Association Between Pretreatment Skeletal Muscle and Outcomes After CAR T-Cell Therapy

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Kyuwan Lee Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Aleksi Iukuridze Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Tianhui He Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Alysia Bosworth Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Lanie Lindenfeld Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Jennifer Berano Teh Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Meagan Echevarria Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Sophia Albanese Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Liezl Atencio Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Rusha Bhandari Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California
Department of Pediatrics, City of Hope Comprehensive Cancer Center, Duarte, California

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F. Lennie Wong Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California

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Andrew S. Artz Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Tanya Siddiqi Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Liana Nikolaenko Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Jasmine Zain Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Matthew Mei Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Geoffrey Shouse Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Leslie L. Popplewell Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Alex F. Herrera Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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L. Elizabeth Budde Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Stephen J. Forman Department of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California

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Saro H. Armenian Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California
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Background: The purpose of this study was to examine the association between baseline skeletal muscle measurements, acute toxicity (immune effector cell–associated neurotoxicity syndrome [ICANS], cytokine release syndrome), and treatment efficacy in patients undergoing CAR T-cell therapy for B-lineage lymphoma. Patients and Methods: Skeletal muscle measurements were obtained from automated CT measurements in 226 consecutive patients who received CAR T-cell therapy between 2015 and 2021. The Kaplan-Meier method was used to examine progression-free survival (PFS) and overall survival (OS) at 1-year. Multivariable regression was used to calculate the hazard ratio (HR) with 95% confidence intervals, adjusted for covariates. Results: The median age of the cohort was 63.1 years (range, 18.5–82.4 years), and most patients were male (66%) and had primary refractory disease (58%). Patients with abnormally low skeletal muscle at baseline were at greater risk of ICANS (HR, 1.74; 95% CI, 1.05–2.87) and had longer length of hospitalization (mean 27.7 vs 22.9 days; P<.05) compared with those with normal muscle mass. Abnormal skeletal muscle was independently associated with risk of disease progression (HR, 1.70; 95% CI, 1.11–2.57) and worse survival (HR, 2.44; 95% CI, 1.49–4.00) at 1 year compared with normal skeletal muscle. Individuals who had abnormal skeletal muscle and high lactate dehydrogenase (LDH) levels at baseline had poor 1-year PFS (17%) and OS (12%) compared with those with normal skeletal muscle and LDH levels (72% and 82%, respectively; P<.001). Patients who had abnormal skeletal muscle and LDH levels had a 5-fold risk (HR, 5.34; 95% CI, 2.97–9.62) of disease progression and a 10-fold risk (HR, 9.73; 95% CI, 4.81–19.70) of death (reference: normal skeletal muscle, normal LDH), independent of prior lines of therapy, extent of residual disease at time of CAR T-cell therapy, functional status, or product. Conclusions: This information can be used for risk stratification prior to CAR T-cell therapy or to implement prehabilitation and nutritional optimization before lymphodepletion as well as thereafter. These efforts will be complementary to ongoing efforts toward sustained efficacy after CAR T-cell therapy.

Background

CAR T-cell therapy is an effective treatment for patients with relapsed/refractory large B-cell lymphoma (LBCL) or other B-lineage lymphomas.15 However, CAR T-cell therapy has been associated with potentially life-threatening toxicities, such as cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS).14 Additionally, there is considerable prognostic variability after CAR T-cell therapy, including length of progression-free survival (PFS) or overall survival (OS) due to the heterogeneous features of disease biology and broad range of clinical comorbid conditions in the relapsed/refractory setting.1,68 In this context, identifying appropriate prognostic biomarkers of response to CAR T-cell therapy is of major clinical importance.

Abnormal skeletal muscle measurements at baseline such as low muscle mass and resultant fat infiltration (myosteatosis) have been associated with adverse cancer-related outcomes and all-cause mortality in patients with solid and hematologic malignancies,912 including in those undergoing hematopoietic cell transplantation (HCT).1315 For patients undergoing CAR T-cell therapy, abnormally low skeletal muscle prior to CAR T-cell infusion may be of particular clinical relevance, given the increasing recognition of the role of muscle as a regulator of immune response.5,16,17 Skeletal muscle cells can modify immune-mediated responses through muscle cell–derived cytokines (myokines), cell surface molecules, and cell-to-cell interactions, which alter T-cell homeostasis.5,17 In this context, poor muscle health may adversely impact outcomes after CAR T-cell therapy.18 There is a paucity of information on the impact of abnormal skeletal muscle measurements at baseline on outcomes after CAR T-cell therapy, including the interaction between these measurements and established prognostic factors, such as elevated lactate dehydrogenase (LDH) levels at baseline.1921 Previous studies have noted worse outcomes after CAR T-cell therapy in patients with poor performance status and elevated LDH levels at baseline.1921 As such, understanding the relationship between objective measurements of skeletal muscle health and biomarkers such as LDH level is an important step toward establishing clinical utility.

Recent advances in software technology have facilitated the near real-time integration of body muscle and fat measurements into CT imaging obtained for standard clinical care, setting the stage for risk stratification for primary or secondary prevention.2225 The overall aims of this study were to describe the association between skeletal muscle measurements obtained from pretreatment CT images and postinfusion acute toxicities (eg, CRS, ICANS) as well as 1-year PFS and OS after CAR T-cell therapy, and to identify individuals at highest risk for worse outcomes after treatment.

Patients and Methods

Patient Cohort and Definitions

This was a retrospective cohort study of patients who underwent CAR T-cell therapy as adults (age ≥18 years) for large B-cell (LBCL) or B-lineage lymphoma at City of Hope (COH) between February 2015 and February 2021. The institutional review board at COH approved the protocol and written informed consent was obtained from study participants in accordance with the Declaration of Helsinki. Medical records were abstracted for patient demographics (age at CAR T-cell therapy, sex, race/ethnicity), diagnosis (LBCL, low-grade lymphoma, transformed low-grade lymphoma, other), number of prior lines of therapy, disease status26 (complete remission, stable disease, partial response, progressive disease), prelymphodepletion largest lesion diameter, prelymphodepletion (<2 days) blood LDH level (normal: <271 U/L; abnormal: ≥271 U/L), health status (Karnofsky performance status score), and CAR T-cell therapy–related variables (lymphodepletion regimen, treatment on clinical trial, T-cell product).

Outcomes of interest included length of hospitalization, ICU admission during the initial hospitalization for CAR T-cell infusion, development of CRS or ICANS, disease progression, and cause-specific mortality. CRS and ICANS were graded according to established consensus criteria.27 Toxicity monitoring and management were conducted per institutional guidelines. Disease response was obtained from chart review, and vital status was obtained from the National Death Index and institutional medical records.

Body Composition at CAR T-Cell Therapy

Body composition was assessed from existent CT scans for pre–CAR T-cell disease response. CT scans completed ≤60 days prior to CAR T-cell infusion were deemed to accurately represent patients’ body composition and were selected for analysis. Three adjacent cross-sectional images were used to quantify muscle area at the level of the third lumbar vertebra (L3).13 Muscle and adipose tissues were quantified with Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS),22 which is a commercially available software that automatically segments skeletal muscle and adipose tissue. To maintain quality assurance, ABACS segmentation for each patient was manually reviewed by members of the research team (A. Iukuridze and L. Atencio), who were blinded to patient characteristics and outcomes. Muscle area was normalized for height (cm2/m2)28 and reported as lumbar skeletal muscle index (SMI). Skeletal muscle radiodensity (SMD), typically used to determine myosteatosis, was measured in Hounsfield units (HU). Skeletal muscle gauge (SMG), which combines skeletal muscle index and density (SMI × SMD), with arbitrary units (AU),2931 was also determined for all participants. Due to a lack of well-established definitions for adiposity and skeletal muscle measures, we used a priori–determined lowest sex-specific tertile cutoffs for abnormal SMI (<35 cm2/m2 [female]; <46 cm2/m2 [male]), SMD (<25.7 HU [female]; <27.7 HU [male]), and SMG (<934 AU [female]; <1,203 AU [male]). Abnormal skeletal muscle was defined as having an abnormally low SMG, because the index represents a composite measure (quantity and quality) of skeletal muscle health,2931 including in patients undergoing HCT.25 Individuals were considered to have abnormal visceral adipose tissue (VAT) according to sex-based VAT tertile cutoffs (>100 cm2 [female]; >168 cm2 [male]).

Statistical Analysis

Patient, disease, and CAR T-cell characteristics were summarized with median and range for continuous variables, and frequencies and percentages for categorical variables. Length of hospitalization, rates of ICU admission, CRS, and ICANS by body composition parameters (abnormal skeletal muscle, visceral adiposity) were compared using the chi-square test for categorical or 2-sided Student t tests for continuous variables. Univariable Cox proportional hazards regression was used to characterize the association between patient, disease, and CAR T-cell–related risk factors and 1-year PFS or OS. For PFS, time was calculated from CAR T-cell infusion to relapse/progression, start of conditioning for HCT, or last contact, whichever occurred first. For OS, time was calculated from CAR T-cell infusion to date of death or last contact, censored at 1-year. The Kaplan-Meier (KM) method was used to examine the association between selected variables and PFS and OS, and log-rank tests were used to compare the KM curves.

