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

Abstract

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.