Clinical utility of therapy selection informed by predicted nonresponse to tumor necrosis factor-ɑ inhibitors: an analysis from the Study to Accelerate Information of Molecular Signatures (AIMS) in rheumatoid arthritis.

Clinical utility TNFi therapy decision impact molecular signature precision medicine real-world evidence response prediction rheumatoid arthritis

Journal

Expert review of molecular diagnostics
ISSN: 1744-8352
Titre abrégé: Expert Rev Mol Diagn
Pays: England
ID NLM: 101120777

Informations de publication

Date de publication:
Jan 2022
Historique:
pubmed: 24 12 2021
medline: 2 4 2022
entrez: 23 12 2021
Statut: ppublish

Résumé

The molecular signature response classifier (MSRC) is a blood-based precision medicine test that predicts nonresponders to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA) so that patients with a molecular signature of non-response to TNFi can be directed to a treatment with an alternative mechanism of action. This study evaluated decision choice and treatment outcomes resulting from MSRC-informed treatment selection within a real-world cohort. Therapy selection by providers was informed by MSRC results for 73.5% (277/377) of patients. When MSRC results were not incorporated into decision-making, 62.0% (62/100) of providers reported deviating from test recommendations due to insurance-related restrictions. The 24-week ACR50 responses in patients prescribed a therapy in alignment with MSRC results were 39.6%. Patients with a molecular signature of non-response had significantly improved responses to non-TNFi therapies compared with TNFi therapies (ACR50 34.8% vs 10.3%, p-value = 0.05). This indicates that predicted non-responders to TNFi therapies are not nonresponders to other classes of RA targeted therapy. Significant changes were also observed for CDAI, ACR20, ACR70, and for responses at 12 weeks. Adoption of the MSRC into patient care could fundamentally shift treatment paradigms in RA, resulting in substantial improvements in real-world treatment outcomes.

Sections du résumé

BACKGROUND BACKGROUND
The molecular signature response classifier (MSRC) is a blood-based precision medicine test that predicts nonresponders to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA) so that patients with a molecular signature of non-response to TNFi can be directed to a treatment with an alternative mechanism of action.
RESEARCH DESIGN AND METHODS METHODS
This study evaluated decision choice and treatment outcomes resulting from MSRC-informed treatment selection within a real-world cohort.
RESULTS RESULTS
Therapy selection by providers was informed by MSRC results for 73.5% (277/377) of patients. When MSRC results were not incorporated into decision-making, 62.0% (62/100) of providers reported deviating from test recommendations due to insurance-related restrictions. The 24-week ACR50 responses in patients prescribed a therapy in alignment with MSRC results were 39.6%. Patients with a molecular signature of non-response had significantly improved responses to non-TNFi therapies compared with TNFi therapies (ACR50 34.8% vs 10.3%, p-value = 0.05). This indicates that predicted non-responders to TNFi therapies are not nonresponders to other classes of RA targeted therapy. Significant changes were also observed for CDAI, ACR20, ACR70, and for responses at 12 weeks.
CONCLUSIONS CONCLUSIONS
Adoption of the MSRC into patient care could fundamentally shift treatment paradigms in RA, resulting in substantial improvements in real-world treatment outcomes.

Identifiants

pubmed: 34937469
doi: 10.1080/14737159.2022.2020648
doi:

Substances chimiques

Antirheumatic Agents 0
Tumor Necrosis Factor-alpha 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

101-109

Auteurs

Vibeke Strand (V)

Division of Immunology/Rheumatology, Stanford University School of Medicine, Palo Alto, CA, USA.

Stanley B Cohen (SB)

Metroplex Clinical Research Center, Rheumatology Department, THR Presbyterian Hospital, Dallas, TX, USA.

Jeffrey R Curtis (JR)

Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.

Lixia Zhang (L)

Data Science, Scipher Medicine, Waltham, MA, USA.

Alan J Kivitz (AJ)

Altoona Center for Clinical Research, Duncansville, PA, USA.

Robert W Levin (RW)

Bay Area Rheumatology, Department of Medicine, University of South Florida, Clearwater, FL, USA.

Angela Mathis (A)

Data Science, Scipher Medicine, Waltham, MA, USA.

Erin Connolly-Strong (E)

Data Science, Scipher Medicine, Waltham, MA, USA.

Johanna B Withers (JB)

Data Science, Scipher Medicine, Waltham, MA, USA.

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Classifications MeSH