Impact of Muscle Measures on Outcome in Patients Receiving Endocrine Therapy for Metastatic Breast Cancer: Analysis of ECOG-ACRIN E2112

Background: Observational data investigating the relationship between body habitus and outcomes in breast cancer have been variable and inconsistent, largely centered in the curative setting and focused on weight-based metrics. This study evaluated the impact of muscle measures on outcomes in patients with metastatic breast cancer receiving endocrine-based therapy. Methods: Baseline CT scans were collected from ECOG-ACRIN E2112, a randomized phase III placebo-controlled study of exemestane with or without entinostat. A CT cross-sectional image at the L3 level was extracted to obtain skeletal muscle mass and attenuation. Low muscle mass (LMM) was defined as skeletal muscle index,41 cm²/m² and low muscle attenuation (LMA) as muscle density,25 HU or,33 HU if overweight/ obese by body mass index (BMI). Multivariable Cox proportional hazard models determined the association between LMM or LMA and progression-free survival (PFS) and overall survival (OS). Correlations between LMM, LMA, and patient-reported outcomes were determined using 2-sample t tests. Results: Analyzable CT scans and follow-up data were available for 540 of 608 patients. LMM was present in 39% (n5212) of patients and LMA in 56% (n5301). Those with LMA were more likely to have obesity and worse performance status. LMM was not associated with survival (PFS hazard ratio [HR]: 1.13, P5.23; OS HR: 1.05, P5.68), nor was LMA (PFS HR: 1.01, P5.93; OS HR: 1.00, P5.99). BMI was not associated with survival. LMA, but not LMM, was associated with increased frequency of patient-reported muscle aches. Conclusions: Both low muscle mass and density are prevalent in patients with hormone receptor–positive metastatic breast cancer. Muscle measures correlated with obesity and performance status; however, neither muscle mass nor attenuation were associated with prognosis. Further work is needed to refine body composition measurements and select optimal cutoffs with meaningful endpoints in specific breast cancer populations, particularly those living with metastatic disease.