S124: PHOSPHOPROTEOMICS ACCURATELY PREDICTS RESPONSES TO MIDOSTAURIN PLUS CHEMOTHERAPY IN TWO INDEPENDENT COHORTS OF FLT3 MUTANT-POSITIVE ACUTE MYELOID LEUKAEMIA
Notice bibliographique
Résumé
Background: Midostaurin plus intensive chemotherapy (M+IC) is approved for FLT3 mutant-positive (FLT3-MP) acute myeloid leukaemia (AML). The presence of refractory/early relapse (R/ER) disease following M+IC treatment suggests the existence of FLT3-independent determinants of M+IC response (Stone et al. NEJM 2017). We have previously reported a phosphoproteomic signature significantly elevated in primary AML blasts that responded to midostaurin ex vivo (Casado et al., 2018, Leukemia). Aims: To build and test a phosphoproteomics-based model to predict M+IC response from FLT3-MP AML patient samples collected at diagnosis. Methods: We retrospectively analysed peripheral blood (PB, n=37) and/or bone marrow (BM, n=34) diagnosis samples of 47 FLT3-MP AML patients subsequently treated with M+IC (median age at diagnosis 61, range 19-79y) using liquid chromatography-tandem mass spectrometry and MS1-based peptide quantification for phosphoproteomics analysis. Data from patients with extreme response profiles were used for model building; the “good-responder” (GR) group had a disease-free survival (DFS)>24 months (n=20), whereas the R/ER group had DFS<6 months (n=14, including refractory patients). Multivariate analysis and machine learning were used to build a phosphoproteomic signature-based model capable of predicting M+IC response from diagnosis samples. The model was validated on an independent, blinded retrospective set of 13 diagnosis FLT3-MP AML samples (median age 60, age range 33-73y, 9xPB and 4xBM). Results: In this study, we identify a highly-predictive phosphoproteomic signature of M+IC response in FLT3-MP AML diagnosis samples, and test it on an independent, blinded patient cohort. First, multivariate analysis of phosphoproteomic data identified several biochemically different groups of AML cases (Fig. 1A), highlighting potential distinct mechanisms of drug response. GR1 and GR2 groups showed upregulation of DNA damage response (DDR), and downregulation of receptor tyrosine kinase (RTK) signalling, and either downregulation of immune response (IR) pathways (GR1), or upregulation of chromatin remodellers (GR2). GR3 showed upregulation of RTK signalling and IR pathways, and downregulation of DDR. A phosphoproteomic signature made of a subset of more than a hundred phosphopeptides discriminating between at least two of these four patient groups (R/ER, GR1-GR3) was used to build a response-prediction model. On the expanded training dataset, including patients with DFS between 6 months and 24 months (n=13), response stratification was achieved with log rank p<1x10-9 (not shown); median DFS was 17.7 weeks for the signature-negative patients, and was not reached for signature-positive patients. The model was then tested on a blinded independent cohort of 13 FLT3-MP patients (Fig. 1B and C), with those positive for our signature showing markedly increased survival than signature-negative patients (median DFS 0 weeks vs not reached, log-rank p<0.0008). The overall model accuracy, with “response” defined as DFS>6 months, was 100% for signature-negative samples (5/5) and 85% for signature-positive samples (6/7, data was censored before 6 months for one patient). Summary/Conclusion: Using MS1-based quantitation of phosphoproteomic data, we identified several potential mechanisms of sensitivity to M+IC. Accounting for response heterogeneity enabled the creation of a model based on a highly-predictive phosphoproteomic signature of M+IC response. In an independent blinded patient cohort of 13 FLT3-MP patients this model predicted M+IC response with 92% accuracy.Keywords: Survival prediction, Acute myeloid leukemia, flt3 inhibitor, Phosphorylation
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».