Variables with P value ≤.10 in univariable analyses for PFS and OS were included in the multivariable model. Backward stepwise elimination was used to remove nonsignificant variables one at a time, starting with the least significant with P>.05, and the model re-estimated. This was repeated until no more variables could be removed. For each outcome (PFS, OS), 2 separate multivariable regression models were created. Model 1 comprised skeletal muscle (normal [ref], abnormal), age at CAR T-cell therapy (continuous), LDH (normal [ref], abnormal), CAR T-cell product (categorical), and largest residual lesion diameter (continuous). Next, we examined the effect of having abnormal skeletal muscle and/or elevated prelymphodepletion LDH, given the well-established prognostic association between LDH and disease response. Model 2 included the following: combined skeletal muscle + LDH variable (normal skeletal muscle, normal LDH level [ref]; abnormal skeletal muscle, normal LDH level; normal skeletal muscle loss, abnormal LDH level; abnormal skeletal muscle, abnormal LDH level), age at CAR T-cell therapy, CAR T-cell product, and largest residual lesion diameter. Variables included in model 1 were also included in the multivariable analysis examining the association between abnormal skeletal muscle and ICANS. All statistical analyses were 2-sided, and a P<.05 was considered statistically significant in the final multivariable models; SPSS Statistics, version 27 (IBM Corp) was used for the analyses.

Results

Patient Characteristics

Of the 280 patients who underwent CAR T-cell therapy for LBCL or B-lineage lymphoma, 54 were excluded because abdominal CT images were obtained >60 days prior to CAR T-cell therapy. Clinical characteristics of the 226 patients (80.7% of the overall cohort) included in the study are detailed in Table 1. Median time from CT scan to CAR T-cell infusion was 10 days (range, 0–60 days), and 190 (84.1%) patients had a CT scan performed within 30 days of CAR T-cell therapy. Before apheresis, 91 (40%) had received >3 lines of therapy, including 39 (17%) who had undergone autologous HCT. Median age was 63.1 years (range, 18.5–82.4 years), and most patients were male (66%), non-Hispanic White (51%), and being treated for de novo LBCL (64%). All patients underwent fludarabine and cyclophosphamide-based lymphodepletion. Median prelymphodepletion LDH was 205 U/L (range, 98–5,693 U/L), and 74 (33%) had an abnormal value (≥271 U/L). Disease status was progressive disease in 172 (76%) patients, followed by stable disease in 42 (19%), partial response in 10 (4%), and complete remission in 2 (1%); 58% had primary refractory disease. Median diameter of the largest residual lesion was 2.3 cm (range, 0–17.2 cm). CAR T-cell products were axicabtagene ciloleucel (51%), lisocabtagene maraleucel (32%), and tisagenlecleucel (17%); 47% of patients were treated on a clinical trial.

Table 1.

Patient Characteristics

Table 1.

The median follow-up time after CAR T-cell infusion was 8.1 months (range, 0.2–62.6 months). CRS of any grade was observed in 141 (62%) patients, with 39 (17%) having grade ≥2 CRS. ICANS was observed in 68 (30%) patients, including 36 (16%) who had grade ≥2 ICANS. Overall, 90 (40%) patients received IL-6 receptor blockade with tocilizumab and 13 (6%) were treated with corticosteroids. Among the 144 patients with available radiographic disease response evaluation on day 28 (±7 days), 57 (37%) and 73 (47%) patients attained a complete remission and partial response, respectively. The 1-year PFS and OS rates for the overall cohort were 50.3% (±3.6%) and 63.5% (±3.6%), respectively; relapse or disease progression accounted for 82% of deaths within the first year. In unadjusted analyses, prelymphodepletion LDH level, residual lesion diameter, CAR T-cell product, and abnormal skeletal muscle were each associated with disease progression and all-cause mortality within 1 year of infusion (Table 2).

Table 2.

Univariate Analysis: Risk Factors for Poor Outcomes After CAR T-Cell Therapy

Table 2.

