Functional Ex-Vivo Testing Combined with Machine Learning Predicts Clinical Response to Azacitidine+Venetoclax in AML
Notice bibliographique
Résumé
Background: The combination of Venetoclax + hypomethylating agents such as Azacitidine (Ven/Aza) has revolutionized the treatment of Acute Myeloid Leukemia (AML). Ven/Aza is currently the standard of care for patients with newly diagnosed AML who are not candidates for intensive chemotherapy (IC), as well as a recurrent therapeutic choice for IC-relapsed/refractory patients. Despite widespread adoption, the predictive biomarkers of Ven/Aza response/resistance available today are not sufficiently conclusive (Griffioen et al. Cancers 2022). In fact, a current hot topic in the field is whether Ven/Aza can also become a frontline therapy for fit patients as an alternative to IC. Several worldwide studies are addressing this issue, including an ongoing clinical trial named “Survival of the Fittest” exploring this concept in the US (NCT04801797). An open question is whether functional ex-vivo testing could become a powerful actionable tool that overcomes limitations presented by genetic and molecular indicators. Aim & Methods: In this study, we utilized an innovative ex-vivo functional platform of Patient Micro Avatars (PMAs) developed at OncoPrecision (García et al, Blood 2022 140 (Supplement 1): 13027-13028) to assess the predictive power for Ven/Aza treatment in a cohort of 11 AML patients. Patient-Derived Cells (PDCs) were obtained from the patients' PBMCs and Ven/Aza activity was assessed using high-throughput flow cytometry. An in-house Machine Learning (ML) tool was developed to identify the optimal variables that predict clinical outcome (Complete Remission: Yes/No). Clusters of predictive Response/Non-Response were defined by analyzing a cohort of >60 previously profiled AML patients and by leveraging the two most predictive populational features generated by the platform that better correlate with clinical outcome from >12,000 combinations of variables. Results: Our findings revealed differential ex-vivo activity of Ven/Aza in AML, with remarkably contrasting outcomes that defined clearly separate clusters of response (Figure - Left Panel). Such ML clustering led to 80% Overall Predictive Power and 100% Positive Predictive Value, meaning that all the patients for whom the platform predicted response achieved complete clinical remission. Moreover, by comparing the performance of other targeted therapies such as Gilteritinib, as well as IC regimens such as AraC-FaraA-IDA, side by side we were able to identify patients for whom Ven/Aza is the optimal regimen (e.g., Patient #169), and others for whom IC or Gilteritinib emerge as more promising therapeutic options (e.g., Patient #170) (Figure - Right Panel). Conclusions: This work presents a groundbreaking approach to predict Aza/Ven responders versus non-responders in AML. Combining the phenotypic outcomes of functional ex-vivo testing with ML tools presents the opportunity to more comprehensively predict response to cutting-edge therapies such as Ven/Aza than traditional genetic testing. This potential is particularly compelling for therapeutic schemes not directly associated with the acquisition of genetic mutations. References: 1. Griffioen MS, ... Smit L. Targeting Acute Myeloid Leukemia with Venetoclax; Biomarkers for Sensitivity and Rationale for Venetoclax-Based Combination Therapies. Cancers (Basel). 2022 Jul 15;14(14):3456. doi: 10.3390/cancers14143456. PMID: 35884517; PMCID: PMC9318140. 2. Alejandra Garcia, ... Gastón Soria; Ex-Vivo Testing Using Patient Micro Avatars (PMAs) Predicts Clinical Response in Acute Leukemias. Blood 2022; 140 (Supplement 1): 13027-13028. doi: https://doi.org/10.1182/blood-2022-164847
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».