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Enregistrement W2606617462 · doi:10.1182/blood.v128.22.2907.2907

Experience with MRD Testing in B- ALL By Flow Cytometry Does Not Prevent Interpretative Discordance

2016· article· en· W2606617462 sur OpenAlexaff
Michael Keeney, Brent L. Wood, Benjamin D. Hedley, Joseph A. DiGiuseppe, Maryalice Stetler‐Stevenson, Elisabeth Paietta, Gerard Lozanski, Adam C. Seegmiller, Bruce Greig, Aaron C. Shaver, Lata Mukundan, Howard R. Higley, Caroline C. Sigman, Gary Kelloff, J. Milburn Jessup, Michael J. Borowitz

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensLondon Health Sciences Centre
Organismes subventionnairesnon disponible
Mots-clésMinimal residual diseaseMedicineClinical trialFlow cytometryOncologyInternal medicineLeukemiaImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Minimal residual disease (MRD) in B lymphoblastic leukemia (B-ALL) as measured by flow cytometry is well-established as an important prognostic factor; Its presence is used to adjust treatment in most therapeutic protocols in children , while the lack of a standardized assay has hampered the introduction of flow cytometric MRD in adult ALL trials. On the other hand, measuring MRD has become part of the standard of care even for patients not on clinical trials. Although flow cytometric analysis of MRD in B-ALL has been well standardized in clinical trials of the Children's Oncology Group (COG) in North America (Borowitz et al Blood 2015;126:964), there are no data on performance characteristics of this assay within routine clinical labs. Methods: As part of an ongoing effort to standardize and decentralize ALL MRD measurement, list-mode data from post-induction marrows were distributed from one COG reference lab to 7 different clinical flow cytometry labs self-identified as having experience with ALL MRD. All labs were provided with the COG protocol used for MRD analysis along with a template illustrating recommended gating strategies, and formulas for calculating MRD burden. List-mode files of pre-treatment B-ALL samples analyzed with the standard COG B-ALL MRD antibody panel were distributed for comparison. In the first rounds, list-mode files from 15 samples were distributed to the 7 labs. Samples included those with and without MRD as assayed in the reference lab. Samples were selected to include normal B-cell precursors (hematogones) or MRD that had undergone phenotypic change with therapy. Some samples had artifacts that could potentially mimic small MRD populations. To improve performance, educational sessions were implemented, and 10 more list-mode file samples were distributed in a second round of challenges. Results: There was considerable dispersion of MRD results among the 7 labs that analyzed the list-mode files (Fig 1A). Although high levels of MRD were uniformly recognized, several labs misclassified normal B-cell precursors and/or mischaracterized small artifacts as MRD. Moreover, among samples correctly identified as positive, quantitative differences in MRD levels from those reported by the reference lab were seen. Among 95 total challenges, the overall discordance rate was 24%. This included 11 false positives, 7 false negatives, and an additional 5 quantitatively discordant cases among positives (defined as outside +/- 0.5 log of the reference lab value). In the second round, positive and negative samples, as well as those with normal precursors were included, though these samples contained fewer artifacts than those of the first round. Performance improved considerably (Fig 1B); out of 70 challenges, there were 5 false positives and 1 false negative (8.6% discordance), and no cases were quantitatively discordant. Four of the 6 deviations occurred in a single lab. Three samples with hematogones were still misclassified as MRD. Conclusions: Despite the provision of a standardized analysis protocol, even experienced laboratories have difficulty with B-ALL MRD analysis by flow cytometry. Some of these difficulties can be overcome with education, but even with education recognition of hematogones still remains a challenge for some labs. Extrapolating these results to other laboratories with less experience indicates the need for caution in migrating MRD testing from centralized reference laboratories, and suggests that implementation of MRD testing as part of routine clinical management of B-ALL patients in a manner similar to that of routine flow cytometric classification of leukemia may require additional training and resources. Figure 1 Figure 1. Disclosures Wood: Pfizer: Honoraria, Other: Laboratory Services Agreement; Juno: Other: Laboratory Services Agreement; Amgen: Honoraria, Other: Laboratory Services Agreement; Seattle Genetics: Honoraria, Other: Laboratory Services Agreement. Lozanski:Stemline Therapeutics Inc.: Research Funding; Beckman Coulter: Research Funding; Boehringer Ingelheim: Research Funding; Genentech: Research Funding. Mukundan:CCS Associates: Employment. Higley:CCS Associates: Employment. Sigman:CCS Associates: Equity Ownership. Borowitz:BD Biosciences: Research Funding; Medimmune: Research Funding; Bristol Myers Squibb: Research Funding; HTG Molecular: Consultancy.

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)0,108
score de la tête « metaresearch » (Gemma)0,178
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,108
Score d'incertitude au seuil0,571

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1080,178
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0020,005
Communication savante0,0050,004
Science ouverte0,0060,005
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0080,005

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 Opus0,020
Tête enseignante GPT0,304
Écart entre enseignants0,284 · 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'étudeObservationnel
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

Citations2
Publié2016
Routes d'admission1
Résumé présentoui

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