Background: Low muscle mass is associated with adverse cancer outcomes including increased chemotherapy toxicity and reduced survival, while adiposity is linked to chronic low grade inflammation that may increase cancer risk, progression and metastasis. This study investigates the relationship of body composition indices, including muscle mass and adiposity, with treatment outcomes in patients with mNSCLC receiving immune check-point inhibitor-Nivo. Methods: Pretreatment computerized tomography (CT) images were evaluated for skeletal muscle area (SMA), density (SMD), and adipose tissue at the 3rd lumbar vertebrae. Skeletal muscle index (SMI) (SMA/height2) was calculated. Total adipose tissue (TAT) is the sum of visceral, intermusculscular and subcutaneous tissue. Established cut points were used for sarcopenia and TAT.(Martin, JCO, 2015. Caan, CEBP, 2017). The Kaplan-Meier method was used to estimate median survival times, and univariable Cox regression modeling was used to estimate and compare progression free survival (PFS), duration of response (DoR) and overall survival (OS). Odds ratios were estimated for predicting toxicity and group proportions were compared using Fisher`s Exact test. Results: Metastatic NSCLC patients (N = 78) had CT scans within 45 days of starting Nivo. Mean age was 67 (range 24-88). 67% were sarcopenic. Grade 3-4 toxicities (CTCAE version 4.0) were reported in 15% of sarcopenic vs 0% of non-sarcopenic patients (Fisher`s exact test p-value = 0.046, adjusted OR p-value < 0.0001). Response rate was 28%. PFS and OS were shorter in patients with high TAT (1.9 vs 3.7 months, p = 0.064 and 6.2 vs 9.8 months, p = 0.05 respectively). DoR was shorter in non-sarcopenic pts (10.5 vs 16.6 months, p = 0.049) and patients with high TAT (4.7 vs 13.8 months, p = 0.012, adjusted HR = 37.2, p = 0.009). Conclusions: Body composition indices, including muscle and adipose tissue, were associated with toxicity outcomes and survival in patients receiving immune checkpoint inhibitor-Nivo. Further studies are needed to explore how routinely obtained CT scans can be used to individualize the oncologic treatment plan and appropriate interventions.
|Journal||Journal of Clinical Oncology|
|State||Published - 20 May 2018|