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

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

doi:10.1016/j.ebiom.2024.105316

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

EBioMedicine. 2024 Sep 17:108:105316. doi: 10.1016/j.ebiom.2024.105316. Online ahead of print.

Authors

Weronika E Borek¹, Luis Nobre¹, S Federico Pedicona¹, Amy E Campbell¹, Josie A Christopher¹, Nazrath Nawaz¹, David N Perkins¹, Pedro Moreno-Cardoso¹, Janet Kelsall¹, Harriet R Ferguson¹, Bela Patel², Paolo Gallipoli², Andrea Arruda³, Alex J Ambinder⁴, Andrew Thompson¹, Andrew Williamson¹, Gabriel Ghiaur⁴, Mark D Minden⁵, John G Gribben², David J Britton¹, Pedro R Cutillas⁶, Arran D Dokal⁷

Affiliations

¹ Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom.
² Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
³ Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
⁴ Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, USA.
⁵ Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
⁶ Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
⁷ Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom. Electronic address: a.dokal@kinomica.com.

PMID: 39293215
DOI: 10.1016/j.ebiom.2024.105316

Abstract

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.

Keywords: Acute myeloid leukaemia; Drug response prediction; Machine learning; Midostaurin plus chemotherapy; Phosphoproteomics; Precision medicine.