AI-derived five-gene signature predicts risk in multiple myeloma under bortezomib-based therapy

Multiple myeloma (MM) is a plasma cell malignancy characterized by clonal expansion within the bone marrow (BM) and remains largely incurable despite recent therapeutic advances. While prognostic indicators have predominantly focused on tumor-intrinsic features, the BM tumor microenvironment (TME) plays a critical role in disease progression and therapeutic resistance. Here, we present a robust prognostic model derived from transcriptomic profiling of the CD138-negative BM fraction, enabling precise risk stratification of patients undergoing bortezomib-based induction therapy. Using bioinformatic deconvolution and a custom immune-stromal signature matrix, we identified gene expression patterns representative of the MM TME. Through rigorous feature selection and elastic-net penalized Cox regression, we developed the MM-5C model, consisting of five genes (SOX11, METTL11B, C3, RBM10, and HOMEZ), which stratifies patients into biologically distinct risk groups and demonstrates prognostic independence from established cytogenetic and clinical staging systems. This model underscores the pivotal role of TME components in shaping therapeutic outcomes and offers a scalable, clinically translatable tool for personalized risk stratification. Our findings highlight the necessity of integrating microenvironmental insights into MM prognostication and pave the way for microenvironment-informed therapeutic decision-making.

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Publication Details

Journal
Scientific Reports
Published
2025-12-03
DOI
https://doi.org/10.1038/s41598-025-30527-y
Primary Topic
Multiple Myeloma Research and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

AI-derived five-gene signature predicts risk in multiple myeloma under bortezomib-based therapy

Orazio Palumbo, B. Rossini, Nicoletta Del Buono, Maria Carmela Vegliante et al.
Scientific Reports
Multiple Myeloma Research and Treatments
article

AI-derived five-gene signature predicts risk in multiple myeloma under bortezomib-based therapy

Orazio Palumbo, B. Rossini, Nicoletta Del Buono, Maria Carmela Vegliante, Doriana Gramegna, Angela Maria Quinto, Paolo Mondelli, Grazia Gargano, Flavia Esposito, Carolina Terragna, Vincenza Solli, Attilio Guarini, Gian Maria Zaccaria, Sabino Ciavarella, Michèle Cavo, Giuseppina Opinto, Susanna Anita Pappagallo
article en

Abstract

Multiple myeloma (MM) is a plasma cell malignancy characterized by clonal expansion within the bone marrow (BM) and remains largely incurable despite recent therapeutic advances. While prognostic indicators have predominantly focused on tumor-intrinsic features, the BM tumor microenvironment (TME) plays a critical role in disease progression and therapeutic resistance. Here, we present a robust prognostic model derived from transcriptomic profiling of the CD138-negative BM fraction, enabling precise risk stratification of patients undergoing bortezomib-based induction therapy. Using bioinformatic deconvolution and a custom immune-stromal signature matrix, we identified gene expression patterns representative of the MM TME. Through rigorous feature selection and elastic-net penalized Cox regression, we developed the MM-5C model, consisting of five genes (SOX11, METTL11B, C3, RBM10, and HOMEZ), which stratifies patients into biologically distinct risk groups and demonstrates prognostic independence from established cytogenetic and clinical staging systems. This model underscores the pivotal role of TME components in shaping therapeutic outcomes and offers a scalable, clinically translatable tool for personalized risk stratification. Our findings highlight the necessity of integrating microenvironmental insights into MM prognostication and pave the way for microenvironment-informed therapeutic decision-making.

Scientific ReportsVol. 16(1)
Casa Sollievo della Sofferenza (IT), Azienda USL di Bologna (IT), Istituto Tumori Bari (IT), Ospedale L. Bonomo (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), University of Bari Aldo Moro (IT), Polytechnic University of Bari (IT)
Istituto Nazionale di Alta Matematica "Francesco Severi", Ministero della Salute, Gruppo Nazionale per il Calcolo Scientifico
Openalex Percentile: Top 50%
Multiple Myeloma Research and Treatments
DOIOpen Access PDFOpenAlex
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