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Enregistrement W2984707601 · doi:10.1182/blood-2019-123904

A 10-Color Flow Cytometry Panel for Both Diagnosis and Minimal Residual Disease Measurement in Chronic Lymphocytic Leukemia

2019· article· en· W2984707601 sur OpenAlexaffabout
Alexandre Bazinet, Ryan N. Rys, Claudia M. Wever, Amadou Barry, Celia M.T. Greenwood, Christian D. Young, Alma Yolanda Arce Mendoza, Ida LaPorta, Sylvain Gimmig, François Mercier, Nathalie A. Johnson

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensMcGill UniversityBecton Dickinson (Canada)Jewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésChronic lymphocytic leukemiaMinimal residual diseaseMedicineImmunophenotypingCD20CD5CytometryAntibodyImmunologyAntigenLeukemiaPathologyFlow cytometry

Résumé

récupéré en direct d'OpenAlex

Background: Chronic lymphocytic leukemia (CLL) has a classic immunophenotype, consisting of light chain restriction, CD5+, CD19+, dim CD20, CD23+, CD43+, CD200+, CD10- and CD79b-. This distinguishes it from normal B cells and other lymphoproliferative disorders (LPDs). Antibodies targeting these antigens are included in two 8-color flow cytometry panels developed by the Euroflow consortium for the work up of B cell LPDs. Combining these antibodies into one 10-color panel would be more cost-effective. Furthermore, new CLL therapies can induce deep remissions, creating an increasing demand to measure minimal residual disease (MRD), defined as having over 1 residual leukemic cell per 10,000 leukocytes (10-4). The current international standardized approach for measuring MRD established by the European Research Initiative on CLL (ERIC) uses a panel of antibodies targeting CD3, CD5, CD19, CD20, CD22, CD43, CD79b and CD81. However, these antibody-fluorochrome combinations are different than those used by the Euroflow diagnostic panels. Thus laboratories considering implementing MRD testing would need to purchase antibodies for 3 different panels (2 diagnostic and 1 MRD). We expanded the Euroflow 8-color lymphocyte screening tube (LST) to include CD200 and CD23, such that CLL can be detected in one 10-color tube, at levels as low as 0.01%. The goal of this study was to determine the potential cost savings in implementing this new panel and to determine if it is sensitive enough to detect MRD. Methods: We calculated the number of samples analyzed with our modified 10-color LST tube (mLST1, obtained lyophilized) from April 2018 to March 2019 to rule out an LPD and the number of antibody aliquots saved using this approach compared to the standard 2-tube Euroflow method. We also created a version of the above-mentioned panel (mLST2) using liquid antibodies to increase the generalizability of our results, substituting CD38 with CD43 to see if this improved MRD detection (see panels below). For MRD testing, we used CLL samples from 24 different patients to produce 60 MRD samples at various concentrations of leukemic cells. Samples were prepared by spiking CLL cells into suspensions of normal leukocytes at approximate concentrations of 0.1%, 0.01% and 0.001%. Each sample was aliquoted and stained with the three panels: ERIC, mLST1 and mLST2. Data was acquired using a BD FACSCanto II or a BD FACSAria Fusion and analysed using BD FACSDiva software. CLL cells were identified based on differential expression of key markers and MRD was calculated as the number of CLL cells/total leukocytes. MRD positivity was defined as ≥ 0.01%. Agreement between the panels was assessed using the Bland-Altman plot method. We also calculated the percentage agreement between the panels in identifying MRD positivity. Results: In 1 year, mLST1 was performed on 474 samples, of these 220 had an LPD and 123 (56%) had a classic CLL phenotype, obviating the need for further testing. This resulted in the net savings of 476 antibody aliquots. For MRD assessment, differential expression of CD5 and CD20 were the most significant contributors in distinguishing CLL from normal B cells using the mLST1 and mLST2. We identified one CLL case with an atypical immunophenotype (dim CD5, bright CD20) which proved difficult to gate using a mLST panel. There was agreement in MRD results obtained with the mLST panels and the ERIC panel. For values above the limit of quantification, the 95% limit of agreement was ±0.3369 log for the ERIC vs mLST1 comparison and ±0.3485 log for the ERIC vs mLST2 comparison. Thus, variability in MRD levels between the panels was less than 2-fold the majority of the time, which we considered clinically acceptable as MRD is measured on an exponential scale. The ERIC panel and the mLST1 had 88.3% agreement in distinguishing MRD-positive versus MRD-negative samples. Agreement was 93.3% between the ERIC panel and the mLST2. Conclusions: Using a modified 10-color LST panel for both diagnosis and MRD measurement of CLL is feasible. The advantages are increased familiarity with the antibodies and potential cost savings, making MRD accessible to more cytometry laboratories. Atypical CLL cases without the usual CD5 positivity and dim CD20 are very difficult to gate using an LST panel. In these cases, the ERIC panel is clearly superior as CD22, CD79b, CD81 and CD43 can still provide separation between the malignant and normal lymphocytes. Disclosures Bazinet: BD Biosciences: Other: Provided a significant amount of the antibodies used in this project free of cost.. Wever:Teva Canada Innovation: Employment. Gimmig:BD Biosciences: Employment. Johnson:Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Merck: Consultancy, Honoraria; Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; BMS: Consultancy, Honoraria; Seattle Genetics: Honoraria.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,425
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,046
Tête enseignante GPT0,289
Écart entre enseignants0,243 · 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 tête enseignante, pas un consensus.

Devis d'étudeEssai 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

Citations6
Publié2019
Routes d'admission2
Résumé présentoui

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