Advanced Method for Predicting Response to Midostaurin plus Chemotherapy in Acute Myeloid Leukaemia Greatly Outperforms Current Companion Diagnostic
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The publication details how mass spectrometry phosphoproteomics was combined with machine learning to identify biomarkers of response to MIC. When applied to two independent retrospective real-world patient test cohorts (n=20) the method predicted MIC response with 83% sensitivity, 100% specificity and HR = 0.005 [95% CI: 0-0.31], greatly outperforming the current FLT3 status-based, stratification method for MIC.
The study also identifies druggable targets in patients that would not be able to receive MIC, with implications for relapse and refractory patients.
"This work has wide-ranging implication in AML and beyond. For AML, the predictive model presented can be used to better identify patients that would benefit from MIC treatment, and could have utility for patients who are currently ineligible for MIC treatment due to lack of mutations in FLT3," said Dr Arran Dokal, the study's corresponding author and Kinomica CTO. "For precision medicine in general, this study highlights the important role that phosphoproteomics is destined to play in drug response prediction and clinical decision-making."
The work was generously supported by two Innovate UK grants.
About Kinomica
Kinomica is a developer of precision oncology diagnostics. The company has developed KScan®, a phosphoproteomic diagnostic platform to help clinicians better realize the full potential of precision medicine by predicting which of the drugs currently approved to treat a disease a particular patient will respond best to, thereby aiding clinical decision making. Learn more at www.kinomica.com and follow us on LinkedIn.
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