Low skeletal muscle mass and cancer cachexia among patients with multiple myeloma undergoing CAR-T cell therapy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".