Body Fat as a Biomarker? Study of ADC Use in DLBCL Suggests “Yes”

Investigators are increasingly looking to biomarkers to predict cancer progression and response to treatment. But not all biomarkers are revealed with blood tests. As a team from Sylvester Comprehensive Cancer Center at the University of Miami showed recently, sometimes a body scan will do.

Results published earlier this month in JCO Clinical Cancer Informatics, a journal from the American Society of Clinical Oncology, found that analyzing body composition—especially the relationship between skeletal muscle and visceral fat—could be used to compute indices associated with a patient’s response to a treatment for relapsed/refractory diffuse large B-cell lymphoma (DLBCL).

Specifically, the team sought to understand the relationship between body composition and response to loncastuximab tesirine, an antibody-drug conjugate (ADC) that targets CD19. It is sold as Zynlonta (ADC Therapeutics) and administered intravenously.

Investigators enrolled a retrospective cohort of 140 patients with R/R DLBCL who had been treated with loncastuximab tesirine in the LOTIS-2 trial. (NCT03589469). During the study, these patients received scans with PET/CT, a combination technique that gives physicians information about patient anatomy and function to assess not only the presence of cancer but also heart disease; it can detect brain metastases, although it is not the primary test for this condition.

Using the scans, investigators conducted body composition analyses, with both manual and deep learning–based segmentation of 3 primary tissue areas: skeletal muscle, subcutaneous fat, and visceral fat, which were measured at the third lumbar vertebra, or L3 level, a well-established technique deemed representative of muscle and fat distribution throughout the body.

From these segmented regions, the team developed body composition ratio indices, including the ratio of skeletal muscle to visceral fat, the ratio of subcutaneous fat to visceral fat, and the relationship of skeletal muscle to a composite of visceral and subcutaneous fat. They performed calculations to examine levels of agreement between manual and automated measures, and then analyzed the relationship between the indices and treatment response, including how much body composition affected time-to-event outcomes.

Kaplan-Meier curves were calculated to estimate progression-free survival (PFS) and overall survival (OS).

Investigators found that both the manual and automated skeletal muscle/visceral fat indices, “as dichotomized, were significant predictors in univariable and multivariable logistic models for failure to achieve complete metabolic response.”

The manual skeletal muscle/visceral fat index was “significantly associated with PFS, but not OS,” in both univariable and multivariable models, they wrote.

What explains this? A 2023 article in the journal Ageing Research Reviews found that sarcopenia—a disorder characterized by lack of muscle strength and quality—can limit response to ADCs.² And, being obese can cause dose reductions during treatment that can also limit effectiveness.

However, because clinical trials often enroll healthier patients, these situations may not show up until drugs are approved and physicians use them in real-world settings.

The University of Miami authors concluded that a patient’s skeletal muscle to visceral fat ratio prior to treatment could be a useful biomarker when evaluating those with R/R DLBCL who are treated with the ADC loncastuximab tesirine.¹

“The proposed deep learning–based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective alternative to conventional methods,” they wrote.

References

1. 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
2. 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.