Health Outcomes by Body Composition Measures

Average length of hospitalization was statistically significantly longer for patients who had abnormal skeletal muscle compared with those who did not (mean [SD], 27.8 [16.6] vs 22.9 [17.9] days; P=.047) (supplemental eTable 1, available with this article at JNCCN.org). Patients with abnormal skeletal muscle also had a greater likelihood of developing any ICANS (38.7% vs 25.8%; P=.048), corresponding to a 1.7-fold risk (HR, 1.74; 95% CI, 1.05–2.87) in the multivariable model. On the other hand, there were no statistically significant differences in ICU admission or CRS rates, including CRS severity, between those with and without abnormal skeletal muscle. Adiposity measures such as VAT were not associated with any of the outcomes examined.

Patients with abnormal skeletal muscle had significantly worse 1-year PFS (35.2% [±6.0%] vs 58.0% [±4.4%]; P=.002) and OS (43.8% [±6.4%] vs 73.8% [±4.0%]; P<.014) compared with those with normal skeletal muscle (Figure 1A, B; Table 3). In adjusted analyses, abnormal skeletal muscle was associated with 1.7-fold (HR, 1.70; 95% CI, 1.11–2.57) risk of disease progression and a >2-fold (HR, 2.44; 95% CI, 1.49–4.00) risk of death within the first year compared with normal skeletal muscle.

Figure 1.
Figure 1.

Kaplan-Meier plots of (A) PFS rates and (B) OS rates within 1 year of CAR T-cell therapy according to skeletal muscle status at baseline.

Abbreviations: OS, overall survival; PFS, progression-free survival.

Citation: Journal of the National Comprehensive Cancer Network 21, 4; 10.6004/jnccn.2022.7100

Table 3.

Cumulative Incidence and Risk of Worse Outcomes After CAR T-Cell Therapy

Table 3.

Among patients with abnormal skeletal muscle, we identified a subgroup of patients with abnormal blood LDH level (n=31; 13.7% of the overall cohort) who had especially poor outcomes. In these patients, 1-year PFS and OS were 17.0% (±7.5%) and 11.7% (±7.5%), respectively. On the other hand, patients with normal LDH level and normal skeletal muscle (n=108; 47.8% of the cohort) had the highest PFS (72.4% [±4.7%]) and OS rates (82.3% [±4.3%]) (Figure 2A, B; Table 3). In adjusted analyses, having both abnormal skeletal muscle and abnormal LDH level was associated with a >5-fold (HR, 5.34; 95% CI, 2.97–9.62) risk of disease progression and a nearly 10-fold (HR, 9.73; 95% CI, 4.81–19.70) risk of death by 1 year (ref: normal LDH level and skeletal muscle) (Table 3).

Figure 2.
Figure 2.

Kaplan-Meier plots of (A) PFS rates and (B) OS rates within 1 year of CAR T-cell therapy according to baseline muscle status and LDH level.

Abbreviations: LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival.

Citation: Journal of the National Comprehensive Cancer Network 21, 4; 10.6004/jnccn.2022.7100

Discussion

In this contemporary cohort of patients who underwent CAR T-cell therapy for B-cell lymphoma as adults, we found significant associations between abnormal skeletal muscle at baseline and clinically relevant outcomes such as ICANS and prolonged length of hospitalization. After adjusting for well-established modifiers of disease response such as extent of residual disease or treatment intensity of CAR T-cell therapy, patients with abnormal skeletal muscle had a nearly 2-fold risk of disease progression and worse OS compared with those with normal skeletal muscle. Importantly, we identified a subgroup of patients with abnormal skeletal muscle and abnormal LDH level at baseline in whom OS was 12% at 1 year, which is in stark contrast to the sizeable proportion of patients with normal LDH level and normal skeletal muscle, for whom OS exceeded 80%. Taken together, the findings from this study speak to the need for more comprehensive strategies to optimize risk stratification prior to consideration of CAR T-cell therapy, and the need to explore alternative management approaches in the subset of patients likely to have acute treatment-related toxicity and poor disease response.

