Chimeric antigen receptor (CAR) T-cell therapy has provided a novel, effective treatment option for patients with relapsed or refractory non-Hodgkin lymphoma. However, many patients still experience relapse after CAR T-cell treatment. As the field evolves and improves, one subtopic for future study is the utility of medical imaging technology to predict and assess therapeutic response. A recent article in Frontiers in Oncology reviewed the state of the science and suggested future directions for research.
“At baseline, high tumor volume … is a prognostic factor associated with treatment failure. Response assessment has not been studied extensively yet,” wrote the authors, led by Laetitia Vercellino of the Nuclear Medicine Department at Hôpital Saint-Louis, Assistance Publique Hôpitaux, in Paris. “The number of patients experiencing relapse stresses the need for reliable biomarkers to closely monitor clinical response and implement early consolidation strategies.”
For example, their research indicated that high tumor volume may be associated with more severe cytokine release syndrome. If confirmed, that correlation could have an impact on patient monitoring and management.
The article reviewed certain tools that have already proven useful in the field of CAR T-cell therapy and could be even more useful to predict and assess response to treatment:
- computed tomography scans
- positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT)
- total metabolic tumor volume
- standardized uptake value
- total lesion glycolysis consumption
The authors noted that research should further evaluate whether the tools can provide predictive and prognostic biomarkers that stratify risk of relapse in patients receiving CAR T-cell therapy for hematologic malignancy. One example is C-reactive protein at time of lymphodepletion.
They called for future research to find ways to use these tools to further improve and personalize care in specific ways:
- risk stratification
- prediction of response
- assessment of response
- early detection of relapse
In addition, they noted, future research should focus on clarifying reasons for variations in response patterns and progression. For example, they wrote, research should examine pseudoprogression, slow and late responses, and the timing of relapses. They cited artificial intelligence and radiomics as specific areas of promise.
“A better knowledge and understanding of imaging data could contribute to detect and treat toxicities timely and further tailor the therapeutic strategy,” the authors wrote.