Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis

Ian Morilla; Mathieu Uzzan; David Laharie; Dominique Cazals-Hatem; Quentin Denost; Fanny Daniel; Genevieve Belleannee; Yoram Bouhnik; Gilles Wainrib; Yves Panis; Eric Ogier-Denis; Xavier Treton

doi:10.1016/j.cgh.2018.08.068

Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis

Clin Gastroenterol Hepatol. 2019 Apr;17(5):905-913. doi: 10.1016/j.cgh.2018.08.068. Epub 2018 Sep 14.

Authors

Affiliations

¹ Intestinal Inflammation Team, Research Centre of Inflammation, UMR1149, INSERM, Paris, France; Université Paris Diderot/Université Sorbonne Paris Cité, Paris, France; Laboratory of Excellence, Labex INFLAMEX, Université Sorbonne Paris Cité, Paris, France; LAGA CNRS UMR 7539, Université Paris 13, Université Sorbonne Paris Cité, Villetaneuse, France.
² Intestinal Inflammation Team, Research Centre of Inflammation, UMR1149, INSERM, Paris, France; Université Paris Diderot/Université Sorbonne Paris Cité, Paris, France; Laboratory of Excellence, Labex INFLAMEX, Université Sorbonne Paris Cité, Paris, France; Hôpital Beaujon, Assistance Publique Hôpitaux de Paris, Clichy la Garenne, France.
³ Service de gastroentérologie, Centre Hospitalier Universitaire de Bordeaux GH Sud - Hôpital Haut-Lévêque, Pessac, France.
⁴ Service de chirurgie digestive, Centre Hospitalier Universitaire de Bordeaux GH Sud - Hôpital Haut-Lévêque, Pessac, France.
⁵ Intestinal Inflammation Team, Research Centre of Inflammation, UMR1149, INSERM, Paris, France; Université Paris Diderot/Université Sorbonne Paris Cité, Paris, France; Laboratory of Excellence, Labex INFLAMEX, Université Sorbonne Paris Cité, Paris, France.
⁶ Département de pathologie, Centre Hospitalier Universitaire de Bordeaux GH Sud - Hôpital Haut-Lévêque, Pessac, France.
⁷ Owkin, Inc, New York, New York.
⁸ Intestinal Inflammation Team, Research Centre of Inflammation, UMR1149, INSERM, Paris, France; Université Paris Diderot/Université Sorbonne Paris Cité, Paris, France; Laboratory of Excellence, Labex INFLAMEX, Université Sorbonne Paris Cité, Paris, France; Hôpital Beaujon, Assistance Publique Hôpitaux de Paris, Clichy la Garenne, France; Service de chirurgie colorectale, Hôpital Beaujon, Clichy la Garenne, France.
⁹ Intestinal Inflammation Team, Research Centre of Inflammation, UMR1149, INSERM, Paris, France; Université Paris Diderot/Université Sorbonne Paris Cité, Paris, France; Laboratory of Excellence, Labex INFLAMEX, Université Sorbonne Paris Cité, Paris, France; Hôpital Beaujon, Assistance Publique Hôpitaux de Paris, Clichy la Garenne, France. Electronic address: [email protected].

PMID: 30223112
DOI: 10.1016/j.cgh.2018.08.068

Abstract

Background & aims: Acute severe ulcerative colitis (ASUC) is a life-threatening condition managed with intravenous steroids followed by infliximab, cyclosporine, or colectomy (for patients with steroid resistance). There are no biomarkers to identify patients most likely to respond to therapy; ineffective medical treatment can delay colectomy and increase morbidity and mortality. We aimed to identify biomarkers of response to medical therapy for patients with ASUC.

Methods: We performed a retrospective analysis of 47 patients with ASUC, well characterized for their responses to steroids, cyclosporine, or infliximab, therapy at 2 centers in France. Fixed colonic biopsies, collected before or within the first 3 days of treatment, were used for microarray analysis of microRNA expression profiles. Deep neural network-based classifiers were used to derive candidate biomarkers for discriminating responders from non-responders to each treatment and to predict which patients would require colectomy. Levels of identified microRNAs were then measured by quantitative PCR analysis in a validation cohort of 29 independent patients-the effectiveness of the classification algorithm was tested on this cohort.

Results: A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (area under the curve, 0.91). We identified 3 algorithms, based on microRNA levels, that identified responders to infliximab vs non-responders (84% accuracy, AUC = 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC = 0.79).

Conclusion: We developed an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues.

Keywords: Acute Severe Ulcerative Colitis; IBD; Neural Network; Prognostic Factor.

MeSH terms

Adult
Aged
Aged, 80 and over
Biomarkers / analysis*
Colitis, Ulcerative / drug therapy*
Colitis, Ulcerative / pathology*
Colon / pathology*
Deep Learning
Drug Monitoring / methods*
Female
France
Gene Expression Profiling / methods*
Hospitals
Humans
Male
MicroRNAs / analysis*
Middle Aged
Treatment Outcome
Young Adult

Substances

Biomarkers
MicroRNAs