Redefining high risk multiple myeloma with an APOBEC/Inflammation-based classifier.

Multiple myeloma (MM), a malignancy of plasma cells in the bone marrow, remains a significant clinical challenge due to its diverse presentation and progression.
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 Recent advancements in genomic and molecular biology have offered deeper insights into the pathogenesis of MM, revealing the involvement of various biological processes, including those driven by APOBEC deaminases and inflammation. Traditional risk stratification methods, such as the International Staging System (ISS) and its revisions (R-ISS and R2-ISS), predominantly rely on clinical parameters and cytogenetic abnormalities to assess patient risk. However, these methods do not fully capture the dynamic nature of MM progression or the molecular underpinnings of the disease.

 Our study introduces a novel risk classifier that incorporates molecular markers related to APOBEC activity and inflammation, alongside traditional clinical markers, to enhance MM risk stratification. This classifier, termed the Editor/Inflammation (EI) score, integrates mRNA levels of survival-associated APOBEC genes, pro/anti-inflammatory genes, and clinical markers to better reflect MM progression dynamics.

 Data for this study was derived from 599 newly diagnosed MM (NDMM) patients with available survival information from the CoMMpass database. An independent validation cohort from the IFM/DFCI 2009 trial was also analyzed. The EI score demonstrated superior performance in predicting OS and PFS compared to existing risk stratification methods. Notably, the addition of EI score gene expression information to traditional classifiers significantly improved their performance, highlighting the prognostic value of integrating molecular data with clinical and cytogenetic information.

Furthermore, the study revealed that MM patients with high-risk cytogenetic features (e.g., del(17p), t(4;14), and +1q) could be reclassified into different risk categories based on their EI score, suggesting a more nuanced understanding of risk in the context of molecular and genetic factors. This reclassification has potential implications for treatment planning and prognostication, indicating that patients previously deemed high-risk may have a more favorable prognosis than assumed when molecular markers are considered.

Our findings challenge the current reliance on cytogenetic abnormalities alone for risk stratification in MM and advocate for the integration of molecular markers that reflect disease progression mechanisms. While the EI score is a proof-of-concept that requires further validation and refinement before clinical application, it represents a significant step toward a more comprehensive and accurate risk stratification system in MM. This approach may ultimately facilitate more personalized treatment strategies, improving outcomes for patients with this complex disease.

In conclusion, the development of the EI score underscores the importance of incorporating molecular data into MM risk assessment. By moving beyond traditional cytogenetic markers to include factors related to APOBEC activity and inflammation, this innovative classifier offers a more detailed and dynamic perspective on MM risk, paving the way for improved patient management and treatment outcomes.

 

The EI-score reclassifies R2-ISS high-risk and del(17p) MM patients into favorable, intermediate, and unfavorable risk groups.

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Prognosis
Life Sciences > Health Sciences > Clinical Medicine > Prognosis
Plasma cells
Life Sciences > Biological Sciences > Anatomy > Haemic and Immune Systems > Immune system > Leukocytes > B cells > Plasma cells
Cancer Staging
Life Sciences > Biological Sciences > Cancer Biology > Cancer Staging
Cancer Biology
Life Sciences > Biological Sciences > Cancer Biology
  • Leukemia Leukemia

    This journal publishes high quality, peer reviewed research that covers all aspects of the research and treatment of leukemia and allied diseases. Topics of interest include oncogenes, growth factors, stem cells, leukemia genomics, cell cycle, signal transduction and molecular targets for therapy.