AIRRC7 - TCR Repertoire as Biomarker for Autoimmune and Inflammatory Diseases (E. Mariotti)
11 بار بازدید -
2 هفته پیش
-
"Unveiling the Potential of Blood
"Unveiling the Potential of Blood T Cell Receptor Repertoire as Biomarkers for Autoimmune and Inflammatory Diseases"
Encarnita Mariotti-Ferrandiz, Sorbonne University, Associate Professor
Autoimmune and inflammatory diseases (AIDs) pose a significant societal burden due to their chronic and debilitating nature. Addressing the need for disease-specific treatments, curative interventions, and enhanced prognostic markers is imperative. While T and B lymphocytes play pivotal roles in autoimmune disorders by targeting tissues, their involvement in inflammatory conditions appears less specific, although some diseases exhibit tissue specificity. To bridge this gap, we conducted an analysis of the T-Cell Receptor (TCR) repertoire using next-generation sequencing in patients across various AID contexts.
TCR sequencing was performed on total blood or sorted CD4 T effector cells (Teff), CD4 T regulatory T-cells (Treg), and CD8 T-cells from the blood in different clinical trials. By employing a combination of classical and descriptive diversity analyses along with innovative machine learning (ML) techniques, our objectives were to provide an atlas of AIDs based on the TCR repertoire and identify TCR repertoires alterations across the autoimmune and inflammatory spectrum and within each condition.
We developed several TCR signature strategies adapted to the different datasets available and validated on publically available datasets classically used for ML method benchmarking.
We applied one of these strategies to (i) tissue-specific autoimmune disorders like type 1 diabetes (T1D) and rheumatoid arthritis (RA) in adult patients, (ii) sequential samples from lupus patients undergoing low-dose IL-2 treatment, and (iii) inflammatory diseases such as myocardial infarction and osteoarthritis. Through these analyses, we identified TCR signatures capable of predicting disease onset (in i), clinical response to treatment (in ii), or disease outcomes (in iii). Validation of these signatures on external datasets further supports their reliability.
In summary, our findings demonstrate that peripheral blood TCR repertoires harbor valuable disease-specific information that can serve as biomarkers to enhance the diagnosis, prognosis, and management of AIDs, ultimately improving patient care.
Finally, this study underscores the potential of ML approaches for classification purposes, highlighting their promise in the realm of biomarker discovery. However, it also emphasizes the critical need for high-quality and harmonized data sharing initiatives. By facilitating access to standardized datasets, we can refine ML methods and deepen our biological understanding of ML-based biomarkers. Such collaborative efforts hold the key to unlocking the full potential of ML in biomedical research and clinical applications.
AIRR Community Meeting VII – Learnings and Perspectives
June 3-6, 2024
University of Porto, Porto, Portugal
https://www.antibodysociety.org/the-a...
Encarnita Mariotti-Ferrandiz, Sorbonne University, Associate Professor
Autoimmune and inflammatory diseases (AIDs) pose a significant societal burden due to their chronic and debilitating nature. Addressing the need for disease-specific treatments, curative interventions, and enhanced prognostic markers is imperative. While T and B lymphocytes play pivotal roles in autoimmune disorders by targeting tissues, their involvement in inflammatory conditions appears less specific, although some diseases exhibit tissue specificity. To bridge this gap, we conducted an analysis of the T-Cell Receptor (TCR) repertoire using next-generation sequencing in patients across various AID contexts.
TCR sequencing was performed on total blood or sorted CD4 T effector cells (Teff), CD4 T regulatory T-cells (Treg), and CD8 T-cells from the blood in different clinical trials. By employing a combination of classical and descriptive diversity analyses along with innovative machine learning (ML) techniques, our objectives were to provide an atlas of AIDs based on the TCR repertoire and identify TCR repertoires alterations across the autoimmune and inflammatory spectrum and within each condition.
We developed several TCR signature strategies adapted to the different datasets available and validated on publically available datasets classically used for ML method benchmarking.
We applied one of these strategies to (i) tissue-specific autoimmune disorders like type 1 diabetes (T1D) and rheumatoid arthritis (RA) in adult patients, (ii) sequential samples from lupus patients undergoing low-dose IL-2 treatment, and (iii) inflammatory diseases such as myocardial infarction and osteoarthritis. Through these analyses, we identified TCR signatures capable of predicting disease onset (in i), clinical response to treatment (in ii), or disease outcomes (in iii). Validation of these signatures on external datasets further supports their reliability.
In summary, our findings demonstrate that peripheral blood TCR repertoires harbor valuable disease-specific information that can serve as biomarkers to enhance the diagnosis, prognosis, and management of AIDs, ultimately improving patient care.
Finally, this study underscores the potential of ML approaches for classification purposes, highlighting their promise in the realm of biomarker discovery. However, it also emphasizes the critical need for high-quality and harmonized data sharing initiatives. By facilitating access to standardized datasets, we can refine ML methods and deepen our biological understanding of ML-based biomarkers. Such collaborative efforts hold the key to unlocking the full potential of ML in biomedical research and clinical applications.
AIRR Community Meeting VII – Learnings and Perspectives
June 3-6, 2024
University of Porto, Porto, Portugal
https://www.antibodysociety.org/the-a...
2 هفته پیش
در تاریخ 1403/04/12 منتشر شده
است.
11
بـار بازدید شده