Body composition measures for the current study were acquired from CT images that were obtained as part of pretreatment clinical evaluation, a strategy that has been successfully used in other populations.32 To date, studies that have linked these CT-based measurements to outcomes after cancer treatment have largely been in patients with solid malignancies,912,33 including those with gastrointestinal cancers, because of the association between nutritional deficiencies and availability of abdominal imaging. We recently reported on the association between pretreatment skeletal muscle health and increased risk of acute toxicities in patients undergoing HCT for hematologic malignancies, including a higher risk of all-cause mortality.13,14 Others have reported on the association between skeletal muscle loss and worse outcomes after conventional therapy in adults with newly diagnosed B-cell malignancies.34 To our knowledge, our study is the first to highlight the association between abnormal skeletal muscle and adverse outcomes in patients undergoing CAR T-cell therapy, a population that will continue to grow in the foreseeable future. We leveraged advances in software technologies, including use of automated body composition measurements, to determine muscle loss in our patients.35 This may provide a platform for rapid real-time integration of this information into decision-making in the clinical setting, prior to the start of CAR T-cell therapy.

The mechanism by which patients undergoing CAR T-cell therapy develop abnormal skeletal muscle is likely multifactorial given the number of modifiers of muscle health in these patients, including lifestyle changes (eg, nutritional imbalance, physical inactivity) and comorbid health conditions that emerge after systemic antineoplastic therapy.36,37 Relevant to CAR T-cell therapy, skeletal muscle cells can regulate immune function through myokines such as IL-6, IL-7, and IL-15 that modulate CD8+ T-cell homeostasis and promote proliferation of naïve T cells and B cells.5,1618 Studies in older nononcology populations have highlighted the bidirectionality of skeletal muscle and the immune system.5,16,17 On the one hand, chronic low-grade inflammation contributes to muscle catabolism via pleiotropic mechanisms mediated by the inflammatory secrotome,5,38 whereas skeletal muscle loss can contribute to insufficient myokine signaling and impaired regenerative capacities of immune cells with time.5,1618 These observations speak to the importance of muscle health in response to CAR T-cell therapy, and highlight the need for additional mechanistic and longitudinal studies to interrogate the association between muscle and the immune system vis-à-vis disease response after CAR T-cell treatment.

Separate from disease response, we also found a significant association between baseline abnormal skeletal muscle and ICANS, which may have contributed to longer hospitalization in these patients. On the other hand, we did not see a difference in the rate or severity of CRS, including tocilizumab use between the 2 groups. Although previous reports have considered CRS and neurotoxicity in aggregate,39,40 it is increasingly recognized that CRS and neurotoxicity may occur exclusive of one another, with potentially different underlying mechanisms.41 A recent systematic review evaluating risk factors for CAR T-cell–related neurotoxicity highlighted the association between older patient age at treatment as well as baseline blood biomarkers and risk of ICANS.42 In the current study, having baseline abnormal skeletal muscle was associated with a nearly 2-fold risk of ICANS, independent of age and blood biomarkers such as LDH level. In the absence of available functional assessments, it is possible that the abnormalities in skeletal muscle seen in our patients reflected baseline physiologic frailty, a condition characterized by the inability to manage acute stressors associated with treatment.43,44 Complementary assessment of physiologic reserves in patients with muscle loss prior to CAR T-cell therapy may allow for risk-based resource allocation (eg, inpatient vs outpatient toxicity monitoring) and consideration of early interventions such as prophylactic corticosteroid administration to mitigate ICANS-associated acute toxicity.45

The strengths of the current study are the large and ethnically diverse patient population included in our analyses, the consideration of well-established patient and disease-related prognostic risk factors in our multivariable models, and inclusion of a broad range of B-cell diagnoses and CAR T-cell products that speak to the generalizability of our findings. We acknowledge that the CT images used in this study were not initially intended for measuring muscle or adipose tissue, and for some patients we relied on the CT component of the PET/CT scan instead of higher-resolution CT. However, previous studies have consistently shown excellent correlation between CT-based abdominal muscle and adiposity measurements and whole-body composition,34,35 as well as robust concordance between high-resolution CT and PET/CT-based imaging.14,46 Given the lack of well-established definitions for adiposity and skeletal muscle measures, clinicians may need to contextualize their interpretation of body composition measures according to the populations of interest, because the thresholds used to define abnormal skeletal muscle or high adiposity in the current study may not be applicable to other patient populations. Moreover, the findings from this study should be considered as preliminary, due to the smaller sample sizes in some of the combined categories, such as abnormal skeletal muscle and LDH level, and the reliance on a single-institution cohort, which may impact generalizability. Prospective studies are needed to validate our findings and to examine the utility of integrating more time-intensive clinical assessments (eg, short physical performance battery, geriatric assessment)47 prior to CAR T-cell therapy in the setting of more readily accessible automated body composition measurements such as those used in the current study.

