Body Fat Biomarker: ADC Study in DLBCL Shows Promise
Body Composition as a Predictor of treatment Success in Relapsed/Refractory DLBCL
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New Research Highlights teh Role of Skeletal Muscle and Visceral Fat Ratios in Predicting Outcomes for Patients with Diffuse Large B-Cell Lymphoma
New York, NY – Body composition, specifically the ratio of skeletal muscle to visceral fat, is emerging as a notable predictor of treatment response in patients with relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Research presented from the LOTIS-2 trial suggests that pre-treatment body composition analysis, especially using deep learning techniques, could offer a more cost-effective and insightful approach to evaluating patient prognosis and treatment efficacy.
Understanding Body Composition Analysis in DLBCL Treatment
A recent study,published in the Journal of Clinical Oncology Clinical Cancer Informatics,delved into the intricate relationship between body composition and treatment outcomes for R/R DLBCL patients receiving the antibody-drug conjugate (ADC) loncastuximab tesirine. The research focused on analyzing three primary tissue areas: skeletal muscle, subcutaneous fat, and visceral fat. Thes measurements were taken at the third lumbar vertebra (L3) level, a standard and representative site for assessing overall body composition.
From these segmented regions, researchers developed key body composition ratio indices. These included the ratio of skeletal muscle to visceral fat, the ratio of subcutaneous fat to visceral fat, and the relationship of skeletal muscle to a combined measure of visceral and subcutaneous fat. The study meticulously compared manual and automated measurement techniques to ensure accuracy and explored how these indices correlated with treatment response, including their impact on time-to-event outcomes such as progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves were utilized to estimate these survival endpoints.
Key Findings: Skeletal Muscle to Visceral Fat Ratio as a Biomarker
The investigators reported significant findings regarding the skeletal muscle to visceral fat index. Both manual and automated measurements of this ratio,when dichotomized,proved to be significant predictors in both univariable and multivariable logistic models for the failure to achieve a complete metabolic response.
Furthermore, the manual skeletal muscle to visceral fat index was found to be substantially associated with progression-free survival (PFS) in both univariable and multivariable analyses. While it did not show a significant association with overall survival (OS) in this specific analysis, its impact on PFS is a crucial insight for clinicians.
Explaining the Link: Sarcopenia and Obesity’s Impact
The observed relationship between body composition and treatment response can be attributed to several factors. A 2023 article in Ageing Research Reviews highlighted that sarcopenia, a condition characterized by a loss of muscle strength and quality, can indeed limit a patient’s response to ADCs. Additionally, obesity can lead to dose reductions during treatment, which in turn can diminish the drug’s effectiveness. It is vital to note that clinical trials often enroll healthier patient populations, meaning these challenges might become more apparent when treatments are used in real-world clinical settings.
The Future of Body composition Analysis in Oncology
The authors from the University of Miami concluded that a patient’s skeletal muscle to visceral fat ratio, assessed prior to treatment, could serve as a valuable biomarker for evaluating patients with R/R DLBCL who are candidates for loncastuximab tesirine therapy.
“The proposed deep learning-based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective choice to conventional methods,” the researchers stated. This suggests that advanced computational methods can provide reliable and accessible tools for oncologists to better stratify patients and personalize treatment strategies.
references
- Kuker RA,Alderuccio JP,Han S,Polar MK,Crane TE,Moskowitz CH,Yang F. Deep learning-based body composition analysis for outcome prediction in relapsed/refractory diffuse large B-cell lymphoma: insights from the LOTIS-2 trial. JCO Clin cancer inform 2025; 9: DOI: 10.1200/CCI-25-00051
- Zhang FM, Wu HF, Shi HP, Yu Z, Zhuang CL. Sarcopenia and malignancies: epidemiology, clinical classification and implications.Age Res Rev. 2023;91:102057: doi:10.1016/j.arr.2023.102057.