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Predictive biomarkers for anti-TNF alpha therapy in IBD patients.

ABSTRACT: Inflammatory bowel disease (IBD) is a chronic gastrointestinal condition characterized by severe gut inflammation, commonly presenting as Crohn's disease, ulcerative colitis or categorized as IBD- unclassified. While various treatments have demonstrated efficacy in adult IBD patients, the advent of anti-TNF therapies has significantly revolutionized treatment outcomes and clinical management. These therapies have played a pivotal role in achieving clinical and endoscopic remission, promoting mucosal healing, averting disease progression, and diminishing the necessity for surgery. Nevertheless, not all patients exhibit positive responses to these therapies, and some may experience a loss of responsiveness over time. This review aims to present a comprehensive examination of predictive biomarkers for monitoring the therapeutic response to anti-TNF therapy in IBD patients. It will explore their limitations and clinical utilities, paving the way for a more personalized and effective therapeutic approach.

SUBMITTER: Kumar M

PROVIDER: S-EPMC10943853 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Predictive biomarkers for anti-TNF alpha therapy in IBD patients.

Kumar Manoj M Murugesan Selvasankar S Ibrahim Nazira N Elawad Mamoun M Al Khodor Souhaila S

Journal of translational medicine 20240316 1

Inflammatory bowel disease (IBD) is a chronic gastrointestinal condition characterized by severe gut inflammation, commonly presenting as Crohn's disease, ulcerative colitis or categorized as IBD- unclassified. While various treatments have demonstrated efficacy in adult IBD patients, the advent of anti-TNF therapies has significantly revolutionized treatment outcomes and clinical management. These therapies have played a pivotal role in achieving clinical and endoscopic remission, promoting muc ...[more]

PMID: 38493113

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Project description:BackgroundTumor necrosis factor-alpha (TNF-α) agonists revolutionized therapeutic algorithms in inflammatory bowel disease (IBD) management. However, approximately every third IBD patient does not respond to this therapy in the long term, which delays efficient control of the intestinal inflammation.MethodsWe analyzed the power of serum biomarkers to predict the failure of anti-TNF-α. We collected serum of 38 IBD patients at therapy prescription and 38 weeks later and analyzed them with relation to therapy response (no-, partial-, and full response). We used enzyme-linked immunosorbent assay to quantify 16 biomarkers related to gut barrier (intestinal fatty acid-binding protein, liver fatty acid-binding protein, trefoil factor 3, and interleukin (IL)-33), microbial translocation, immune system regulation (TNF-α, CD14, lipopolysaccharide-binding protein, mannan-binding lectin, IL-18, transforming growth factor-β1 (TGF-β1), osteoprotegerin (OPG), insulin-like growth factor 2 (IGF-2), endocrine-gland-derived vascular endothelial growth factor), and matrix metalloproteinase system (MMP-9, MMP-14, and tissue inhibitors of metalloproteinase-1).ResultsWe found that future full-responders have different biomarker profiles than non-responders, while partial-responders cannot be distinguished from either group. When future non-responders were compared to responders, their baseline contained significantly more TGF-β1, less CD14, and increased level of MMP-9, and concentration of these factors could predict non-responders with high accuracy (AUC = 0.938). Interestingly, during the 38 weeks, levels of MMP-9 decreased in all patients, irrespective of the outcome, while OPG, IGF-2, and TGF-β1 were higher in non-responders compared to full-responders both at the beginning and the end of the treatment.ConclusionsThe TGF-β1 and CD14 can distinguish non-responders from responders. The changes in biomarker dynamics during the therapy suggest that growth factors (such as OPG, IGF-2, and TGF-β) are not markedly influenced by the treatment and that anti-TNF-α therapy decreases MMP-9 without influencing the treatment outcome.

Project description:Background: Programmed cell death protein 1 (PD1) inhibitors have revolutionized cancer therapy, yet many patients fail to respond. Thus, the identification of accurate predictive biomarkers of therapy response will improve the clinical benefit of anti-PD1 therapy. Method: We assessed the baseline serological autoantibody (AAb) profile against ~2300 proteins in 10 samples and ~4600 proteins in 35 samples with alveolar soft part sarcoma (ASPS), non-small-cell lung cancer (NSCLC) and lymphoma using Nucleic Acid Programmable Protein Arrays (NAPPA). 23 selected potential AAb biomarkers were verified using simple, affordable and rapid enzyme linked immune sorbent assay (ELISA) technology with baseline plasma samples from 12 ASPS, 16 NSCLC and 46 lymphoma patients. SIX2 and EIF4E2 AAbs were further validated in independent cohorts of 17 NSCLC and 43 lymphoma patients, respectively, using ELISA. The IgG subtypes in response to therapy were also investigated. Results: Distinct AAb profiles between ASPS, NSCLC and lymphoma were observed. In ASPS, the production of P53 and PD1 AAbs were significantly increased in non-responders (p=0.037). In NSCLC, the SIX2 AAb was predictive of response with area under the curve (AUC) of 0.87, 0.85 and 0.90 at 3 months, 4.5 months, 6 months evaluation time points, respectively. In the validation cohort, the SIX2 AAb was consistently up-regulated in non-responders (p=0.024). For lymphoma, the EIF4E2 AAb correlated with a favorable response with AUCs of 0.68, 0.70, and 0.70 at 3 months, 4.5 months, and 6 months, respectively. In the validation cohort, the AUCs were 0.74, 0.75 and 0.66 at 3 months, 4.5 months, and 6 months, respectively. The PD1 and PD-L1 IgG2 AAbs were highly produced in ~20% of lymphoma responders. Furthermore, bioinformatics analysis revealed antigen functions of these AAb biomarkers. Conclusion: This study provides the first evidence that AAb biomarkers selected using high-throughput protein microarrays can predict anti-PD1 therapeutic response and guide anti-PD1 therapy.

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Predictive biomarkers for anti-TNF alpha therapy in IBD patients.

Publications

Predictive biomarkers for anti-TNF alpha therapy in IBD patients.

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