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Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model

A growing need to evaluate risk-adapted treatments in multiple myeloma (MM) exists. Several clinical and molecular scores have been developed in the last decades, which individually explain some of the variability in the heterogeneous clinical behavior of this neoplasm. Recently, we presented Iacobu...

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Autores principales: Mosquera Orgueira, Adrián, González Pérez, Marta Sonia, Díaz Arias, José Ángel, Antelo Rodríguez, Beatriz, Mateos, María-Victoria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348861/
https://www.ncbi.nlm.nih.gov/pubmed/35935610
http://dx.doi.org/10.1097/HS9.0000000000000760
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author Mosquera Orgueira, Adrián
González Pérez, Marta Sonia
Díaz Arias, José Ángel
Antelo Rodríguez, Beatriz
Mateos, María-Victoria
author_facet Mosquera Orgueira, Adrián
González Pérez, Marta Sonia
Díaz Arias, José Ángel
Antelo Rodríguez, Beatriz
Mateos, María-Victoria
author_sort Mosquera Orgueira, Adrián
collection PubMed
description A growing need to evaluate risk-adapted treatments in multiple myeloma (MM) exists. Several clinical and molecular scores have been developed in the last decades, which individually explain some of the variability in the heterogeneous clinical behavior of this neoplasm. Recently, we presented Iacobus-50 (IAC-50), which is a machine learning-based survival model based on clinical, biochemical, and genomic data capable of risk-stratifying newly diagnosed MM patients and predicting the optimal upfront treatment scheme. In the present study, we evaluated the prognostic value of the IAC-50 gene expression signature in an external cohort composed of patients from the Total Therapy trials 3, 4, and 5. The prognostic value of IAC-50 was validated, and additionally we observed a better performance in terms of progression-free survival and overall survival prediction compared with the UAMS70 gene expression signature. The combination of the IAC-50 gene expression signature with traditional prognostic variables (International Staging System [ISS] score, baseline B2-microglobulin, and age) improved the performance well above the predictability of the ISS score. IAC-50 emerges as a powerful risk stratification model which might be considered for risk stratification in newly diagnosed myeloma patients, in the context of clinical trials but also in real life.
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spelling pubmed-93488612022-08-04 Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model Mosquera Orgueira, Adrián González Pérez, Marta Sonia Díaz Arias, José Ángel Antelo Rodríguez, Beatriz Mateos, María-Victoria Hemasphere Article A growing need to evaluate risk-adapted treatments in multiple myeloma (MM) exists. Several clinical and molecular scores have been developed in the last decades, which individually explain some of the variability in the heterogeneous clinical behavior of this neoplasm. Recently, we presented Iacobus-50 (IAC-50), which is a machine learning-based survival model based on clinical, biochemical, and genomic data capable of risk-stratifying newly diagnosed MM patients and predicting the optimal upfront treatment scheme. In the present study, we evaluated the prognostic value of the IAC-50 gene expression signature in an external cohort composed of patients from the Total Therapy trials 3, 4, and 5. The prognostic value of IAC-50 was validated, and additionally we observed a better performance in terms of progression-free survival and overall survival prediction compared with the UAMS70 gene expression signature. The combination of the IAC-50 gene expression signature with traditional prognostic variables (International Staging System [ISS] score, baseline B2-microglobulin, and age) improved the performance well above the predictability of the ISS score. IAC-50 emerges as a powerful risk stratification model which might be considered for risk stratification in newly diagnosed myeloma patients, in the context of clinical trials but also in real life. Lippincott Williams & Wilkins 2022-08-02 /pmc/articles/PMC9348861/ /pubmed/35935610 http://dx.doi.org/10.1097/HS9.0000000000000760 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Mosquera Orgueira, Adrián
González Pérez, Marta Sonia
Díaz Arias, José Ángel
Antelo Rodríguez, Beatriz
Mateos, María-Victoria
Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title_full Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title_fullStr Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title_full_unstemmed Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title_short Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
title_sort prognostic stratification of multiple myeloma using clinicogenomic models: validation and performance analysis of the iac-50 model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348861/
https://www.ncbi.nlm.nih.gov/pubmed/35935610
http://dx.doi.org/10.1097/HS9.0000000000000760
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