Conclusions

We found a strong association between skeletal muscle health and a range of outcomes in patients undergoing CAR T-cell therapy, including the morbidity associated with ICANS and disease response. These associations speak to the multidimensionality of information obtained from readily available skeletal muscle assessments. Ultimately, the findings from the current study may facilitate the development of targeted interventions to improve outcomes, given the long latency between apheresis and autologous CAR T-cell delivery. This may include prehabilitation and nutritional optimization prior to the start of lymphodepletion as well as tailored medical approaches shortly thereafter. These efforts will be complementary to ongoing efforts toward sustained efficacy after CAR T-cell therapy.

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    Vercellino L, Di Blasi R, Kanoun S, et al. Predictive factors of early progression after CAR T-cell therapy in relapsed/refractory diffuse large B-cell lymphoma. Blood Adv 2020;4:56075615.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Popuri K, Cobzas D, Esfandiari N, et al. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging 2016;35:512520.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Shachar SS, Deal AM, Weinberg M, et al. Skeletal muscle measures as predictors of toxicity, hospitalization, and survival in patients with metastatic breast cancer receiving taxane-based chemotherapy. Clin Cancer Res 2017;23:658665.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Muresan BT, Sánchez Juan C, Artero A, et al. Diagnosis of pre-sarcopenia from a single selectional crosscut at C3 region, using CT scans before radiotherapy. Nutr Hosp 2019;36:11011108.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Bhandari R, Berano Teh J, He T, et al. Association between body composition and development of glucose intolerance after allogeneic hematopoietic cell transplantation. Cancer Epidemiol Biomarkers Prev 2022;31:20042010.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol 2014;32:30593068.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Lee DW, Santomasso BD, Locke FL, et al. ASTCT consensus grading for cytokine release syndrome and neurologic toxicity associated with immune effector cells. Biol Blood Marrow Transplant 2019;25:625638.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Martin L, Birdsell L, Macdonald N, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 2013;31:15391547.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Weinberg MS, Shachar SS, Muss HB, et al. Beyond sarcopenia: characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population. Breast J 2018;24:278284.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Williams GR, Deal AM, Muss HB, et al. Frailty and skeletal muscle in older adults with cancer. J Geriatr Oncol 2018;9:6873.

  • 31.

    Williams GR, Deal AM, Muss HB, et al. Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia? Oncotarget 2017;8:3365833665.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Amini B, Boyle SP, Boutin RD, et al. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019;74:16711678.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Franco Valle K, Lubner MG, Pickhardt PJ. Computed tomography assessment of sarcopenic myosteatosis for predicting overall survival in colorectal carcinoma: systematic review. J Comput Assist Tomogr 2022;46:157162.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Albano D, Dondi F, Ravanelli M, et al. Prognostic role of “radiological” sarcopenia in lymphoma: a systematic review. Clin Lymphoma Myeloma Leuk 2022;22:e340349.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Cespedes Feliciano EM, Popuri K, Cobzas D, et al. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. J Cachexia Sarcopenia Muscle 2020;11:12581269.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Price S, Kim Y. Body composition impacts hematopoietic stem cell transplant outcomes in both autologous and allogeneic transplants: a systematic review. Nutr Cancer 2022;74:27312747.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    McGovern J, Dolan RD, Horgan PG, et al. Computed tomography-defined low skeletal muscle index and density in cancer patients: observations from a systematic review. J Cachexia Sarcopenia Muscle 2021;12:14081417.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Marzetti E, Carter CS, Wohlgemuth SE, et al. Changes in IL-15 expression and death-receptor apoptotic signaling in rat gastrocnemius muscle with aging and life-long calorie restriction. Mech Ageing Dev 2009;130:272280.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Acharya UH, Dhawale T, Yun S, et al. Management of cytokine release syndrome and neurotoxicity in chimeric antigen receptor (CAR) T cell therapy. Expert Rev Hematol 2019;12:195205.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Hay KA. Cytokine release syndrome and neurotoxicity after CD19 chimeric antigen receptor-modified (CAR-) T cell therapy. Br J Haematol 2018;183:364374.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Garcia Borrega J, Gödel P, Rüger MA, et al. In the eye of the storm: immune-mediated toxicities associated with CAR-T cell therapy. HemaSphere 2019;3:e191.

  • 42.

