What question did this study set out to answer?

The study aims to develop a machine learning-based gene signature to enhance risk stratification in multiple myeloma and identify potential therapeutic targets.

May 29, 2026

A machine learning (ML)–based six-gene signature for risk stratification and therapeutic target identification in multiple myeloma.

Key Points

The study aims to develop a machine learning-based gene signature to enhance risk stratification in multiple myeloma and identify potential therapeutic targets.
Performed differential gene expression (DGE) and gene set enrichment analysis (GSEA) on TP53 mutant and wild-type multiple myeloma samples.
Constructed models using Cox regression and four machine learning algorithms: LASSO, CoxBoost, XGBoost, and Random Forest.
Achieved external validation using GSE24080 dataset with a focus on drug sensitivity analysis across 16 multiple myeloma cell lines.
The CoxBoost model produced a six-gene signature with a C-index of 0.742, significantly outperforming clinical variables alone (C-index 0.629, p<0.001).
Identified three compounds (MACIMORELIN, MW-150, BROFAROMINE) with selective activity against high-risk multiple myeloma cells (p<0.001).
The six-gene signature demonstrated minimal overfitting (training C-index 0.748 vs. test C-index 0.742) and a significant hazard ratio of 1.95 (95% CI: 1.51-2.52, p<0.001) in multivariable analysis.

Abstract

7558 Background: Multiple myeloma(MM) accounts for 10-15% of hematologic malignancies with significant heterogeneity. Current risk stratification relies on clinical variables (ISS, sex, race, age, cytogenetics) but has limited prognostic accuracy (C-index ~0.63).TP53mt MM has worse outcomes, present in only 5% of newly diagnosed patients and increase to 25% at progression. Methods: We performed differential gene expression (DGE) and gene set enrichment analysis (GSEA) between TP53mt (36) and TP53wt (717) MM from (MMRF CoMMpass). Intersection of DGE and GSEA identified 46 genes, which along with clinical variables were modeled using Cox regression and 4 ML algorithms: LASSO, CoxBoost, XGBoost and Random Forest. Models were trained on 70% and tested on 30% of data, with external validation on GSE24080 (n=559).The final 6-gene signature was functionally evaluated in drug sensitivity data from 16 MM cell lines (DepMap). Performance was assessed using concordance index (C-index) and hazard ratios (HR). Results: CoxBoost model 6-gene signature (UBE2C, STMN1, TK1, DSCC1, PTTG1, CENPF) outperformed all other models. Combined with clinical variables, it achieved C-index 0.742 in discovery (95% CI: 0.708-0.776) versus 0.629 for clinical variables alone (+18.0% improvement, p<0.001).Minimal overfitting was observed (training: 0.748, test: 0.742, gap: 0.006). In multivariable Cox analysis, the CoxBoost risk score remained highly significant (HR 1.95, 95%CI:1.51-2.52,p<0.001) independent of ISS stage (HR 2.89, p<0.001) and age (HR 1.39 per decade, p<0.001).External validation on GSE24080 showed improved performance (C-index 0.683,+6.8% over clinical variables).The signature showed 50% gene overlap with LASSO (3/6 genes) and outperformed UAMS-70 (C-index 0.742 vs 0.631).Drug sensitivity analysis identified three compounds (MACIMORELIN, MW-150, BROFAROMINE) with selective activity against high-risk MM cells (p<0.001),validated across 16 cell lines. Conclusions: TP53 and its downstream pathways are central to MM progression. CoxBoost modeling produced a 6-gene signature that improved MM risk stratification over clinical variables, identifies therapeutic vulnerabilities, and is computationally efficient model suitable for clinical deployment, warranting prospective validation in MM treatment stratification. Performance of clinical-only and clinic-genomic machine learning models. Model Train C-Index Test C-Index Overfitting Improvement vs Clinical %Improvement Clinical 0.69 0.72 -0.03 0 0 Random Forest 0.57 0.59 -0.02 -0.13 -18.56 LASSO 0.70 0.68 0.02 -0.04 -5.89 XGBoost 0.81 0.65 0.16 -0.06 -8.95 CoxBoost 0.75 0.74 0.01 0.02 3.31

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