Search Results (137)

Obesity is a systemic metabolic disorder characterized by chronic low-grade inflammation and insulin resistance, with growing evidence indicating that the brain represents a primary and particularly vulnerable target organ. Beyond peripheral metabolic consequences, obesity induces region-specific structural, functional, and biochemical alterations within the central nervous system, contributing to cognitive impairment, dysregulated energy homeostasis, and increased susceptibility to neurodegenerative diseases. This narrative review examines key neurometabolic and neuroinflammatory mechanisms underlying obesity-related brain vulnerability, including downstream neuroinflammation, impaired insulin signaling, mitochondrial dysfunction, oxidative stress, blood–brain barrier disruption, and impaired brain clearance mechanisms. These processes preferentially affect frontal and limbic networks involved in executive control, reward processing, salience detection, and appetite regulation. Advanced neuroimaging has substantially refined our understanding of these mechanisms. Magnetic resonance spectroscopy provides unique in vivo insight into early neurometabolic alterations that may precede irreversible structural damage and is complemented by diffusion imaging, volumetric MRI, functional MRI, cerebral perfusion imaging, and positron emission tomography. Together, these complementary modalities reveal microstructural, network-level, structural, hemodynamic, and molecular alterations associated with obesity-related brain vulnerability and support the concept that such brain dysfunction is dynamic and potentially modifiable. Integrating neurometabolic and multimodal neuroimaging biomarkers with metabolic and clinical profiling may improve early risk stratification and guide preventive and therapeutic strategies aimed at preserving long-term brain health in obesity. Full article

Positron Emission Tomography (PET) is undergoing a profound transformation. Driven by the convergence of highly sensitive long-axial field-of-view (LAFOV) total-body PET systems and an expanding portfolio of targeted radiopharmaceuticals, PET is progressively evolving beyond its traditional role in oncologic diagnosis and staging. Ultra-sensitive scanners enable whole-body imaging with markedly reduced radiotracer doses, rapid acquisition times, and true dynamic multiparametric imaging across all organs simultaneously. In parallel, molecularly targeted radioligands support tumour phenotyping, theranostic applications, and personalized dosimetry. Together, these advances position PET as a systemic imaging platform capable of interrogating whole-body tumour biology, guiding precision therapies, and potentially enabling early detection or surveillance strategies in selected high-risk populations. This narrative review summarizes the technological foundations of total-body PET, reviews current clinical and translational applications, discusses opportunities and limitations for early detection and surveillance, and outlines a research and implementation roadmap to responsibly translate this paradigm into clinical oncology. Full article

Background/Objectives: Alzheimer’s disease (AD) has a prolonged preclinical phase and marked heterogeneity. Brain age and the Brain Age Gap (BAG), derived from neuroimaging and machine learning (ML), offer a non-invasive, system-level indicator of brain integrity, with potential relevance for early detection, risk stratification, and intervention monitoring. This review summarizes the conceptual basis, imaging characteristics, biological relevance, and explores its potential clinical utility of BAG across the AD continuum. Methods: We conducted a narrative synthesis of evidence from morphometric structural magnetic resonance imaging (sMRI), connectivity-based functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI), alongside recent advances in deep learning architectures and multimodal fusion techniques. We further examined associations between BAG and the Amyloid/Tau/Neurodegeneration (A/T/N) framework, neuroinflammation, cognitive reserve, and lifestyle interventions. Results: BAG may reflect neurodegeneration associated with AD, showing greater deviations in individuals with mild cognitive impairment (MCI) and early AD, and is correlated with tau pathology, neuroinflammation, and metabolic or functional network dysregulation. Multimodal and deep learning approaches enhance the sensitivity of BAG to disease-related deviations. Longitudinal BAG changes outperform static BAG in forecasting cognitive decline, and lifestyle or exercise interventions can attenuate BAG acceleration. Conclusions: BAG emerges as a promising, dynamic, integrative, and modifiable complementary biomarker with the potential for assessing neurobiological resilience, disease staging, and personalized intervention monitoring in AD. While further standardization and large-scale validation are essential to support clinical translation, BAG provides a novel systems-level perspective on brain health across the AD continuum. Full article

