A genetically encoded protein tag for control and quantitative imaging of CAR T cell therapy.

Chimeric antigen receptor (CAR) T cell therapy has been successful for hematological malignancies. Still, a lack of efficacy and potential toxicities have slowed its application for other indications. Furthermore, CAR T cells undergo dynamic expansion and contraction in vivo that cannot be easily predicted or controlled. Therefore, the safety and utility of such therapies could be enhanced by engineered mechanisms that engender reversible control and quantitative monitoring. Here, we use a genetic tag based on the enzyme Escherichia coli dihydrofolate reductase (eDHFR), and derivatives of trimethoprim (TMP) to modulate and monitor CAR expression and T cell activity. We fused eDHFR to the CAR C terminus, allowing regulation with TMP-based proteolysis-targeting chimeric small molecules (PROTACs). Fusion of eDHFR to the CAR does not interfere with cell signaling or its cytotoxic function, and the addition of TMP-based PROTACs results in a reversible and dose-dependent inhibition of CAR activity via the proteosome. We show the regulation of CAR expression in vivo and demonstrate imaging of the cells with TMP radiotracers. In vitro immunogenicity assays using primary human immune cells and overlapping peptide fragments of eDHFR showed no memory immune repertoire for eDHFR. Overall, this translationally-orientied approach allows for temporal monitoring and image-guided control of cell-based therapies.

Regulation of eDHFR-tagged proteins with trimethoprim PROTACs.

Temporal control of protein levels in cells and living animals can be used to improve our understanding of protein function. In addition, control of engineered proteins could be used in therapeutic applications. PRoteolysis-TArgeting Chimeras (PROTACs) have emerged as a small-molecule-driven strategy to achieve rapid, post-translational regulation of protein abundance via recruitment of an E3 ligase to the target protein of interest. Here, we develop several PROTAC molecules by covalently linking the antibiotic trimethoprim (TMP) to pomalidomide, a ligand for the E3 ligase, Cereblon. These molecules induce degradation of proteins of interest (POIs) genetically fused to a small protein domain, E. coli dihydrofolate reductase (eDHFR), the molecular target of TMP. We show that various eDHFR-tagged proteins can be robustly degraded to 95% of maximum expression with PROTAC molecule 7c. Moreover, TMP-based PROTACs minimally affect the expression of immunomodulatory imide drug (IMiD)-sensitive neosubstrates using proteomic and biochemical assays. Finally, we show multiplexed regulation with another known degron-PROTAC pair, as well as reversible protein regulation in a rodent model of metastatic cancer, demonstrating the formidable strength of this system. Altogether, TMP PROTACs are a robust approach for selective and reversible degradation of eDHFR-tagged proteins in vitro and in vivo.

Monitoring Therapeutic Response to Anti-FAP CAR T Cells Using [18F]AlF-FAPI-74.

PURPOSE: Despite the success of chimeric antigen receptor (CAR) T-cell therapy against hematologic malignancies, successful targeting of solid tumors with CAR T cells has been limited by a lack of durable responses and reports of toxicities. Our understanding of the limited therapeutic efficacy in solid tumors could be improved with quantitative tools that allow characterization of CAR T-targeted antigens in tumors and accurate monitoring of response. EXPERIMENTAL DESIGN: We used a radiolabeled FAP inhibitor (FAPI) [18F]AlF-FAPI-74 probe to complement ongoing efforts to develop and optimize FAP CAR T cells. The selectivity of the radiotracer for FAP was characterized in vitro, and its ability to monitor changes in FAP expression was evaluated using rodent models of lung cancer. RESULTS: [18F]AlF-FAPI-74 showed selective retention in FAP+ cells in vitro, with effective blocking of the uptake in presence of unlabeled FAPI. In vivo, [18F]AlF-FAPI-74 was able to detect FAP expression on tumor cells as well as FAP+ stromal cells in the tumor microenvironment with a high target-to-background ratio. We further demonstrated the utility of the tracer to monitor changes in FAP expression following FAP CAR T-cell therapy, and the PET imaging findings showed a robust correlation with ex vivo analyses. CONCLUSIONS: This noninvasive imaging approach to interrogate the tumor microenvironment represents an innovative pairing of a diagnostic PET probe with solid tumor CAR T-cell therapy and has the potential to serve as a predictive and pharmacodynamic response biomarker for FAP as well as other stroma-targeted therapies. A PET imaging approach targeting FAP expressed on activated fibroblasts of the tumor stroma has the potential to predict and monitor therapeutic response to FAP-targeted CAR T-cell therapy. See related commentary by Weber et al., p. 5241.

Building the Bridge: Molecular Imaging Biomarkers for 21st Century Cancer Therapies.

Precision medicine, where the molecular underpinnings of the disease are assessed for tailored therapies, has greatly impacted cancer care. In parallel, a new pillar of therapeutics has emerged with profound success, including immunotherapies such as checkpoint inhibitors and cell-based therapies. Nonetheless, it remains essential to develop paradigms to predict and monitor for therapeutic response. Molecular imaging has the potential to add substantially to all phases of cancer patient care: predicative, companion diagnostics can illuminate therapeutic target density within a tumor, and pharmacodynamic imaging biomarkers can complement traditional modalities to judge a favorable treatment response. This "Focus on Molecular Imaging" article discusses the current role of molecular imaging in oncology and highlights an additional step in clinical paradigm termed a "therapeutic biomarker," which serves to assess whether next generation drugs reach their target to elicit a favorable clinical response.

A Chemical Approach for Programmable Protein Outputs Based on Engineered Cell Interactions.

Cell-cell interactions and communication are crucial to the proper function of complex mammalian physiology including neurocognitive and immune system functions. While many tools are available for observing and perturbing intracellular processes, relatively few exist to probe intercellular processes. Current techniques for studying interactions often rely on direct protein contact, and few can manipulate diverse, functional outputs with tunable protein expression. To address these limitations, we have developed a small-molecule approach based on a trimethoprim prodrug-enzyme pair capable of reporting the presence of two different engineered cell populations with programmable protein outputs. The approach relies on bacterial nitroreductase enzyme catalysis, which is orthogonal to normal mammalian biology, and diffusion of trimethoprim from "activator" cells to "receiver" cells. We test this strategy, which can theoretically regulate many different types of proteins, using biochemical and in vitro culture assays with optical and cytokine protein readouts. This describes the first small-molecule approach capable of detecting and controlling engineered cell-cell outputs, and we anticipate future applications that are especially relevant to the field of immuno-oncology.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.

OBJECTIVES: There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. METHODS: Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. RESULTS: Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists. CONCLUSIONS: These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. KEY POINTS: • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.