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Predictive phosphoproteomic signatures for midostaurin plus chemotherapy response in FLT3 mutant positive acute myeloid leukaemia.

2021· article· en· W3169327621 on OpenAlexaff
Arran Dokal, Andrea Arruda, Ryan J. Smith, Calum Greenhalgh, Francesca Patella, Pedro Casado, Bela Wrench, Jane Theaker, Andrew Thompson, Mark D. Minden, John G. Gribben, David J. Britton, Pedro R. Cutillas

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMidostaurinMedicineMyeloid leukemiaBone marrowChemotherapyCancer researchOncologyInternal medicineSurvival analysis

Abstract

fetched live from OpenAlex

7019 Background: Midostaurin is approved for FLT3 mutant-positive (FLT3+) acute myeloid leukemia (AML), however efficacy has also been observed in a subpopulation of FLT3 mutant-negative AML, suggesting that FLT3 mutation is not the only determinant in conferring midostaurin sensitivity. We previously described a phosphoproteomic signature significantly elevated in primary AML blasts that responded to midostaurin ex vivo (Casado et al Leukaemia 2018). This signature includes phosphorylation sites on protein kinase C delta (a midostaurin off-target) and its substrate GSK3A. In this study, we tested whether these phospho-signatures could group FLT3+ patients based on clinical responses to midostaurin plus chemotherapy. Methods: We obtained FLT3+ bone marrow (BM) and peripheral blood (PB) diagnosis specimens (n=56 cases) from the Leukemia Tissue Bank at Princess Margaret Cancer Centre. These patients were treated with standard chemotherapy plus midostaurin. Phospho-signatures quantified using mass spectrometry were analysed with a classification machine learning algorithm to group patients based on response to treatment as a function of phospho-signature status. Other features (e.g. genetic mutations, HSC-transplant) were also analysed. Differential survival analysis was carried out with Kaplan-Meier and Log Rank test methods. Phospho-signatures for BM and PB samples were analysed independently. Results: A first ML model was developed based on the signature described in the Casado et al study. Patients positive for this signature exhibited a survival probability of 243 weeks, compared to 126 weeks in signature negative patients (averages by geometric mean, Log Rank p = 9.88e-05). As the patients in the current study received chemotherapy, in addition to midostaurin, we also identified a new signature consisting of 26 phospho-sites (model 2), which partially overlapped with the first model. Patients positive for model 2 signature showed a markedly longer survival time than negative patients (269 vs 76 weeks, Log Rank p = 1.30e-05 for PB and 241 vs 56, Log Rank p = 2.13e-09 for BM specimens, Table). No other features separated survival as clearly as model 2. Conclusions: We have identified phospho-signatures with the potential to further stratify FLT3+ AML for midostaurin treatment. The presence of PRKCD signalling components in signatures provides a rationale for midostaurin activity in sensitive cases. Analysis will also be performed on FLT3 mutant-negative cases to validate the signature in this group.[Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.442
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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