Glioblastoma (GBM) is one of the most common and aggressive primary malignant tumors of the central nervous system, accounting for about half of all gliomas in adults. Despite intensive research and advances in molecular biology, genomics, and modern neuroimaging techniques, the prognosis for patients with GBM remains extremely poor. Despite the implementation of multimodal treatment involving surgery, radiotherapy, and chemotherapy with temozolomide, the average survival time of patients is only about 15 months. This is primarily due to the complex biology of this cancer, which involves numerous genetic and epigenetic abnormalities, as well as a highly heterogeneous tumor structure and the presence of glioblastoma stem cells with self renewal capacity. Mutations and abnormalities in genes such as IDH-wt, EGFR, PTEN, TP53, TERT, and CDKN2A/B are crucial in the pathogenesis of GBM. In particular, IDH-wt status (wild-type isocitrate dehydrogenase) is one of the most important identification markers distinguishing GBM from other, more favorable gliomas with IDH mutations. Frequent EGFR amplifications and TERT gene promoter mutations lead to the deregulation of tumor cell proliferation and increased aggressiveness. In turn, the loss of function of suppressor genes such as PTEN or CDKN2A/B promotes uncontrolled cell growth and tumor progression. The immunosuppressive tumor microenvironment also plays an important role, promoting immune escape and weakening the effectiveness of systemic therapies, including immunotherapy. The aim of this review is to summarize the current state of knowledge on the epidemiology, classification, pathogenesis, diagnosis, and treatment of glioblastoma multiforme, as well as to discuss the impact of recent advances in molecular and imaging diagnostics on clinical decision-making. A comprehensive review of recent literature (2018–2025) was conducted, focusing on WHO CNS5 classification updates, novel biomarkers (IDH, TERT, MGMT, EGFR), and modern diagnostic techniques such as liquid biopsy, radiogenomics, and next-generation sequencing (NGS). The results of the review indicate that the introduction of integrated histo-molecular diagnostics in the WHO 2021 classification has significantly increased diagnostic precision, enabling better prognostic and therapeutic stratification of patients. Modern imaging techniques, such as advanced magnetic resonance imaging (MRI), positron emission tomography (PET), and radiomics and radiogenomics tools, allow for more precise assessment of tumor characteristics, prediction of response to therapy, and monitoring of disease progression. Contemporary molecular techniques, including DNA methylation profiling and NGS, enable in-depth genomic and epigenetic analysis, which translates into a more personalized approach to treatment. Despite the use of multimodal therapy, which is based on maximum safe tumor resection followed by radiotherapy and temozolomide chemotherapy, recurrence is almost inevitable. GBM shows a high degree of resistance to treatment, which results from the presence of stem cell subpopulations, dynamic clonal evolution, and the ability to adapt to unfavorable microenvironmental conditions. Promising preclinical and early clinical results show new therapeutic strategies, including immunotherapy (cancer vaccines, checkpoint inhibitors, CAR-T therapies), oncolytic virotherapy, and Tumor Treating Fields (TTF) technology. Although these methods show potential for prolonging survival, their clinical efficacy still needs to be confirmed in large studies. The role of artificial intelligence in the analysis of imaging and molecular data is also increasingly being emphasized, which may contribute to the development of more accurate predictive models and therapeutic decisions. Despite these advancements, GBM remains a major therapeutic challenge due to its high heterogeneity and treatment resistance. The integration of molecular diagnostics, artificial intelligence, and personalized therapeutic strategies that may enhance survival and quality of life for GBM patients. Full article

