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Patient outcomes improve when a molecular signature test guides treatment decision-making in rheumatoid arthritis.
Curtis, Jeffrey R; Strand, Vibeke; Golombek, Steven; Zhang, Lixia; Wong, Angus; Zielinski, Mark C; Akmaev, Viatcheslav R; Saleh, Alif; Asgarian, Sam; Withers, Johanna B.
Afiliación
  • Curtis JR; Division of Clinical Immunology and Rheumatology, The University of Alabama at Birmingham, Birmingham, AL, USA.
  • Strand V; Division of Immunology/Rheumatology, Stanford University, Palo Alto, CA, USA.
  • Golombek S; Allergy, Asthma & Arthritis Associates, St. Clare's Health, Denville, NJ, USA.
  • Zhang L; Scipher Medicine Corporation, Waltham, MA, USA.
  • Wong A; Scipher Medicine Corporation, Waltham, MA, USA.
  • Zielinski MC; Scipher Medicine Corporation, Waltham, MA, USA.
  • Akmaev VR; Scipher Medicine Corporation, Waltham, MA, USA.
  • Saleh A; Scipher Medicine Corporation, Waltham, MA, USA.
  • Asgarian S; Scipher Medicine Corporation, Waltham, MA, USA.
  • Withers JB; Scipher Medicine Corporation, Waltham, MA, USA.
Expert Rev Mol Diagn ; : 1-10, 2022 Nov 03.
Article en En | MEDLINE | ID: mdl-36305319
Clinicians can offer rheumatoid arthritis patients many types of therapies but the response rate for each of these drugs is low. For example, within the first year of treatment, just about one-half of patients respond to the first-line drug, csDMARD. Only one-third of methotrexate-unresponsive patients will respond to the most common second-line agent, a tumor necrosis factor-α inhibitor. These low response rates present a critical challenge to treating patients. Clinicians try different cs- and b/tsDMARD and fail to quickly identify the most effective options. Then, disease will progress, irreversibly destroying patient joints, diminishing patient health-related quality of life, and increasing risks of cardiovascular disease, cancer, and death. To help clinicians quickly identify the best drugs for patients in a treat-to-target approach, a precision-medicine test was developed to identify patients unlikely to respond to tumor necrosis factor-α inhibitors. This molecular signature response classifier considers both molecular features (patient RNA-expression levels) and clinical features (e.g. body mass index, sex) to predict patient response. To evaluate the effectiveness of this test, the outcomes of patients treated with classifier-selected drugs (in a large, tested cohort) were compared with outcomes of patients treated with conventionally selected therapies (in an external cohort of electronic-health-record data). Patients treated with classifier-selected therapies were approximately three times as likely to achieve remission than were patients treated with conventionally selected drugs. These results suggest that this molecular signature response classifier is a valuable tool for more quickly identifying optimal therapies to treat rheumatoid arthritis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Expert Rev Mol Diagn Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Expert Rev Mol Diagn Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido