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Enregistrement W2906757730 · doi:10.1182/blood-2018-99-110470

Unsuccessful Cytogenetics and Outcomes in Acute Leukemia

2018· article· en· W2906757730 sur OpenAlexaff
Lauren Lee, Hélène Bruyèrè, Michael J. Barnett, Raewyn Broady, Donna L. Forrest, Alina S. Gerrie, Donna E. Hogge, Stephen H. Nantel, Sujaatha Narayanan, Maryse Power, David Sanford, Kevin Song, Heather J. Sutherland, Cynthia L. Toze, Jennifer White, Yasser Abou Mourad

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensVancouver General HospitalLeukemia & Lymphoma Society of Canada
Organismes subventionnairesnon disponible
Mots-clésCytogeneticsMedicineLeukemiaInternal medicineAcute leukemiaOncologyBone marrowMyeloid leukemiaKaryotypeChromosome abnormalityBiologyChromosome

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Cytogenetic features at diagnosis have significant independent prognostic impact in acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Failed or unsuccessful cytogenetics (UC) is estimated to occur in 10% of AML cases and 25-30% of ALL cases (Grimwade, 2010; Pullarkat, 2008). Previous studies suggest worse outcomes in AML patients (pts) with UC, similar to pts with unfavorable karyotype with lower response rates to induction chemotherapy and poor 5-year (yr) survival rates (Medeiros, 2014; Lazarevic, 2015). Hydroxyurea (HU) and steroids are often given urgently for cytoreduction prior to obtaining cytogenetics. The effects of such pretreatment on rates of UC have not been studied previously. In this study, we compared clinical outcomes of acute leukemia pts with UC versus successful cytogenetics (SC) and determined whether the use of HU or steroids affects cytogenetics success rates. Methods: All pts <70 yrs with de novo acute leukemia (AML or ALL) with available diagnostic bone marrow and cytogenetics testing seen at the Leukemia/Bone Marrow Transplant (BMT) Program of British Columbia between January 2010 and December 2016 were included. Pertinent information was reviewed from the program database. Cytogenetic failure was defined by the presence of <10 metaphases with a normal karyotype in the absence of a clinically actionable FISH abnormality. Pts with SC were risk stratified based on NCCN guideline criteria (NCCN, 2018). Baseline features of pts with SC and UC were compared using Chi-squared and two-tailed t-testing. Overall survival (OS) was calculated from the date of initial diagnosis to the date of death from any cause. Disease free survival (DFS) was calculated from the date of attaining first complete remission (CR1) to the date of relapse or death from any cause. Survival outcomes were estimated using the Kaplan-Meier method, with p values determined using the log rank test. A p < 0.05 was considered significant. Results: We identified 654 cases of acute leukemia (AML N=515; ALL N=139). Of these, 39 (6%) had UC (AML N=16, 3%; ALL N=23, 17%). There was no difference in age at diagnosis between groups (UC vs SC, 46 vs 51 yrs, p=0.08). AML pts with UC had higher initial white blood count (WBC) at diagnosis (71 vs 34, p<0.001). No effect of WBC was seen in ALL pts with UC versus SC. HU and/or steroid use prior to cytogenetics was similar between UC and SC groups in both AML and ALL pts (UC vs SC, HU: 8% vs 12%, p=0.61; steroids: 5% vs 1%, p=0.07. Therapy with intensive induction was also similar between groups (UC vs SC, 92% vs 90%; p=1.00). 137 (27%) of AML pts with SC had favorable risk disease, 182 (37%) intermediate, and 180 (36%) high risk. In ALL pts with SC, 32 (28%) had standard risk, and 84 (72%) had high risk disease. Follow up time in UC and SC groups was similar (32 vs 27m, p=0.18). CR rates in AML pts with UC was 81%, which was similar to pts with intermediate risk disease (84%, p=0.72) and better than CR rates of 39% observed in high risk pts (p=0.03). Compared to pts with SC, AML pts with UC had similar 5-yr OS and DFS as pts with intermediate risk disease (5-yr OS 25% vs 37%, p=0.45; 5-yr DFS 28% vs 28%, p=0.84), superior survival to high risk pts (5-yr OS 25% vs 17%, p=0.01; 5-yr DFS 28% vs 13%, p=0.036), and inferior survival to favorable risk pts (5-yr OS 25% vs 68%, p=0.07, 5-yr DFS 28% vs 52%, p=0.008), Figure 1. CR rates in ALL pts with UC was 83% and not significantly different than standard (94%, p=0.22) or high risk pts (86%, p=0.74). Compared to pts with SC, ALL pts with UC had similar 5-yr OS and DFS to pts with standard and high risk disease (UC vs standard vs high; 5-yr OS 58% vs 51% (p=0.93) vs 55% (p=0.85); 5-yr DFS 50% vs 37% (p=0.86) vs 36% (p=0.27)). Subgroup analysis of UC pts demonstrated no difference in survival in UC pts receiving BMT (N=12) vs no BMT (N=27). UC pts with high WBC >20 (N=14) had inferior survival compared to those with WBC ≤ 20 (N=25) (5-yr OS 60% vs 29%, p=0.025; 5-yr DFS 52% vs 12%, p=0.013). Conclusions: Unsuccessful cytogenetics was more frequently observed in ALL as compared to AML pts, though at lower rates than reported in previous studies. HU and steroid use had no effect on the rates of cytogenetic success. In comparison to previous reports, UC in AML was not associated with inferior outcomes, and survival was similar to intermediate risk pts. High initial WBC count was prognostic in pts with UC, and further studies are warranted to evaluate factors that affect outcomes in pts with UC. 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)0,001
score de la tête « metaresearch » (Gemma)0,003
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 seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,017
Tête enseignante GPT0,309
Écart entre enseignants0,292 · 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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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