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Enregistrement W4310103232 · doi:10.1182/blood-2022-169307

Umbrella Trial in Myeloid Malignancies: The Myelomatch National Clinical Trials Network Precision Medicine Initiative

2022· article· en· W4310103232 sur OpenAlexaff
Richard F. Little, Megan Othus, Sarit Assouline, Sherry S. Ansher, Ehab Atallah, R. Coleman Lindsley, Boris Freidlin, Steven D. Gore, Lyndsay N. Harris, Christopher S. Hourigan, S. Percy Ivy, Shahanawaz Jiwani, Erin Langan, Selina M. Luger, Laura C. Michaelis, Olatoyosi Odenike, David R. Patton, Miguel‐Angel Perales, Jerald P. Radich, Bhanu Ramineni, Jesse J. Salk, Patrick J. Stiff, Wendy Stock, Richard M. Stone, Geoffrey L. Uy, Paul Williams, Brent L. Wood, Katherine H Worthington, Laura M. Yee, Amer M. Zeidan, Jianqiao Zhang, Mark R. Litzow, Harry P. Erba

Notice bibliographique

RevueBlood · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensMcGill UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésClinical trialMedicinePrecision medicineOncologyInternal medicineIntensive care medicinePathology

Résumé

récupéré en direct d'OpenAlex

Introduction: To accelerate myeloid cancer therapeutics, the National Clinical Trials Network (NCTN) is launching the National Cancer Institute (NCI) Myeloid Malignancies Molecular Analysis for Therapy Choice (myeloMATCH) precision medicine clinical trial. Sponsored by NCI and conducted across the entire NCTN system with initial trials to be led by SWOG, Alliance, ECOG-ACRIN, and CCTG, the initiative leverages public-private partnerships with pharmaceutical industry and biotech companies to create an efficient regulatory model incorporating cross-company novel-novel combinations in trials for acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). The goal is to create a portfolio of rationally designed treatment substudies onto which patients sequentially enroll over their treatment journey. As patients transition to higher tiers with increasingly lower remaining tumor burden, the focus will be to target residual disease more precisely. Methods: Newly diagnosed patients are enrolled onto the Master Screening and Reassessment Protocol (MSRP) for baseline clinical and laboratory evaluation. Specimens are sent to the Molecular Diagnostics Network (MDNet) with a 72-hour turn-around for patient assignment to an initial treatment substudy via an integrated informatics system developed by the NCI Precision Medicine Analysis and Coordination Center (PMACC). Assignments will be based on algorithms adjusted for prevalence of co-mutations to enhance accrual of rare molecular subsets to specific targeted treatment trials. As shown in figure 1, there are 4 tiers and 5 clinical baskets. Tier-1 is for initial therapy grouped by MDS, younger AML, and older AML. These are typically randomized phase 2 studies testing sensitivity to novel drug combinations with measurable residual disease (MRD) assessment conducted centrally by MDNet. Subsequent therapy occurs in higher tiers. These assignments are made by MDNet/PMACC based on prior treatment substudy outcome. Flow cytometry and duplex sequencing will be employed in Tier-4 clinical trials that will target residual disease. Statistical designs will evaluate the clinical utility of the assays and biomarkers to determine if targeting residual disease confers clinical benefit. Planned activation is quarter 4 of 2022 with the MSPRP, 2-young adult tier-1 studies and 1 tier-2 study. These are testing combinations of azacitidine, venetoclax, CPX351, 7+3 for ELN defined high risk AML, standard risk AML, and in tier-2 the ability to "erase” residual disease after tier-1 treatment. Additional studies in development include agents for mutant TP53, KIT, FLT-3, NPM1, IDH 1/2, higher and lower-risk MDS and a study for reduced intensity transplant and maintenance to include efforts for diverse populations. Launch for these studies is planned for mid 2023. Tier-4 studies to target KIT, IDH, FLT3 and others are in discussion. Discussion: MyeloMATCH is the largest focused investment of infrastructure and researchers ever coordinated by the US Network Groups and NCI that follows patients from diagnosis thorough all treatment in a single disease area. The charge is to rapidly advance therapeutics in myeloid malignancies. MyeloMATCH is designed to efficiently screen and assign patients to precision treatment trials of promising therapeutic combinations. By using early endpoints to identify large activity signals, myeloMATCH will generate data with promising findings for definitive study. The clinical and laboratory data can be interrogated across the initiative to generate hypotheses for additional focused testing. Participants receiving their treatment journey through myeloMATCH will contribute to a unique clinically annotated database with specimens for "omics” serially collected from pre-treatment and through follow up. This will provide a national resource for understanding drug sensitivity and resistance, as well as clonal evolution. In this manner, myeloMATCH aims to reduce the time and investment in failed phase 3 studies, and instead aims to provide high-quality randomized trial data that will enhance selection of phase 3 priorities. We believe this new paradigm for the collaborative conduct of clinical trials may provide meaningful advances for patients with AML and MDS, mentor investigators, and accelerate drug development. Updates and specific treatment-trial designs will be discussed at the meeting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,040
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,209

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0400,018
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,002
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0180,006

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.

Tête enseignante Opus0,201
Tête enseignante GPT0,446
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai non randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2022
Routes d'admission1
Résumé présentoui

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