Sarcopenia Is a Clinically Relevant and Independent Predictor of Health Outcomes after Chimeric Antigen Receptor T-Cell Therapy for Lymphoma
Notice bibliographique
Résumé
Abstract Introduction: Chimeric antigen receptor T cell (CAR-T) therapy is an effective treatment for patients with relapsed/refractory diffuse large B cell lymphoma (DLBCL) or other B-cell lymphomas. However, the potent anti-lymphoma effect of CAR-T is balanced by the risk of acute toxicities, namely cytokine release syndrome (CRS) and Immune effector cell-Associated Neurotoxicity Syndrome (ICANS), as well as the variable length of progression-free survival (PFS) after CAR-T. Tools to better risk-stratify for adverse outcomes and to guide targeted interventions are lacking. Sarcopenia (loss of lean muscle mass) is an important cause of age-related functional decline in the general population and is an independent predictor of health outcomes in patients with solid and hematologic cancers, irrespective of age or comorbidity. Advances in software technology have facilitated the near real-time integration of body composition measurements into imaging studies obtained as part of standard clinical care. To date, there have been no studies to examine the association between sarcopenia and outcomes after CAR-T therapy. Methods: Using a retrospective cohort design, 280 consecutive patients with DLBCL or B-cell lymphoma, age ≥18y, and treated with CAR-T therapy between 2015 to 2020 at a single center were included in the study. This analysis was restricted to 226 (80.7%) patients with available computed tomography scans ≤60d from CAR-T. Skeletal muscle area was ascertained from abdominal scans using an automatic image analysis software (APACS; Voronoi Health Analytics; Vancouver, Canada); 3rd lumbar vertebra was used as a landmark because of its high correlation with whole-body muscle mass (J Clin Oncol 2016 34:1339); Figure. Trained researchers blinded to patient demographics and outcomes manually validated these measurements (SliceOmatic; Tomovision; Quebec, Canada). Skeletal muscle index (SMI) was calculated as the ratio of skeletal muscle area (cm 2) divided by height (m). Sarcopenia was defined according to sex-based cutoffs (lowest SMI tertile). Kaplan-Meier method was used to examine PFS at one-year. Multivariable regression was used to calculate the hazard ratio (HR) for PFS and odds ratio (OR) for toxicities with 95% confidence intervals (CI), adjusted for covariates (demographics [age, race/ethnicity], disease characteristics [largest lymph node diameter, blood lactate dehydrogenase], CAR-T product, ECOG performance status). Results: Median age at CAR-T was 63y (range: 18-84); 65.9% were male; 50.9% were non-Hispanic white; 8.8% had ECOG ≥2; 80.5% had a diagnosis of DLBCL; CAR-T products: axicabtagene ciloleucel (51.3%), lisocabtagene maraleucel (31.9%), other (16.8%); 46.9% were treated on a clinical trial; median residual lymph node diameter prior to CAR-T was 2.3cm (range: 0-17.2); 8.0% underwent HCT <1 year after CAR-T and follow-up was censored at HCT. Outcomes: 59.1% developed CRS (18.2% grade ≥2) and 30.1% developed ICANS (15.9% grade ≥2). In adjusted analyses, the odds of developing CRS or ICANS was 1.9-fold (CRS: 1.89 [95%CI: 1.02-3.5], ICANS: 1.93 [1.06-3.51]) higher among patients who were sarcopenic (reference: normal body composition). Average length of hospitalization was also longer (25.6d vs. 21.9d; p=0.037) among patients with sarcopenia. Survival: One-year PFS for the overall cohort was 50.1% (±4.2); PFS was significantly worse for patients who were sarcopenic compared to those with normal muscle mass (35.1% [±6.2] vs. 57.7% [±4.3], p=0.005; Figure). In adjusted analyses, sarcopenia was associated with inferior one-year PFS (HR=1.73 [CI: 1.12-2.68]) compared to those with normal muscle mass. Conclusion: Sarcopenia is an important and independent predictor of outcomes after CAR-T with potential downstream health-economic consequences, including increased burden of acute toxicities and prolonged length of hospitalization. Taken together, these data form the basis for real-time decision making prior to CAR-T (e.g. pre-habilitation, consideration of alternative treatments), or during/shortly after CAR-T (e.g. increased supportive care, rehabilitation), setting the stage for innovative strategies to improve outcomes after CAR-T therapy. Figure 1 Figure 1. Disclosures Artz: Radiology Partners: Other: Spouse has equity interest in Radiology Partners, a private radiology physician practice. Budde: Merck, Inc: Research Funding; Amgen: Research Funding; Astra Zeneca: Research Funding; Mustang Bio: Research Funding; Novartis: Consultancy; Gilead: Consultancy; Roche: Consultancy; Beigene: Consultancy. Herrera: Merck: Consultancy, Research Funding; Gilead Sciences: Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Karyopharm: Consultancy; Kite, a Gilead Company: Research Funding; Seagen: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Research Funding; Takeda: Consultancy; Tubulis: Consultancy; Genentech: Consultancy, Research Funding. Popplewell: Novartis: Other: Travel; Pfizer: Other: Travel; Hoffman La Roche: Other: Food. Shouse: Kite Pharmaceuticals: Speakers Bureau; Beigene Pharmaceuticals: Honoraria. Siddiqi: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Juno therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BeiGene: Other: DSM Member, Speakers Bureau; PCYC: Speakers Bureau; Jannsen: Speakers Bureau; Dava Oncology: Honoraria; ResearchToPractice: Honoraria. Forman: Lixte Biotechnology: Consultancy, Current holder of individual stocks in a privately-held company; Allogene: Consultancy; Mustang Bio: Consultancy, Current holder of individual stocks in a privately-held company.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».