Phosphoproteomics predict response to midostaurin plus chemotherapy in independent cohorts of FLT3-mutated acute myeloid leukaemiaResearch in context

• Published in: EBioMedicine Vol. 108; p. 105316
• Main Authors: Weronika E. Borek, Luis Nobre, S. Federico Pedicona, Amy E. Campbell, Josie A. Christopher, Nazrath Nawaz, David N. Perkins, Pedro Moreno-Cardoso, Janet Kelsall, Harriet R. Ferguson, Bela Patel, Paolo Gallipoli, Andrea Arruda, Alex J. Ambinder, Andrew Thompson, Andrew Williamson, Gabriel Ghiaur, Mark D. Minden, John G. Gribben, David J. Britton, Pedro R. Cutillas, Arran D. Dokal
• Format: Journal Article
• Language: English
• Published: Elsevier 01-10-2024
• Subjects: Acute myeloid leukaemia; Drug response prediction; Machine learning; Midostaurin plus chemotherapy; Phosphoproteomics; Precision medicine
• ISSN: 2352-3964

Background: Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients. Methods: We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20). Findings: We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10−5, HR = 0.005 [95% CI: 0–0.31]). Interpretation: In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology. Funding: This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd.