Proteomics and Phospho-Proteomics Reveal Predictive Signatures of Response and Mechanisms of Resistance to Midostaurin Plus Chemotherapy in FLT3 Mutant Positive Acute Myeloid Leukemia
Notice bibliographique
Résumé
Abstract 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 conferring midostaurin sensitivity. Casado et al previously described phosphoprotein signatures significantly associated with ex vivo responses to midostaurin in primary AML blasts (Casado et al Leukaemia 2018). In the current study, we tested whether our signatures could group FLT3+ patients based on clinical responses to midostaurin plus chemotherapy. Methods: FLT3+ bone marrow (BM) and peripheral blood (PB) specimens were collected at diagnosis, post-treatment and relapse (n=54 cases) from the Leukemia Tissue Bank at Princess Margaret Cancer Centre. All patients in this study were treated with standard chemotherapy plus midostaurin. Protein/phosphoprotein-signatures for BM and PB samples were analysed independently. Case-studies with multiple post-treatment time-points or relapse events following second line treatments were also analysed. Peptides (proteomics) and enriched phosphopeptides (phosphoproteomics) were quantified using liquid chromatography - tandem mass spectrometry. A classification machine learning (ML) algorithm was trained to group patients based on response to treatment as a function of protein/phosphoprotein-signature status. Other features (e.g. genetic mutations, HSC-transplant) were also analysed. Differential survival analysis between patient groups was carried out with Kaplan-Meier and Log Rank test methods. Pathways upregulated in post-treatment or relapse specimens, particularly from those cases that responded poorly to chemo + midostaurin (i.e. early relapse / refractory disease) were investigated using enrichment statistical methods including kinase-substrate enrichment analysis (KSEA) and gene ontology analysis and identified as potential mechanisms of resistance. Statistical significance of enrichment was determined using parametric methods and p-values adjusted for multiple testing using the Benjamini-Hochberg method. Results: ML models were developed based on the ex-vivo phosphoproteomics signatures described in the Casado et al study, from which we trained a predictive model (model 1). Patients positive for model 1 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 identified a new phosphoproteomic signature consisting of 26 phospho-sites which partially overlapped with the ex-vivo signature. Patients positive for this new phosphoproteomic 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). A proteomic signature was also identified in the current study. Positive patients showed a longer survival time than negative patients (330 vs 173 weeks, Log Rank p = 5.0e-04 for PB and 460 vs 156, Log Rank p = 5.2e-06 for BM specimens, Table), however this was less differentiating than the phosphoproteomic signature. Pathways upregulated in post-treatment or relapse specimens from early relapse or refractory cases were associated with molecular functions such as cell proliferation, anti-apoptosis, non-homologous end-joining, transcriptional regulation, spliceosome and cytoskeleton remodelling. Conclusions: We have identified protein and phosphoprotein signatures with the potential to further stratify AML for midostaurin treatment. Phosphoproteomic signatures differentiated according to response better than the proteomic signatures. Pathways upregulated in relapse/refractory cases may have a role in resistance and this will be determined in follow up studies. Analysis will also be performed on FLT3 mutant-negative cases to validate the signatures and elucidate mechanisms of resistance in this group. Disclosures Veiga Nobre: Kinomica Ltd.: Current Employment. Minden: Astellas: Consultancy. Gribben: Janssen: Honoraria, Research Funding; AZ: Honoraria, Research Funding; Abbvie: Honoraria; BMS: Honoraria; Gilead/Kite: Honoraria; Morphosys: Honoraria; Novartis: Honoraria; Takeda: Honoraria; TG Therapeutics: Honoraria. Britton: Kinomica Ltd.: Current Employment, Current equity holder in publicly-traded company.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,000 |
| 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 source (Gemma direct ou Codex distillé), 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 ».