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Enregistrement W4417021048 · doi:10.1182/blood-2025-5780

Low skeletal muscle mass and cancer cachexia among patients with multiple myeloma undergoing CAR-T cell therapy

2025· article· en· W4417021048 sur OpenAlexaff
Samuel J. Yates, Abigail Sneider, Claire Wild, Ishan Roy, Kayla Beck, Ioanna Karras, Varun Akella, Nicholas Feinberg, Karteek Popuri, Mirza Faisal Beg, Peter A. Riedell, Mariam Nawas, Benjamin A. Derman

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensMemorial University of NewfoundlandSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaCachexiaCancerLymphomaSkeletal muscleDiffuse large B-cell lymphomaChimeric antigen receptorRituximab

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Chimeric Antigen Receptor T-cell therapy (CAR-T) has changed the treatment landscape of patients (pts) with relapsed or refractory Multiple Myeloma (MM). Despite improvements in management of CAR-T toxicities, including ICANS, CRS, and B-cell aplasia, non-relapse mortality (NRM) remains a concern. In newly diagnosed MM, low skeletal muscle mass (SMM) is common and associated with poor treatment outcomes. Low SMM is driven in part by cancer cachexia, physical inactivity, and an inflammatory state. Furthermore, many chemotherapies and supportive care medications (e.g. corticosteroid-induced myopathy) cause myocyte breakdown. To date, no data is available among patients with MM undergoing CAR-T therapy despite compelling data in lymphoma pts undergoing CAR-T (Valtis, Blood Advances, 2025 and Rejeski, Cancer Immunology Research, 2023). Current brain-to-vein times in CAR-T remain ~50 days and emerging therapeutics to improve cachexia and low SMM are being investigated (Groarke, NEJM, 2024); time and tools exists to potentially optimize low SMM. In this hypothesis generating retrospective study of pts with MM undergoing CAR-T, we hypothesized low SMM would be prevalent, associated with NRM, and correlate with markers of cancer cachexia. Methods: All MM pts age ≥18 years at the University of Chicago receiving B-cell maturation antigen (BCMA) CAR-T therapy with a PET-CT scan from within 60 days of therapy were included. SMM was evaluated at the 3rd lumbar spinal level then divided by the pt’s height to calculate skeletal muscle index (SMI); SMI<34.4 cm2/m2 (female) and SMI<45.4 cm2/m2 (male) defined low SMM (Cruz-Jentoft, The Lancet, 2019). Cachexia was clinically defined both by Fearon Criteria (Fearon, Lancet Oncology, 2011) and Weight Loss Grading Scale (WLGS) (Martin, JCO, 2014). The Data Analysis Facilitation Suite (Voronoi Health Analytics Inc) was utilized to obtain automated body composition measurements. Cachexia biomarkers (TNF-alpha, IL-6, IL-1 beta, Leptin, GDF-15, IGF-1, P-Selectin, TNFS14, CCL2-MCL1) (Burkhart, BJH, 2019) were assessed on biobanked serum collected prior to lymphodepleting chemotherapy. Results: 53 pts were evaluated. Median age was 67 years (range, 41-83). Pts were primarily White (75.5%) and male (51%). Median HCT-CI was 2 and the entire cohort was fit by ECOG PS (≤2). 51% of pts had at least one high-risk cytogenetic abnormality. The median number of prior lines of therapy was 5 (range, 3-6). The most common CAR T product administered was cilta-cel (60%) followed by ide-cel (17%). 23% received an investigational BCMA-directed CAR T. CRS was common (91%) and primarily grade 1 (66%) while ICANS was rare (15%). The best ORR for the whole cohort was 47/53 (89%) with a CR rate of 37/53 (70%). The median follow-up time was 13.3 months; there were 25 progression events and 18 deaths (4 without prior progression). One-year PFS and OS was 60% (95% CI 44-72%) and 73% (95% CI 57-83%), respectively. The prevalence of low SMI and cachexia (Fearon Criteria and WLGS) was 51% and 23%, respectively. There were no differences in demographics, prior lines of therapy, disease risk, or CAR-T products by SMI category though ferritin (1052 vs. 638; p=0.15) was numerically higher. Low SMI was not associated with CRS, ICANS, or response to CAR-T. Overall survival by SMI category at 6 months for normal vs low SMI (95% vs. 78%; p=0.35) did not reach the level of significance. In multivariable Cox models for OS at 6 months controlling for ECOG PS and high-risk cytogenetics, low SMI showed a notable effect size but was not statistically significant (Hazard Ratio=1.61; 95% CI 0.58-4.4; p=0.35). Four NRM events occurred with two in each SMI category. 21 pts had evaluable serum for cachexia biomarker analysis. Leptin was lower among those with cachexia (WLGS≥2 (1519 pg/mL) vs WLGS<2 (3710 pg/mL; p=0.07) and low muscle mass (low SMI (2427 pg/mL vs. 4202 pg/mL; p=0.7) though not statistically significant. Conclusions: Low SMM is prevalent for MM pts undergoing CAR-T, though its impact on early mortality remains unclear in this analysis. Low leptin levels may indicate these patients are in a semi starvation state leading to the development of low SMM. There was no link between previously established cachexia biomarkers with SMM in MM prior to CAR-T, potentially obfuscated by prolonged courses of corticosteroids received leading up to CAR-T. Further MM-specific investigation of cachexia biomarkers is needed.

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)0,001
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 enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,009
Tête enseignante GPT0,250
Écart entre enseignants0,241 · 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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