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Original article
A blood-based prognostic biomarker in IBD
  1. Daniele Biasci1,
  2. James C Lee1,2,
  3. Nurulamin M Noor1,
  4. Diana R Pombal1,2,
  5. Monica Hou3,
  6. Nina Lewis4,
  7. Tariq Ahmad5,
  8. Ailsa Hart6,7,
  9. Miles Parkes1,
  10. Eoin F McKinney1,2,
  11. Paul A Lyons1,2,
  12. Kenneth G C Smith1,2
  1. 1 Department of Medicine, University of Cambridge, Cambridge, UK
  2. 2 Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK
  3. 3 PredictImmune Ltd, Cambridge, UK
  4. 4 Nottingham University Hospitals NHS Trust, Nottingham, UK
  5. 5 University of Exeter Medical School, Exeter, UK
  6. 6 St Mark’s Hospital, London, UK
  7. 7 Antigen Presentation Research Group, Imperial College, London, UK
  1. Correspondence to Dr James C Lee and Professor Kenneth G C Smith, Cambridge Institute of Therapeutic Immunology & Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK; jcl65{at}cam.ac.uk, kgcs2{at}cam.ac.uk

Abstract

Objective We have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).

Design Patients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).

Results In both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).

Conclusion This is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.

  • crohn’s disease
  • ulcerative colitis
  • gene expression
  • Ibd clinical
  • Ibd basic besearch

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

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Footnotes

  • DB and JCL contributed equally.

  • Contributors DB, JCL, EM, PAL and KGCS conceived the study. JCL, NN, NL, TA, AH and MP recruited patients and performed prospective phenotyping. DB analysed the microarray data to generate a candidate gene list and generated a scaled qPCR-based model. DRP and MH generated qPCR data for use in the final classifier. EM and PAL developed the final model. JCL and KGCS wrote the final manuscript with input from DB, MP, EM and PAL. All authors reviewed the final manuscript.

  • Funding This work was funded by the Wellcome Trust (Interim Translation Award 099450/Z/12/Z and Project Grant 094227/Z/10/Z), Crohn’s and Colitis UK (Medical Research Award M/09/2), Medical Research Council (Programme Grant MR/L019027/1) and the Cambridge NIHR Biomedical Research Centre. Analytical validation experiments were funded by PredictImmune. JCL and EM were supported by Wellcome Trust Intermediate Clinical Fellowships (105920/Z/14/Z and 104064/Z/14/Z respectively) and DB by a Marie Curie PhD Fellowship (TranSVIR FP7-PEOPLE-ITN-2008 #238756). KGCS is a Wellcome Trust Investigator.

  • Competing interests DB, JCL, EM, PAL and KGCS are coinventors on a patent covering the method of assessing prognosis in IBD. EM, PAL and KGCS are cofounders and consultants for PredictImmune. JCL is a consultant for PredictImmune.

  • Ethics approval Ethical approval was obtained from the Cambridgeshire Regional Ethics committee (REC08/H0306/21).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Patient consent for publication Not required.

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