Precision oncology offers a new approach to cancer care by providing targeted treatments for patients with actionable genetic mutations. However, in the U.S., nearly one-third of eligible patients face barriers such as delayed test results, limited awareness and access issues, impacting survival outcomes.
Pierantonio Russo, MD, Ramaa Nathan, PhD, Daniel Pfeffer, PhD, and Alex Moore, PhD, conducted a study that highlights the potential of targeted therapies for patients with myelodysplastic syndromes (MDS), a challenging hematologic malignancy. By analyzing genomic and claims data from over 3,600 patients, the research demonstrates a 46.5% reduction in 12-month mortality rates among patients receiving targeted therapies compared to those who did not. Remarkably, therapies like enasidenib (IDH2 mutations) showed a near-complete elimination of risk, underscoring their impact.
Despite these advances, only 9% of biomarker-positive MDS patients received targeted treatments, pointing to critical disparities in care and access. Addressing these gaps is vital to ensure all eligible patients benefit from life-extending therapies, particularly given their significant potential to improve survival and quality of life.
This research underscores the urgency of bridging access gaps and integrating targeted approaches into routine MDS care—unlocking better outcomes for every patient.
Download the poster below to view the full research and findings.
Author
After leaving the Mayo Clinic, from 1988 to 2007, he held academic, clinical and administrative leadership positions as Chief of Cardiac Surgery, Pediatric Cardiac Surgery and Heart Transplantation at several Academic Hospitals in the…
Ramaa Nathan is a Director of Data Science at EVERSANA with several years of experience in the financial and healthcare industries. Ramaa is a data scientist and statistician with extensive programming, statistical and data…
Alex holds a PhD in chemical engineering from University of Pennsylvania with a focus on molecular simulation of organic glasses utilizing predictive modeling techniques. At University of Pennsylvania, he gained years of hands-on experience…
Daniel has a background in cosmology where he developed tools to apply machine learning techniques to the field. At EVERSANA, he has worked extensively with medical insurance claims and electronic health records (EHR) on many…