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Enregistrement W2986080996 · doi:10.1182/blood-2019-122311

Value of Flow Cytometry for MRD-Based Relapse Prediction in B-Cell Precursor Acute Lymphoblastic Leukemia in a Multi-Center Setting

2019· article· en· W2986080996 sur OpenAlexaff
Signe Modvig, Hans O. Madsen, Heléne Hallböök, Sanna Siitonen, Liv Osnes, Susanne Rosthøj, Anne Tierens, Vesa Juvonen, Helen Vålerhaugen, Magnus Hultdin, Mats Ehinger, Anna Porwit, Rėda Matuzevičienė, Mindaugas Stoškus, Millaray Marincevic, Aili Lilleorg, Mervi Taskinen, Nina Toft, Ólafur Gísli Jónsson, Kaie Pruunsild, Goda Vaitkevičienė, Kim Vettenranta, Bendik Lund, Jonas Abrahamsson, Kjeld Schmiegelow, Hanne Vibeke Marquart

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineMinimal residual diseaseInternal medicineOncologyRisk stratificationSingle CenterLeukemiaImmunology

Résumé

récupéré en direct d'OpenAlex

Background: PCR of rearranged antigen receptor genes is the method of choice for MRD quantification in ALL. Although FCM-MRD is faster and biologically more informative than PCR, the analysis requires a high level of training. The only larger published studies using FCM-MRD based stratification (Borowitz, Blood, 2008 and 2015) showed a clear association with clinical outcome in BCP-ALL. However, MRD analyses were centralized and these studies included only one MRD-based stratification (MRD levels at the end of induction). Patients and methods: We examined FCM-MRD as stratification tool in BCP-ALL at various timepoints in a large-scale multicenter (18 MRD centers) study. A total of 1487 patients with BCP-ALL (1298 children (younger than 18 years) and 189 adults (18-45 years) are included in the study and were treated according to the NOPHO ALL2008 protocol between July 2008 and February 2016. The median follow-up time for patients in first remission was 51 months (IQR 32-75). MRD was measured by FCM and/or real time quantitative PCR on days 15, 29 (end of induction) and 79 (for standard (SR) and intermediate risk (IR) patients) and prior to and after high risk blocks. A 6-colour FCM analysis including 3 standardized antibody combinations was used and performed in 18 laboratories. Patients were stratified by FCM-MRD, or by PCR-MRD if no FCM-MRD marker was available. End-of-induction MRD (cut-off 10-3) was used to stratify patients to standard risk (SR) vs intermediate risk (IR) or IR vs high risk consolidation therapy (in case of WBC > 100 x 109/L at diagnosis). Patients with MRD >=2.5x10-1 on day 15 were stratified to high risk block therapy. Patients with MRD >=5x10-2 on day 29 or day 79/post high risk-2 block MRD >=10-3 were stratified to HSCT. Primary outcomes were 5year event-free survival (5y EFS) and 5year cumulative incidence of relapse (5y CIR). Results: Only two patients (0.14% of total) had neither an informative FCM nor a PCR marker, and an informative FCM marker combination for MRD monitoring was identified in 96.2% of patients. There was a significant correlation between FCM- and PCR-MRD levels on day 15 (r=0.77, p<0.0001, n=153) and 29 (r=0.81, p<0.0001, n=140). Based on FCM-MRD only, the median MRD level on day 15, 29 and 79/post high risk-2 block was 5x10-3, 1.1x10-4, and below detection limit, respectively. Adults had significantly higher MRD levels at all time-points (p<0.0001 for day 15 and 29, p=0.0019 for day 79, Mann-Whitney). The 5y EFS was 86.1% (95% CI 84.1-88.1) with a 5y CIR of 9.5% (95% CI 7.8-11.3, n=1487). The day 29 FCM-MRD level was closely associated with clinical outcome and a higher hazard of relapse was seen independently for a FCM-MRD >=10-3 (hazard ratio (HR) 2.4, CI 1.6-3.7, p<0.0001), age>18 year (HR 3.0, CI 1.7-5.3, p<0.0001), WBC>=100 (HR 2.7, CI 1.6-4.6, p=0.0001), and B-other (HR 2.1, CI 1.2-3.5, p=0.0052) or high risk B-ALL cytogenetic aberration (rearranged KMT2A/iAMPchr21/hypodiploid) (HR 3.2, CI 1.6-6.1, p=0.0006) (multivariate cause-specific Cox regression, n=1328). Patients with a day 79 FCM-MRD >=10-4 and <10-3 had a significantly higher CIR (22.1%, CI 10.8-33.5%, n=68) compared to FCM-MRD <10-4 (7.5%, CI 2.1-12.8%, n=110) or undetectable (6.3%, CI 4.5-8.2%, n=999, p=0.0087 for FCM-MRD >=10-4 and <10-3vs <10-4 or undetectable). After adjusting for WBC, age, and the day 29 FCM-MRD level, a day 79 FCM-MRD >=10-4 and <10-3 was still significantly associated with a worse 5y CIR for non-transplanted patients (HR 2.3, CI 1.19-4.36, p=0.012 compared to undetectable FCM-MRD, n=1171). Patients with day 15 FCM-MRD <10-3 had a significantly better 5y EFS (92.0%, CI 89.2-95.0%) and CIR (3.9%, CI 1.7-6.1%, n=432) than patients with FCM-MRD >=10-3 and <2.5x10-1, who had a 5y EFS of 85.5% (CI 82.7-88.3%, p=0.0016, n=837) and a 3-fold higher 5y CIR (11.0%, CI 8.4-13.5%, p<0.0001, n=432). Among patients with day 15 FCM-MRD<10-3, the relapse incidence was comparable for patients with FCM-MRD 10-4 - <10-3 and below 10-4 (CIR 3.6, CI 0.5-6.7 vs. CIR 4.1, CI 1.0-7.2, p=0.83, n=432). Conclusion: FCM-MRD performed in a multi-center setting is a clinically useful method for disease monitoring and MRD-based treatment stratification in BCP-ALL. Moreover, FCM-MRD is a reliable indicator of outcome in BCP-ALL independently of other key risk factors. Residual disease >=10-4 and <10-3 at day 79 in SR/IR patients not allocated to HSCT further identifies patients with a high risk of relapse. Disclosures No relevant conflicts of interest to declare.

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)0,005
score de la tête « metaresearch » (Gemma)0,006
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,006
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
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 Opus0,012
Tête enseignante GPT0,269
Écart entre enseignants0,258 · 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

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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