In vivo nanoparticle-based T cell imaging can predict therapy response towards adoptive T cell therapy in experimental glioma.

ABSTRACT

Rationale Intrinsic brain tumors, such as gliomas are largely resistant to immunotherapies including immune checkpoint blockade. Adoptive cell therapies (ACT) including chimeric antigen receptor (CAR) or T cell receptor (TCR)-transgenic T cell therapy targeting glioma-associated antigens are an emerging field in glioma immunotherapy. However, imaging techniques for non-invasive monitoring of adoptively transferred T cells homing to the glioma microenvironment are currently lacking. Methods: Ultrasmall iron oxide nanoparticles (NP) can be visualized non-invasively by magnetic resonance imaging (MRI) and dedicated MRI sequences such as T2* mapping. Here, we develop a protocol for efficient ex vivo labeling of murine and human TCR-transgenic and CAR T cells with iron oxide NPs. We assess labeling efficiency and T cell functionality by flow cytometry and transmission electron microscopy (TEM). NP labeled T cells are visualized by MRI at 9.4 T in vivo after adoptive T cell transfer and correlated with 3D models of cleared brains obtained by light sheet microscopy (LSM). Results: NP are incorporated into T cells in subcellular cytoplasmic vesicles with high labeling efficiency without interfering with T cell viability, proliferation and effector function as assessed by cytokine secretion and antigen-specific killing assays in vitro. We further demonstrate that adoptively transferred T cells can be longitudinally monitored intratumorally by high field MRI at 9.4 Tesla in a murine glioma model with high sensitivity. We find that T cell influx and homogenous spatial distribution of T cells within the TME as assessed by T2* imaging predicts tumor response to ACT whereas incomplete T cell coverage results in treatment resistance. Conclusion: This study showcases a rational for monitoring adoptive T cell therapies non-invasively by iron oxide NP in gliomas to track intratumoral T cell influx and ultimately predict treatment outcome.