Systemic and integrated analysis of the transcriptome of patients with rheumatoid arthritis submitted to treatment with anti-TNFa antibodies to determine the molecular pattern of response to treatment.

• Grant number: 23/12549-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2024
• End date: April 30, 2025
• Field of knowledge: Biological Sciences - Immunology - Immunogenetics
• Principal Investigator: Daniela Luz Hessel da Cunha
• Grantee: Gabriel Hiro Guedes Kobayakawa Fornel
• Host Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil

Abstract

Rheumatoid arthritis (RA) is characterized by chronic infiltration of immune cells into the synovial membrane, leading to progressive destruction of articular cartilage and bone. The most notable success in treating rheumatoid arthritis has been the introduction of Tumor Necrosis Factor (TNF) inhibitors. Anti-TNF agents have radically changed the prognosis of many patients with RA, providing important improvements in clinical signs and symptoms, quality of life, and long-term protection of synovial joint integrity. Despite this great achievement, there is a large fraction of patients treated with anti-TNF (30-40%) do not show significant clinical improvement. To date, little is known about the biological mechanisms underlying this differential response to anti-TNF agents. Clinical observations have shown that patients who fail one anti-TNF treatment may still respond to a different anti-TNF drug. In this context, the integration of high-throughput transcriptomic and genomic data offers a new opportunity to characterize the biological basis underlying complex traits and thus try to define molecular patterns related to the response to anti-TNF agents. Furthermore, currently, in the laboratory, recombinant anti-TNF antibodies are being selected that could serve as therapeutic tools. Understanding the basis of non-response can help personalize patient therapy and gain insight into the heterogeneity of RA at the molecular level. In this sense, the present project aims to analyze in a systemic and integrated way transcriptomes of patients with rheumatoid arthritis who underwent treatment with anti-TNFa antibodies, with the purpose of validating a predictive algorithm of molecular diagnosis by machine learning in order to confirm patterns that will help guide the development of more comprehensive antibodies for use in the therapy of this important chronic disease.