Clinical Bioinformatics Research Group, Department of Clinical Medicine, Faculty of Health Sciences, UiT- The Arctic University of Norway, Tromsø, Norway.
Genomics Support Centre Tromso, UiT- The Arctic University of Norway, Department of Clinical Medicine, Faculty of Health Sciences, UiT- The Arctic University of Norway, Tromso, Norway.
Int J Colorectal Dis. 2022 Jun;37(6):1321-1333. doi: 10.1007/s00384-022-04176-w. Epub 2022 May 11.
In ulcerative colitis (UC), the molecular mechanisms that drive disease development and patient response to therapy are not well understood. A significant 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 effective individualised therapy. Our study focused on identifying predictive signalling pathways that predict anti-integrin therapy response in patients with UC.
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 diffusion-based signalling model which is mainly focused on the T-cell receptor signalling network. The diffusion model uses network connectivity between receptors and transcription factors.
The network diffusion 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 diffusion model demonstrated high predictive performance for discriminating responders from non-responders in comparison with 'nnet'. We have found 48 receptor-TF pairs identified 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 identified 40 best receptor-TF pairs and CD40-NFKB2 as the best predictor receptor-TF pair (AUC = 0.72). We also identified subnetworks that highlight the network interactions, connecting receptors and transcription factors involved in cytokine and fatty acid signalling. The findings suggest that anti-integrin therapy responses in cytokine and fatty acid signalling can stratify UC patient subgroups.
We identified signalling pathways that may predict the efficacy of anti-integrin therapy in UC patients and personalised therapy alternatives. Our results may lead to the advancement of a promising clinical decision-making tool for the stratification of UC patients.
在溃疡性结肠炎（UC）中，驱动疾病发展和患者对治疗反应的分子机制尚不清楚。相当一部分 UC 患者对生物治疗反应不足。因此，需要生物标志物来预测患者对现有 UC 治疗的反应，并确定最有效的个体化治疗。我们的研究重点是确定预测信号通路，以预测 UC 患者对抗整合素治疗的反应。
我们检索并预处理了两个公开的 UC 患者接受抗整合素治疗的基因表达数据集（GSE73661 和 GSE72819）：（1）12 名非炎症性肠病对照和 41 名接受Vedolizumab 治疗的 UC 患者；（2）70 名样本，其中 58 名非应答者和 12 名应答者 UC 患者接受 Etrolizumab 治疗，无非炎症性肠病对照。我们使用了一种主要集中在 T 细胞受体信号网络的基于扩散的信号模型。扩散模型使用受体和转录因子之间的网络连接性。
在治疗前，网络扩散评分比原始基因表达更能区分 VDZ 应答者和非应答者。在两个抗整合素治疗数据集上，与“nnet”相比，扩散模型在区分应答者和非应答者方面表现出较高的预测性能。我们发现了 48 对受体-TF 对，被确定为 VDZ 治疗反应的最佳预测因子，AUC≥0.76。在这些受体-TF 预测因子对中，FFAR2-NRF1、FFAR2-RELB、FFAR2-EGR1 和 FFAR2-NFKB1 是最佳预测因子。对于 Etrolizumab，我们确定了 40 对最佳受体-TF 对，CD40-NFKB2 是最佳预测受体-TF 对（AUC=0.72）。我们还确定了突出连接细胞因子和脂肪酸信号转导中受体和转录因子的网络相互作用的子网络。研究结果表明，细胞因子和脂肪酸信号转导中的抗整合素治疗反应可以对 UC 患者亚组进行分层。
我们确定了可能预测 UC 患者抗整合素治疗疗效和个性化治疗替代方案的信号通路。我们的结果可能为 UC 患者分层的有前途的临床决策工具的发展提供依据。
