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P299 Identification of a prognostic biomarker able to predict ulcerative colitis patients that will not respond to standard therapy

Pereira M.1, Carvalho S.1, Azevedo L.2, Albergaria A.1, Lima J.1, Reis C.3, Pedroto I.4, Maia L.4, Marcos-Pinto R.4, Lago P.4, Pinho S.S.*1

1IPATIMUP/i3S, University of Porto, Porto, Portugal 2University of Porto, Faculty of Medicine, Department of Health Information and Decision Sciences, Porto, Portugal 3IPATIMUP/i3S, University of Porto, Glycobiology in Cancer, Porto, Portugal 4Porto Centre Hospital, Hospital Santo Antόnio, Gastrenterology department, Porto, Portugal

Background

Ulcerative Colitis (UC) is associated with high rate of morbidity and disability. There is an urgent unmet need to identify a specific biomarker that early in the disease course select suitable patients for appropriate therapy, avoiding thereby unnecessary step-up therapies and patients'disability due to prolonged inflammation. Recently, we showed that aberrant glycosylation of T cells plays a crucial role in UC pathogenesis [1,2]. We herein studied whether this molecular marker is able to predict therapy response in UC patients, early in disease course.

Methods

131 formalin fixed paraffin-embedded colonic biopsies collected at the time of diagnosis from 131 UC patients were analyzed by immunohistochemistry in order to evaluate the expression of our biomarker (glycosylation levels in intestinal T cells). The relationship between biomarker expression and clinicopathological/therapeutic features of UC patients was analyzed. ROC curves were performed and the predictive value of the biomarker in the response to therapy was determined.

Results

Univariate analysis showed that our biomarker is able to predict patients' therapeutic outcome, early in disease course, by distinguishing patients that will display a stable disease course (always under 5-ASA) from those that will step-up therapy. High levels of biomarker expression, at/near to diagnosis can predict 78% (Negative Predictive Value - NPV) of the patients that will display a good disease course (always under 5ASA; p<0.05). When the biomarker is analyzed in severe UC patients (MayoE 3) at diagnosis, the sensitivity of the biomarker increase (from 46% to 64%), in which low levels of biomarker are able to predict 78% (Postive Predictive Value - PPV) of the UC patients that will step-up therapy to biologics (with bad disease course). Multivariate analysis revealed that only our biomarker and C Reactive Protein are shown to be independent predictors of non-response to standard therapy (5ASA; corticosteroids; immunomodulators). Interestingly, the ROC curve (AUC=0.714, p=0.001) revealed a powerful effect of both molecular parameters when analyzed together, suggesting an additive value in the prediction of the failure to standard therapy. This additive predictive effect was stronger when analyzed in severe patients (MayoE 3) in which the association of both biomarkers is able to predict 70% of the UC patients (PPV), early in the disease course, that will not respond to standard therapy.

Conclusion

Our results reveal a potential novel molecular tool in the prediction of failure to standard therapy in UC patients with promising prognostic value to be included in the algorithm of the therapy-decision making of UC patients.

References:

[1] Dias AM, Dourado J, Lago P, Cabral J, Marcos-Pinto R, Salgueiro P, Almeida CR, Carvalho S, Fonseca S, Lima M, Vilanova M, Dinis-Ribeiro M, Reis CA, Pinho SS., (2014), Dysregulation of T cell receptor N-glycosylation: a molecular mechanism involved in ulcerative colitis, Human Molecular Genetics

[2] Pinho SS and Reis CA, (2015), Glycosylation in Cancer: mechanisms and clinical implications. Nature Reviews Cancer