Evaluating Molecular and Clinical Predictors in Myelodysplastic Syndromes through Machine Learning Integration
Authors: Mosquera Orgueira Adrian , Perez Encinas Manuel , Pérez Míguez Carlos , Crucitti Davide , Piñeiro Fiel Manuel , Díaz Varela Nicolas , Mora Elvira , Díaz-Beyá Marina , Montoro Maria Julia , Pomares Helena , Ramos Ortega Fernando , Tormo Mar , Jerez Andres , Nomdedeu Josep , de Miguel Sanchez Carlos , Arenillas Leonor , Carcel Corella Paula , Cedena Romero Maria Teresa , Xicoy Blanca , Rivero María Eugenia , Del Orbe Barreto Rafael Andrés , Bewersdorf Jan Philipp , Stahl Maximilian , Stempel Jessica M. , Kewan Tariq , Zeidan Amer M. , Diez-Campelo Maria , Valcarcel David 
Published: December 2024

Introduction:

Recent advancements in the prognostication of Myelodysplastic Syndromes (MDS) have highlighted the importance of integrating genetic data with traditional clinical and laboratory parameters. MDS has traditionally been stratified using models such as the Revised International Prognostic Scoring System (IPSS-R) and, more recently, the molecular IPSS (IPSS-M). These models guide therapeutic decisions and clinical trial designs. Our previous work introduced the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) score, which demonstrated robust predictive accuracy using conventional data. This study aims to enhance AIPSS-MDS by incorporating molecular annotations.

Objectives:

The primary objective was to recalibrate the AIPSS-MDS model by integrating comprehensive molecular data to evaluate their prognostic significance alongside traditional clinical indices. We aimed to assess the model's ability to predict overall survival (OS) and leukemia-free survival (LFS) and identify specific genetic variables that independently affect prognosis.

Methodology:

We calculated the AIPSS-MDS scores for OS and LFS within a subset of patients in the IPSS-M cohort who had non-missing annotation (1,548 patients for OS and 1,427 for LFS). Then, we used the entire IPSS-M cohort (2,956 patients) to generate a random survival forest (RSF) model using all the 130 genetic variables. Afterwards, we conducted cox regression analyses to identify significant genetic predictors of OS and LFS independently of the AIPSS-MDS score, and integrated these into our models to create the molecular AIPSS score for MDS (AIPSSmol-MDS). Models were evaluated based on time-dependent Areas Under the Curve (AUCs) and out-of-bag concordance indexes (c-indexes).

Results:

The AIPSS-MDS yielded a c-index of 0.735 for OS and 0.801 for LFS, underscoring the robustness of traditional data inputs. The model based on the 130 genetic parameters (genetic-only) model showed a slightly lower c-index (0.685 for OS and 0.772 for LFS). The integrative model combining both data types achieved a c-index of 0.746 for OS and 0.805 for LFS, indicating that molecular data marginally enriches the prognostic landscape, and does not replace conventional data. Next, we used cox regression to identify gene mutations associated with prognosis independently of the AIPSS-MDS score. Mutations in ASXL1, CBL, EZH2, FLT3-ITD, KRAS, NRAS, RUNX1, SETBP1, and STAG2, as well as TP53 multihit mutations, emerged as significant predictors of OS; whereas mutations in ASXL1, EZH2, FLT3-ITD, MLL-PTD, RUNX1, STAG2, U2AF1, and WT1, as well as TP53 multi-hit mutations, were independent predictors of LFS. Incorporating these markers into AIPSSmol-MDS achieved c-indexes of 0.743 for OS and 0.812 for LFS, and improved moderately the time-dependent AUCs for LFS prediction. Independent validation efforts are ongoing and will be presented in ASH.

Conclusion:

Our study underscores the importance of conventional data in MDS prognostication and shows that adding layers of molecular annotations offers modest improvements in predictive accuracy. AIPSSmol-MDS, though slightly improved, remains a robust tool, especially where molecular data is unavailable. Future research should incorporate broader genomic data to further refine these models and enhance precision medicine in MDS.