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Elsa Bernard, Ph.D.
Beyond Genetic Heterogeneity in MDS: Towards Molecularly Guided Classification, Risk Stratificiation and Treatment Decisions

Elsa Bernard, Ph.D.

Memorial Sloan-Kettering Cancer Center

2020 Funding recipient

Beyond Genetic Heterogeneity in MDS: Towards Molecularly Guided Classification, Risk Stratificiation and Treatment Decisions

EvansMDS Young Investigator Award

PROJECT SUMMARY

Clinical management of MDS patients is challenged by heterogeneity in clinical presentation and outcomes. The disparate symptoms and survival of patients within established nosological entities, as defined by the WHO and the revised International Prognostic Scoring System (IPSS-R), call for better biomarkers in the diagnostic workup of MDS. Despite our knowledge of the genes mutated in MDS and the implementation of routine profiling at diagnosis, our understanding of how gene mutations should inform clinical decisions remains limited. The large number of recurrently mutated genes (>80) and the fact that most patients harbor several driver mutations (median 3) have hindered the translation of molecular findings into actionable clinical practice.

The heterogeneity in clinical presentation and response to therapy certainly mirrors diverse genetic etiologies. While hypomethylating agents (HMA) are the mainstay of therapy for higher risk MDS, only half of patients respond and most responders experience treatment failure within 2 years with a markedly poor survival thereafter. Taken together, there is a pressing need to improve clinical management of MDS patients and to select the treatment strategies that are best tailored to the patients’ molecular and clinical profiles.

The present proposal aims to use population genomic approaches to learn the genetic components that underpin MDS pathogenesis and how they underlie disease presentation, treatment response and outcomes.

Our preliminary data provide strong supportive evidence that with statistically powered cohorts and methods to account for genetic heterogeneity we can uncover disease-defining molecular subgroups and identify robust prognostic and predictive biomarkers. Our end goal is to characterize reproducible biomarkers to support early and accurate diagnosis, better risk assessment and optimal treatment strategies. To translate our findings into formal clinical guidelines, we work with the MDS International Working Group (IWG) for Classification and Prognosis.