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

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.
Competing Interests: Declaration of interests WEB—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences, named on a Kinomica patent; LN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; SFP—is an employee at Kinomica, owns Kinomica share options; AEC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; NN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JAC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; DNP—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; PMC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JK—is an employee at Kinomica, owns Kinomica share options; HRF—no conflict of interest; BP—no conflict of interest; PG—no conflict of interest; AA—no conflict of interest; AJA—received a honorarium for speaking engagements from Astellas; AT—received consultant fees from Kinomica for the role of Programme Director; AW—owns Kinomica share options, funded attendance and travel to conferences; GG—no conflict of interest; MDM—no conflict of interest; JGG—Kinomica co-founder, owns Kinomica share options; DJB—co-founder of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; PRC—co-founder and director of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; ADD—is an employee at Kinomica (CTO), named on Kinomica patents, Kinomica funded attendance and travel to conferences owns Kinomica share options.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)