2026-09-14 東京大学
多発性骨髄腫 AI治療支援
＜関連情報＞
- https://www.s.u-tokyo.ac.jp/ja/press/11222/
- https://academic.oup.com/bib/article/27/5/bbag475/8787147?login=false
遺伝子発現、縦断的検査値、および治療歴を統合した多発性骨髄腫における動的なマルチモーダル生存予測 Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratory measurements, and treatment history
Shangru Jia,Artem Lysenko,Keith A Boroevich,Alok Sharma,Tatsuhiko Tsunoda
Briefings in Bioinformatics Published:07 September 2026
DOI:https://doi.org/10.1093/bib/bbag475
Abstract
Prognostic stratification in multiple myeloma (MM) relies on staging systems fixed at diagnosis, discarding temporal information accumulated during treatment. We developed a dynamic multimodal framework that predicts residual overall survival from observation windows of 1–18 months post-diagnosis. The model integrates DeepInsight-transformed gene expression, longitudinal trajectories of 10 laboratory analytes, and treatment history through missingness-aware gated fusion. On the Multiple Myeloma Research Foundation (MMRF) cohort from the Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile (CoMMpass) study (n = 752), five-fold-specific models trained on the development dataset achieved a mean concordance index (C-index) of 0.773 ± 0.024 and 1-year time-dependent area under the receiver operating characteristic curve (AUC) of 0.789 ± 0.021 on a common held-out CoMMpass validation split, outperforming the evaluated survival-learning baselines including DeepSurv, a Cox proportional hazards neural network, and random survival forests. Kaplan–Meier stratification showed significant separation at all primary landmarks (log-rank P＜ .001, hazard ratios 3.46–3.93). A distilled student model retaining only the DeepInsight gene expression representation and five baseline clinical features transferred to an independent microarray cohort (GSE24080, n = 507) without retraining, achieving a C-index of 0.672 and a time-dependent AUC at 1-year of 0.740, supporting cross-cohort transferability in a reduced-input setting. Interpretability analyses recovered ubiquitin-proteasome, endoplasmic reticulum (ER) stress, and Interferon Alpha Response signals consistent with established myeloma biology. These findings support the potential of dynamic multimodal modeling for longitudinal prognostic assessment in MM.
