MétaCan
Menu
Retour à la cohorte
Enregistrement W3213022568 · doi:10.1182/blood-2021-149018

Characteristics and Outcomes of Adolescent and Young Adult (AYA) Patients with Myelodysplastic Syndrome (MDS) and Chronic Myelomonocytic Leukemia (CMML): A Single-Center Retrospective Analysis

2021· article· en· W3213022568 sur OpenAlexaboutno aff
Shehab Mohamed, Tareq Abuasba, Kelly S. Chien, Guillermo Montalban‐Bravo, Faezeh Darbaniyan, Sherry Pierce, Kelly A. Soltysiak, Fadi Haddad, David McCall, Branko Cuglievan, Elias Jabbour, Naval Daver, Tapan M. Kadia, Naveen Pemmaraju, Hagop M. Kantarjian, Guillermo Garcia‐Manero

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineChronic myelomonocytic leukemiaMyelodysplastic syndromesInternal medicineRetrospective cohort studyInternational Prognostic Scoring SystemCancerPediatricsBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Myelodysplastic syndrome (MDS) is mainly a disease of the elderly, with a median age of 72 years. There is little information regarding Adolescent and Young Adult (AYA) Patients with Myelodysplastic syndrome (MDS). AYA cancer patients are defined as those patients ages between 15-39 years according to NCCN guidelines. This retrospective study describes the general characteristics, cytogenetics, mutational profiles, treatments, and outcomes of AYA with MDS Diagnosis. Methodology We analyzed the clinical database of a single tertiary care center for patients with MDS ages between 18- 39 years from January 2012 through December 2020. We used 18 years as age cut-off, and not 15, due to the structure of our cancer center. Results In this retrospective study, 65 patients were identified. The median age was 30 (18-39) years, with female sex predominance (n=37) (57%). Baseline laboratory findings: median hemoglobin (HgB) was 9.45gm/dL (6.2-14.8), white blood cells (WBC) (3.4x10e9/L [0.3-136.9], platelets 63,000 (5,000-479,000), bone marrow blast 4% [0-17], IPSS was low in 11 patients (17%), intermediate- 1 in 23 (35%), intermediate- 2 16(25%) and high 8 (12%). 58 patients (89%) had MDS and seven (11%) had CMML. Twenty patients (30.7%) had a previous history of other cancers, with sarcomas being the most frequent with 6 cases (9.2%). Therapy-related MDS (t-MDS) was observed in 18 patients (27.6%). Ten patients (15.3%) had bone marrow failure syndrome, with GATA2 syndrome being the most frequent. Fanconi anemia and Schwachman-Diamond Syndrome was documented in two patients respectively. The most recurrent cytogenetics alterations were diploid in 20 patients (30.7%), followed by complex in 11 (16.9%). The most frequent mutations were RUNX1 (somatic)15%, followed by DNMT3A, TP53, NRAS, GATA2 and TET2, as shown in the Figure 1. Hypomethylating agents (HMAs) were the most frequent first line treatment used in 16 patients (24.6%). Forty-three patients (66%) underwent an allogeneic bone marrow transplant with a median OS (95% CI) of 27 months (9-45). While for the group of patients who didn't receive transplant, it was 21 months (7-69) (p=0.19) vs patients who didn't receive transplant. Allogeneic transplantation in TP53-mutated patients resulted in a Median OS (95% CI) of 21 months (12-65). Patients who progressed into AML had an inferior median OS (95% CI) of 21 months (12-65) for vs 28 months (11-47) for those who did not progressed to AML(p=0.025). In multivariate analysis expression of RUNX1 and NOTCH1, was associated with inferior outcomes (p-value=0.035, 0.004 respectively) (Figure 2,3 and 4) Conclusion In our cohort, MDS occurred as part of marrow failure syndrome or consequence of therapy t-MDS. Somatic RUNX1 was the most frequent mutation in AYA group with MDS. RUNX1, NOTCH1 and Tp53 mutated patients had worse outcome. Most patients underwent bone marrow transplant Figure 1 Figure 1. Disclosures Jabbour: Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, Genentech: Research Funding. Daver: Abbvie: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Glycomimetics: Research Funding; Novimmune: Research Funding; Amgen: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Hanmi: Research Funding; Sevier: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Trovagene: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; ImmunoGen: Consultancy, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Kadia: Liberum: Consultancy; Novartis: Consultancy; Pfizer: Consultancy, Other; Jazz: Consultancy; BMS: Other: Grant/research support; Amgen: Other: Grant/research support; Pulmotech: Other; Genentech: Consultancy, Other: Grant/research support; Aglos: Consultancy; Sanofi-Aventis: Consultancy; Genfleet: Other; Astellas: Other; Ascentage: Other; AstraZeneca: Other; AbbVie: Consultancy, Other: Grant/research support; Dalichi Sankyo: Consultancy; Cure: Speakers Bureau; Cellonkos: Other. Pemmaraju: LFB Biotechnologies: Consultancy; Incyte: Consultancy; Protagonist Therapeutics, Inc.: Consultancy; Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; CareDx, Inc.: Consultancy; DAVA Oncology: Consultancy; Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Cellectis S.A. ADR: Other, Research Funding; Celgene Corporation: Consultancy; Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding; Roche Diagnostics: Consultancy; Daiichi Sankyo, Inc.: Other, Research Funding; Affymetrix: Consultancy, Research Funding; Plexxicon: Other, Research Funding; ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees; Samus: Other, Research Funding; ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees; Aptitude Health: Consultancy; Springer Science + Business Media: Other; MustangBio: Consultancy, Other; Sager Strong Foundation: Other; HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees; Dan's House of Hope: Membership on an entity's Board of Directors or advisory committees; Clearview Healthcare Partners: Consultancy; Blueprint Medicines: Consultancy; Bristol-Myers Squibb Co.: Consultancy; ImmunoGen, Inc: Consultancy; Pacylex Pharmaceuticals: Consultancy. Kantarjian: NOVA Research: Honoraria; KAHR Medical Ltd: Honoraria; Precision Biosciences: Honoraria; BMS: Research Funding; Amgen: Honoraria, Research Funding; Jazz: Research Funding; Ascentage: Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; Ipsen Pharmaceuticals: Honoraria; Astra Zeneca: Honoraria; Astellas Health: Honoraria; Aptitude Health: Honoraria; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Taiho Pharmaceutical Canada: 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 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)0,001
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 enseignants0,002
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
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 Opus0,007
Tête enseignante GPT0,228
Écart entre enseignants0,220 · 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
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetAcute Myeloid Leukemia ResearchTravaux en français237 207