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Enregistrement W2968763813 · doi:10.1182/blood.v130.suppl_1.4276.4276

Spliceosome Mutations Are Associated with Frailty in Older Patients with Myeloid Malignancies

2017· article· en· W2968763813 sur OpenAlexaboutno aff
Marlise R. Luskin, Angel M. Cronin, Kevin Copson, WooRam Jung, Martha Wadleigh, Daniel J. DeAngelo, David P. Steensma, Jane A. Driver, Andrew A. Lane, R. Coleman Lindsley, Gregory A. Abel

Notice bibliographique

RevueBlood · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)MedicineMyeloid leukemiaGrip strengthWeight lossInternal medicineGerontologyOncologyObesityPhysical therapyBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Frailty is a state of depleted physiologic reserve that is common in older patients with myeloid malignancies. While it is associated with chemotherapy-related toxicity, poor response to therapy, and higher mortality (Sherman, 2013; Fega, 2015), little is known about its causes. Moreover, there are increasing data demonstrating that clonal somatic mutations may be related to atherosclerotic disease (Jaiswal, 2017), which is an important driver of frailty. In this context, we sought to determine whether there is an association between hematopoietic DNA mutations and frailty in patients with overt myeloid neoplasms, hypothesizing that both RAS/MAPK and spliceosome mutations would be associated with the frailty phenotype. Methods: Starting in February of 2015,all patients aged 75 and older who presented for initial consultation to the DFCI Adult Leukemia Clinic were approached for evaluation by a trained research assistant. In a 15-minute interview, the assistant used two methods to characterize the patient as frail, pre-frail, or robust. The first employs a cumulative deficit approach (Rockwood, 2007) including 26 questions adapted from the Yale Precipitating Events Project (Searle, 2008), as well as the delayed recall section of the Montreal Cognitive Assessment (Nasreddine, 2005), a clock-in-the-box test (Chester, 2011), a grip strength test (Gill, 2006), and a gait speed test (Studenski, 2011). The second is a phenotype approach (Fried, 2001), which gives equal weight to performance on the gait speed and grip strength tests, as well as three questions about weight loss, energy expenditure and self-reported exhaustion. Patients were assigned frailty designations using the worse of the two scores. 85% of patients approached agreed to participate. Routine assessment of recurrent genetic alterations was performed using the Rapid Heme Panel (RHP), a gene panel sequencing test that evaluates 95 genes recurrently mutated in hematologic malignancies (Kluk, 2016). Associations between mutations and frailty were assessed via tests for trend across frailty status categories as well as a Fisher's exact test comparing the proportion of patients with robust status (versus frail/pre-frail combined). Results: As of July 2017, 93 patients with MDS (n=39), MPN (n=18), MDS/MPN (n=18), or AML (n=18) had completed frailty assessment and disease genetic characterization. Patients were majority male (n=70, 75%) with median age of 78 years (range, 75-89). Hypertension (65%) and hyperlipidemia (42%) were common. 59% (n=55) of patients in the cohort were pre-frail, 23% were frail (n=21), and 18% (n=17) were robust. There were no significant differences in frailty status between AML patients and those with chronic myeloid disorders (MDS, MPN, MDS/MPN). Gene mutations present in at least 5% of the cohort included SRSF2 (25%), SF3B1 (12%) , U2AF1 (8%), TET2 (44%), ASXL1 (26%), DNMT3A (8%), JAK2 (24%), RUNX1 (19%), TP53 (13%), STAG2 (9%), IDH2 (8%), EZH2 (8%) , and CBL (6%). 47% of patients had one of 4 non-co-occurring splicing mutations (SRSF2, SF3B1, U2AF1, ZRSR2). 18% of patients had one or more mutations that cause aberrant activation of RAS/MAPK signaling (NRAS, KRAS, CBL, PTPN11, RIT1, FLT3, KIT) and are associated with leukemic transformation of MDS (Lindsley, 2015). Patient with any splicing mutations were less likely to be robust (5% robust, 73% pre-frail, 23% frail) compared to those without (31% robust, 47% pre-frail, 22% frail; P for trend = 0.05; P -exact for robust versus pre-frail/frail = 0.001). Patients with an SF3B1 mutation specifically were also less likely to be robust then patients without an SF3B1 mutation (P for trend=0.02, Figure). The association between presence of a splicing mutation and frailty persisted within subgroups of patients with chronic myeloid malignancies and patients with AML, although the latter did not reach statistical significance (p=0.10) in that small dataset. No other single mutation, number of mutations present, or presence of a mutation in the RAS/MAPK pathway was significantly associated with frailty. Conclusion: In our cohort, patients with splicing mutations were less likely to be robust than patients without splicing mutations. These findings suggest that molecular characterization of myeloid disease may identify patients at higher risk for frailty and complications associated with treatment. Download : Download high-res image (185KB) Download : Download full-size image Disclosures DeAngelo: Glycomimetics: Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy; Blueprint Medicines: Honoraria, Research Funding; Immunogen: Honoraria, Research Funding; Shire: Honoraria; Takeda Pharmaceuticals U.S.A., Inc.: Honoraria; Celgene: Research Funding; Incyte: Consultancy, Honoraria; Pfizer Inc.: Consultancy, Honoraria, Research Funding; ARIAD: Consultancy, Research Funding; Novartis Pharmaceuticals Corporation: Consultancy, Honoraria, Research Funding. Steensma: Celgene: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Janssen: Consultancy, Research Funding; H3 Biosciences: Consultancy; Incyte: Equity Ownership; Onconova: Consultancy; Takeda: Consultancy; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lane: Stemline Therapeutics: Research Funding; N-of-one: Consultancy. Lindsley: MedImmune: Research Funding; Jazz Pharmaceuticals: Consultancy; Takeda Pharmaceuticals: 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)0,000
score de la tête « metaresearch » (Gemma)0,002
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 seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,019
Tête enseignante GPT0,272
Écart entre enseignants0,252 · 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
Publié2017
Routes d'admission1
Résumé présentoui

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