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Enregistrement W4417014418 · doi:10.1182/blood-2025-5773

Do chip mutations predict frailty and toxicity in transplant-eligible patients with multiple myeloma?

2025· article· en· W4417014418 sur OpenAlexaff
Steven Chun-Min Shih, Harjot Vohra, Sahar Khan, Salman Basrai, Esther Masih‐Khan, Anup J. Devasia, Donna Reece, Suzanne Trudel, Keith Stewart, Sita Bhella, Vishal Kukreti, Chloe Yang, Guido Lancman, Rodger E. Tiedemann, Anca Prica, Eugene Leung, Aaron D. Schimmer, Sagi Abelson, Christine Chen

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensOttawa HospitalOntario Institute for Cancer ResearchWindsor Regional HospitalAlberta Cancer FoundationPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésToxicityMultiple myelomaClinical trialProspective cohort studyComorbidityHematopoietic stem cell transplantationPerformance statusStem cell

Résumé

récupéré en direct d'OpenAlex

Abstract Background We have previously reported that current frailty tools have limited ability to predict treatment-related toxicity in transplant-eligible (TE) patients with multiple myeloma (MM) (Devasia, ASH 2022). Although clonal hematopoiesis of indeterminate potential (CHIP) mutations has been associated with more frailty and treatment-related toxicities in patients with newly diagnosed MM (Gelli, Scientific Reports 2024), their clinical relevance specifically in TE patients has yet to be elucidated. Therefore, we aimed to evaluate whether the presence of CHIP mutations predicts frailty and toxicity in patients preparing for autologous stem cell transplant (ASCT). Method We have two ongoing prospective trials in TE-MM patients at our centre. The “Frailty” study evaluates the utility of various frailty assessments prior to ASCT in predicting transplant-related toxicity and outcomes (Shih, IMS 2025). The ARCH-001 study evaluates the prevalence and evolution of CHIP mutations at different time points relevant to their ASCT (Khan, ASH 2024). Patients enrolled in both studies were selected for this correlative analysis. The presence of CHIP mutations prior to ASCT (ARCH-001) served as the predictor variable. The following variables extracted from Frailty study served as the outcome variables: 1) subjective measures of function (Rockwood Frailty, Karnofsky Performance [KPS], ECOG), 2) objective measures of fitness [hand grip strength, 6-min walk test, Timed Up and Go, 3) comorbidity indices (Charlson and HCT-CI), 4) organ function (eGFR, serum albumin, BNP, PFT, ECHO), 5) Post-ASCT acute grade ≥3 organ toxicities and mortality to Day 100, ICU use, time to engraftment, transfusion requirements and length of stay (LOS). Chi2or Fisher’s exact test was used as appropriate for categorical variables. T-test was used for continuous variables. All P values were 2 sided and statistically significant at P < 0.05. Statistical analysis was performed using STATA version 19. Results We identified 101 participants who were enrolled in both studies and had evaluable data. Of these, 38 (37.6%) had at least one CHIP mutation and 5 (5.0%) had two CHIP mutations. DNMT3A, ASXL1 and TET2 mutations were most common, with 30 (29.7%) patients having at least one of these. Pre-ASCT There was a greater proportion of patients with CHIP mutation in those age ≥65 than those age <65 (76.3% vs 23.7% respectively, P=0.025), but no difference in other demographics (sex, BMI), or pre-transplant parameters (first vs salvage transplant, melphalan dose or stem cell dose). The presence of CHIP mutations was not associated with increased all-grade anemia, leukopenia or thrombocytopenia prior to ASCT (P = 0.443, P = 0.204 and P = 0.259, respectively), or any of the frailty parameters (data will be presented). Post-ASCT There was no correlation between the presence of CHIP mutations and any of the acute grade ≥3 transplant-related toxicities. There were only 2 ICU admissions and 0 deaths by day+100. There was a trend towards more cardiac-related adverse events in patients with CHIP mutations compared to those without (21.1% vs 9.5% respectively), but this was not statistically significant (P = 0.057). There was also a trend towards more cumulative toxicity (defined as ≥2 toxicity events) in those with CHIP mutations compared to those without (50.0% vs.31.8% respectively), but this was not statistically significant either (P = 0.068). The mean number of days to neutrophil and platelet engraftment in all patients was 12.6 and 17.6, respectively. The presence of CHIP mutations was not associated with prolonging these (P = 0.395 and P = 0.202, respectively). The mean number of bags of red cells and platelets required for transfusion during ASCT in all patients was 0.5 (0-8) and 1.0 (0-9), respectively. The presence of CHIP mutations did not increase these either (P = 0.109 and P = 0.230). The mean LOS in those with vs without CHIP mutations was 22.1 and 17.9 days, respectively, but the difference was not statistically significant (P = 0.197). Conclusion The presence of CHIP mutations in TE-MM patients was not associated with increased frailty, acute grade ≥3 transplant-related toxicity or any short-term transplant-related outcomes. However, this was a highly selected population that tends to be younger and fitter. Continued follow-up is required to evaluate whether these CHIP mutations affect longer-term treatment-related efficacy, toxicity or survival outcomes in TE-MM patients.

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 enseignants0,001
Score d'incertitude au seuil0,005

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 ouverte0,0000,000
Intégrité de la recherche0,0010,000
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 Opus0,015
Tête enseignante GPT0,276
Écart entre enseignants0,261 · 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
Publié2025
Routes d'admission1
Résumé présentoui

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