    Grant SJ, Grimshaw AA, Silberstein J, et al. Clinical presentation, risk factors, and outcomes of immune effector cell-associated neurotoxicity syndrome following chimeric antigen receptor T cell therapy: a systematic review. Transplant Cell Ther 2022;28:294302.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Bertschi D, Waskowski J, Schilling M, et al. Methods of assessing frailty in the critically ill: a systematic review of the current literature. Gerontology 2022;68:13211349.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Muscedere J, Waters B, Varambally A, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med 2017;43:11051122.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Oluwole OO, Bouabdallah K, Muñoz J, et al. Prophylactic corticosteroid use in patients receiving axicabtagene ciloleucel for large B-cell lymphoma. Br J Haematol 2021;194:690700.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Albano D, Camoni L, Rinaldi R, et al. Comparison between skeletal muscle and adipose tissue measurements with high-dose CT and low-dose attenuation correction CT of 18F-FDG PET/CT in elderly Hodgkin lymphoma patients: a two-centre validation. Br J Radiol 2021;94:20200672.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Nakano J, Fukushima T, Tanaka T, et al. Physical function predicts mortality in patients with cancer: a systematic review and meta-analysis of observational studies. Support Care Cancer 2021;29:56235634.

    • PubMed
    • Search Google Scholar
    • Export Citation

Submitted August 18, 2022; final revision received October 25, 2022; accepted for publication November 21, 2022.

Previous presentation: These data were presented in part at the 65th ASH Annual Meeting and Exposition; December 11–13, 2021; Atlanta, Georgia. Blood 2021;138(Suppl 1):Abstract 2502.

Author contributions: Study concept and design: Lee, Armenian. Data curation: He, Bosworth, Lindenfeld, Teh, Echevarria, Albanese, Atencio, Bhandari, Wong. Data analysis and interpretation: He, Wong, Armenian. Data interpretation: All authors. Manuscript writing—original draft: Lee, Armenian. Manuscript writing—review and editing: All authors

Disclosures: Dr. Artz has disclosed having an immediate family member owning stock/ownership interest in Radiology Partners. Dr. Nikolaenko has disclosed being employed by and having stock/ownership interest in Kite Pharma. Dr. Mei has disclosed receiving grant/research support from Bristol-Myers Squibb, BeiGene, and Incyte; serving on an advisory board for Novartis, SeaGen, CTI, Janssen, and EUSA; and serving on a speakers’ bureau for Morphosys. Dr. Shouse has disclosed serving on a speakers’ bureau for Kite Pharma. Dr. Herrera has disclosed serving as a consultant for Bristol-Myers Squibb, Genentech, Merck, Seattle Genetics, AstraZeneca, Karyopharm, ADC Therapeutics, Takeda, Tubulis, Regeneron, Genmab, Pfizer, Caribou, Adicet Bio, and AbbVie. Dr. Budde has disclosed receiving grant/research support from Merck and Amgen; serving as a principal investigator for Mustang Bio; and serving on an advisory board for AstraZeneca, Roche, Genentech, and Kite Pharma. The remaining authors have disclosed that they have not received any financial considerations from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This study was supported, in part, by grants from the Leukemia and Lymphoma Society Scholar Award for Clinical Research (2315-17; S.H. Armenian) and the National Cancer Institute of the National Institutes of Health under award number R01CAHL150069 (S.H. Armenian).

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. None of the funders had any role in the conduct of the study; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript.

Correspondence: Saro H. Armenian, DO, MPH, Department of Population Sciences, City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010. Email: sarmenian@coh.org

Supplementary Materials

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  • Figure 1.

    Kaplan-Meier plots of (A) PFS rates and (B) OS rates within 1 year of CAR T-cell therapy according to skeletal muscle status at baseline.

    Abbreviations: OS, overall survival; PFS, progression-free survival.

  • Figure 2.

    Kaplan-Meier plots of (A) PFS rates and (B) OS rates within 1 year of CAR T-cell therapy according to baseline muscle status and LDH level.

    Abbreviations: LDH, lactate dehydrogenase; OS, overall survival; PFS, progression-free survival.

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    Gauthier J, Gazeau N, Hirayama AV, et al. Impact of CD19 CAR T-cell product type on outcomes in relapsed or refractory aggressive B-NHL. Blood 2022;139:37223731.

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    • Search Google Scholar
    • Export Citation
  • 22.

