Genomic characterization of functional high-risk multiple myeloma patients

Area of Science:

• Hematology
• Oncology
• Genomics

Background:

• Multiple myeloma (MM) patients with suboptimal response or early relapse, termed functional high-risk (FHR), exhibit poor prognoses.
• Risk stratification in MM is crucial for guiding treatment strategies and predicting patient outcomes.

Purpose of the Study:

• To identify and characterize functional high-risk (FHR) multiple myeloma (MM) patients.
• To investigate the genomic and molecular features associated with FHR MM.
• To develop a predictive tool for identifying FHR MM at diagnosis.

Main Methods:

• Analysis of the CoMMpass dataset to classify newly-diagnosed MM patients into genomic high-risk (GHR), FHR, and standard-risk (SR) groups.
• Evaluation of genomic markers (translocations, TP53 alterations, 1q21 gain) and International Staging System (ISS) stage for GHR classification.
• Assessment of response to induction therapy and relapse timing for FHR classification.
• Analysis of mutations in the IL-6/JAK/STAT3 pathway, gene expression profiles, and mutational signatures in FHR patients.
• Development of a machine learning classifier for FHR patient identification.

Main Results:

• FHR patients demonstrated the poorest survival outcomes compared to GHR and SR groups.
• FHR MM is characterized by increased mutations in the IL-6/JAK/STAT3 pathway.
• A gene expression profile linked to aberrant mitosis and DNA damage response was observed in FHR patients.
• Mutational signatures associated with abnormal DNA damage response corroborated these findings.
• A machine learning classifier successfully identified most FHR patients at the time of diagnosis.

Conclusions:

• Functional high-risk multiple myeloma is associated with distinct molecular alterations, including the IL-6/JAK/STAT3 pathway and DNA damage response mechanisms.
• These findings provide insights into the biology of aggressive MM and potential therapeutic targets.
• A machine learning approach can aid in early identification of high-risk MM patients, facilitating timely intervention.