EJNMMI Research (Jan 2024)
ImmunoPET provides a novel way to visualize the CD103+ tissue-resident memory T cell to predict the response of immune checkpoint inhibitors
Abstract
Abstract Background Immune checkpoint inhibitors (ICIs) have made significant progress in oncotherapy improving survival of patients. However, the benefits are limited to only a small subgroup of patients who could achieve durable responses. Early prediction of response may enable treatment optimization and patient stratification. Therefore, developing appropriate biomarkers is critical to monitoring efficacy and assessing patient response to ICIs. Main body Herein, we first introduce a new potential biomarker, CD103, expressed on tissue-resident memory T cells, and discuss the potential application of CD103 PET imaging in predicting immune checkpoint inhibitor treatment. In addition, we describe the current targets of ImmunoPET and compare these targets with CD103. To assess the benefit of PET imaging, a comparative analysis between ImmunoPET and other imaging techniques commonly employed for tumor diagnosis was performed. Additionally, we compare ImmunoPET and immunohistochemistry (IHC), a widely utilized clinical method for biomarker identification with respect to visualizing the immune targets. Conclusion CD103 ImmunoPET is a promising method for determining tumor-infiltrating lymphocytes (TILs) load and response to ICIs, thereby addressing the lack of reliable biomarkers in cancer immunotherapy. Compared to general T cell markers, CD103 is a specific marker for tissue-resident memory T cells, which number increases during successful ICI therapy. ImmunoPET offers noninvasive, dynamic imaging of specific markers, complemented by detailed molecular information from immunohistochemistry (IHC). Radiomics can extract quantitative features from traditional imaging methods, while near-infrared fluorescence (NIRF) imaging aids tumor detection during surgery. In the era of precision medicine, combining such methods will offer a more comprehensive approach to cancer diagnosis and treatment.
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