Ian Morilla1, Mathieu Uzzan2, David Laharie3, Dominique Cazals-Hatem2, Quentin Denost4, Fanny Daniel5, Genevieve Belleannee6, Yoram Bouhnik2, Gilles Wainrib7, Yves Panis8, Eric Ogier-Denis5, Xavier Treton9. 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: xtreton@gmail.com.
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.
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.
Authors: Johanne Brooks-Warburton; James Ashton; Anjan Dhar; Tony Tham; Patrick B Allen; Sami Hoque; Laurence B Lovat; Shaji Sebastian Journal: Frontline Gastroenterol Date: 2021-12-10
Authors: John Gubatan; Steven Levitte; Akshar Patel; Tatiana Balabanis; Mike T Wei; Sidhartha R Sinha Journal: World J Gastroenterol Date: 2021-05-07 Impact factor: 5.742