriminated resistant and responder patients to tocilizumab treatment. Our model may contribute to prognostic biomarkers that may identify treatment-resistant and responder subpopulations of UC patients. <p><i>Availability and implementation:</i> Software is available at https://github.com/Amy3100/receptor2tfDiffusion. <p><i>Supplementary information:</i> Supplementary data are available at Bioinformatics Advances online.

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