    Popuri K, Cobzas D, Esfandiari N, et al. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging 2016;35:512520.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Shachar SS, Deal AM, Weinberg M, et al. Skeletal muscle measures as predictors of toxicity, hospitalization, and survival in patients with metastatic breast cancer receiving taxane-based chemotherapy. Clin Cancer Res 2017;23:658665.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Muresan BT, Sánchez Juan C, Artero A, et al. Diagnosis of pre-sarcopenia from a single selectional crosscut at C3 region, using CT scans before radiotherapy. Nutr Hosp 2019;36:11011108.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Bhandari R, Berano Teh J, He T, et al. Association between body composition and development of glucose intolerance after allogeneic hematopoietic cell transplantation. Cancer Epidemiol Biomarkers Prev 2022;31:20042010.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol 2014;32:30593068.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Lee DW, Santomasso BD, Locke FL, et al. ASTCT consensus grading for cytokine release syndrome and neurologic toxicity associated with immune effector cells. Biol Blood Marrow Transplant 2019;25:625638.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Martin L, Birdsell L, Macdonald N, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 2013;31:15391547.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Weinberg MS, Shachar SS, Muss HB, et al. Beyond sarcopenia: characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population. Breast J 2018;24:278284.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Williams GR, Deal AM, Muss HB, et al. Frailty and skeletal muscle in older adults with cancer. J Geriatr Oncol 2018;9:6873.

  • 31.

    Williams GR, Deal AM, Muss HB, et al. Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia? Oncotarget 2017;8:3365833665.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Amini B, Boyle SP, Boutin RD, et al. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019;74:16711678.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Franco Valle K, Lubner MG, Pickhardt PJ. Computed tomography assessment of sarcopenic myosteatosis for predicting overall survival in colorectal carcinoma: systematic review. J Comput Assist Tomogr 2022;46:157162.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Albano D, Dondi F, Ravanelli M, et al. Prognostic role of “radiological” sarcopenia in lymphoma: a systematic review. Clin Lymphoma Myeloma Leuk 2022;22:e340349.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Cespedes Feliciano EM, Popuri K, Cobzas D, et al. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. J Cachexia Sarcopenia Muscle 2020;11:12581269.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Price S, Kim Y. Body composition impacts hematopoietic stem cell transplant outcomes in both autologous and allogeneic transplants: a systematic review. Nutr Cancer 2022;74:27312747.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    McGovern J, Dolan RD, Horgan PG, et al. Computed tomography-defined low skeletal muscle index and density in cancer patients: observations from a systematic review. J Cachexia Sarcopenia Muscle 2021;12:14081417.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Marzetti E, Carter CS, Wohlgemuth SE, et al. Changes in IL-15 expression and death-receptor apoptotic signaling in rat gastrocnemius muscle with aging and life-long calorie restriction. Mech Ageing Dev 2009;130:272280.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Acharya UH, Dhawale T, Yun S, et al. Management of cytokine release syndrome and neurotoxicity in chimeric antigen receptor (CAR) T cell therapy. Expert Rev Hematol 2019;12:195205.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Hay KA. Cytokine release syndrome and neurotoxicity after CD19 chimeric antigen receptor-modified (CAR-) T cell therapy. Br J Haematol 2018;183:364374.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Garcia Borrega J, Gödel P, Rüger MA, et al. In the eye of the storm: immune-mediated toxicities associated with CAR-T cell therapy. HemaSphere 2019;3:e191.

  • 42.

    Grant SJ, Grimshaw AA, Silberstein J, et al. Clinical presentation, risk factors, and outcomes of immune effector cell-associated neurotoxicity syndrome following chimeric antigen receptor T cell therapy: a systematic review. Transplant Cell Ther 2022;28:294302.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Bertschi D, Waskowski J, Schilling M, et al. Methods of assessing frailty in the critically ill: a systematic review of the current literature. Gerontology 2022;68:13211349.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Muscedere J, Waters B, Varambally A, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med 2017;43:11051122.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Oluwole OO, Bouabdallah K, Muñoz J, et al. Prophylactic corticosteroid use in patients receiving axicabtagene ciloleucel for large B-cell lymphoma. Br J Haematol 2021;194:690700.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Albano D, Camoni L, Rinaldi R, et al. Comparison between skeletal muscle and adipose tissue measurements with high-dose CT and low-dose attenuation correction CT of 18F-FDG PET/CT in elderly Hodgkin lymphoma patients: a two-centre validation. Br J Radiol 2021;94:20200672.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47.

    Nakano J, Fukushima T, Tanaka T, et al. Physical function predicts mortality in patients with cancer: a systematic review and meta-analysis of observational studies. Support Care Cancer 2021;29:56235634.

    • PubMed
    • Search Google Scholar
    • Export Citation
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