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Multimodal profiling of peripheral blood identifies proliferating circulating effector CD4+ T cells as predictors for response to integrin α4β7-blocking therapy in patients with inflammatory bowel disease

Horn V. et al. (BioRxiv) DOI: 10.1101/2023.10.01.560386

Multimodal profiling of peripheral blood identifies proliferating circulating effector CD4+ T cells as predictors for response to integrin α4β7-blocking therapy in patients with inflammatory bowel disease


  • IBD

  • Vedolizumab

  • CD4+ T cells

Main Findings

The therapeutic arsenal to combat inflammatory bowel disease (IBD) has been steadily increasing over the past decades. IBD is a collective term of intestinal inflammatory pathologies with a multi-factorial origin and disease phenotype. Currently, IBD is grouped into two major disease subsets, called ulcerative colitis and Crohn’s disease. In part due to the variable disease pathology, it remains a challenge to treat and manage IBD patients due to the absence of reliable predictors for therapy response. 

In this preprint, the authors aim to address this issue in the context of patient responsiveness to an immunotherapy based on the anti-integrin α4β7 antibody vedolizumab, which modulates the infiltration of immune cells into the intestine. The authors broadly characterize the immune responses in peripheral blood of IBD (mostly ulcerative colitis) patients before and after the treatment with vedolizumab (n = 47) using a multi-omics approach (incl. scRNAseq, CITE-seq, immune repertoire profiling, cytometry, serum cytokine proteomics) and flow cytometry. IBD patients were followed up to 50 weeks of vedolizumab treatment, however most of the results were shown for 6 weeks after start of the therapy. Additionally, age- and sex-matched healthy controls were included. Vedolizumab treatment elicited alterations in the abundance of both innate (monocytes, dendritic cells) and adaptive immune cell compartments in circulation, as well as the integrin α4β7 expression on these cells in both responders and non-responders. Next, the authors showed an increased T cell receptor diversity of circulating gut-homing CD4+ memory T cells after treatment, but not CD8+ T cells or B cells. To identify which immune cell populations or their secreted cytokines could serve as response predictors, the authors integrated diverse parameters (including immune cell phenotypes, their abundance and secreted and circulatory cytokine levels) and applied a logistic regression model. Using this machine learning approach, the authors identified Ki67+ (proliferating) CD4+ effector memory T cells as potential predictors of vedolizumab treatment failure with AUC = 0.94.


  • The patient cohort has a good sample size to perform analytical assays, however that patient cohort consists mostly of ulcerative colitis patients, so the findings might not be necessarily applied to Crohn’s disease patients.

  • The structure of the paper needs improvement, as a lot of important findings were hidden in the supplementary. It is quite difficult for a reader to follow the logic of the authors, as seemingly some redundant data that do not support the main conclusions is also in the supplementary. For example, how did authors decide to look into T cell receptor diversity from the bulk approaches, as seemingly a lot of cell types are changing upon treatment?

  • The machine learning approach is on a limited sample size  with an unclear input number of patients, followed by validation in 15 (according to the main text) or 13 individuals (as mentioned in the supplementary). Therefore, the resulting high AUC should be taken with some caution. It would be interesting to see if the prediction could validated in external cohorts (however we acknowledge this is challenging. While it being a limitation, we would not expect the authors to include this in the current manuscript.).

  • Most of the presented findings for the “after treatment” condition are only 6 weeks after, it would be interesting to see more long-term changes as seemingly this data was also collected. How does Ki67+ cell proportion change in responders vs. non-responders?

  • For the clonotype analysis, it would be interesting to see not the bulk comparison, but if the TCR diversity increases for responders vs. non-responders.

  • The study would greatly benefit from a more thorough functional analysis of Ki67+ CD4+ effector memory T cells, as their phenotype, for example in the terms of being Th1 or Th17, remained unclear. If the dataset is large enough, a separation into clinical parameters may be warranted (ideally additional readouts of severity apart of calprotectin, i.e. symptoms, colonoscopic score)


The study used a comprehensive profiling of IBD patients before and after vedolizumab treatment with state-of-the-art methods and identified a Ki67+ CD4+ effector memory population associated with treatment failure. However, Ki67+ CD4+ T cells in IBD are not an entirely novel finding (Funderburg et al. Immunology 2013) , and this is a very general indicator of proliferating T cells during inflammation. Nevertheless, the study has very promising translational consequences for the IBD treatment, as Ki67+ CD4+ T cells should be easily detectable in patients’ blood, i.e. screening for this population may aid the choice of IBD treatment.


Reviewed by Deborah Gil and Alisa Iakupova as part of a cross-institutional journal club between the Vanderbilt University Medical Center (VUMC), the Max-Delbrück Center Berlin, the Medical University of Vienna and other life science institutes in Berlin and Vienna.

The authors declare no conflict of interests in relation to their involvement in the review.

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