Glomerular filtration rate (GFR) is a key indicator of renal function. Traditional methods for GFR measurement have limitations including invasiveness, low spatial resolution, and lengthy protocols. Positron emission tomography (PET) radiotracers have emerged as promising tools for non-invasive, accurate, and dynamic renal function assessment. Objectives: This systematic literature review evaluates the clinical utility, and current evidence surrounding PET radiotracers used for GFR measurement in humans, emphasizing advances over conventional renal imaging modalities. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, from database inception to November 2024. The search identified studies evaluating PET-based measurement of glomerular filtration rate (GFR) and renal perfusion. Inclusion criteria encompassed human studies using PET radiotracers (e.g., ⁶⁸Ga, ¹⁸F) with comparisons to reference standards (estimated GFR or serum creatinine). Two authors independently screened titles/abstracts, extracted data, and assessed bias using Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). Exclusions included animal studies, reviews, and non-English articles. Results: Eleven studies met inclusion criteria, with ⁶⁸Ga-EDTA showing the highest validation against reference standards such as ⁵¹Cr-EDTA plasma clearance, demonstrating strong correlation. PET imaging offered superior spatial–temporal resolution, enabling accurate split renal function assessment and quantitative analysis of both filtration and perfusion. ⁶⁸Ga-somatostatin analogues exhibited moderate correlations between renal SUV and estimated GFR, with post-PRRT uptake changes indicating early nephrotoxicity. Among novel tracers, ⁶⁸Ga-FAPI showed a strong inverse SUV–GFR relationship, reflecting renal fibrosis and suggesting potential as a chronic kidney disease (CKD) biomarker but requires further clinical validation. Limitations across studies include small sample sizes, retrospective designs, and variability in reference standards. Conclusions: PET radiotracers, particularly ⁶⁸Ga-EDTA, represent a significant advancement for non-invasive, quantitative GFR measurement with improved precision and renal anatomical detail compared to traditional methods. Future prospective, large-scale human studies with standardized protocols are needed to establish these PET tracers as routine clinical tools in nephrology. Integration of hybrid PET/MRI and novel tracer development may further enhance renal diagnostic capabilities. Full article

Background/Objectives: The role of α-synuclein (α-syn) in the pathogenesis of Parkinson’s disease (PD) or neurodegenerative diseases such as Lewy body dementia (LBD) and multiple system atrophy (MSA) is commonly accepted. Through different physiological dysfunctions, abnormal forms of α-syn are generated. These abnormal aggregates accumulate and alter pre- and postsynaptic transmission, in particular that of dopamine. Thus, the development of a diagnostic biomarker of synucleinopathies remains crucial and challenging. The development of an α-syn positron emission tomography (PET) radiopharmaceutical may be suitable to early diagnose and stratify patients, follow up disease progression, and evaluate future therapies. Methods: To develop a selective α-syn PET tracer, we synthesized an original series based on alkynyl(aza)indoles. Fifteen final ligands were synthesized bearing indoles or azaindoles from one side of the alkyne and a substituted phenyl ring for the opposite side of the alkyne. The final ligands were tested to determine Ki and/or Kd toward α-syn, tau, and Aβ. Results: The SAR showed that the indole series exhibited moderate to low affinity for α-syn and, moreover, lower Ki toward Aβ and tau (i.e., compound 39, Ki_(αsyn) 21.7 nM, Ki_(Aβ) 64.4 nM, Ki_(Tau) 27.6 nM), highlighting the low potency of these series to afford an α-syn tracer. The introduction of a nitrogen on the different positions of the phenyl to obtain the corresponding azaindoles resulted for most of the compounds in better affinity for α-syn and selectivity towards Aβ compared to the indole analogs (i.e., compound 43, Ki_(αsyn) 4.7 nM, Ki_(Aβ) 24.4 nM, and Ki_(Tau) 4.61 nM). A fluorinated azaindole derivative was prepared with a view to obtaining a ¹⁸F tracer and exhibited the highest affinity for α-syn but without selectivity against tau and Aβ. The radiosynthesis of [¹⁸F]45 was performed in a two-step procedure starting from the tosylated and protected precursor. [¹⁸F]45 was obtained in 85 ± 5 min with a radiochemical yield of 32 ± 3%. Molar activity, determined from a calibration with stable 45, was around 130 GBq/µmole. The dynamic PET imaging showed that [¹⁸F]45 was able to cross the blood–brain barrier, but non-specific uptake was observed, confirming the in vitro results. Conclusions: Although promising nanomolar affinity for the target, the new tracer showed mainly non-specific in vivo uptake in the rat brain, indicating that further pharmacomodulations on the azaindole series are required. Full article

Senescence is a dynamic, multifaceted process implicated in tissue aging, organ dysfunction, and intricately associated with numerous chronic diseases. As senescent cells accumulate, they drive inflammation, fibrosis, and metabolic disruption through the senescence-associated secretory phenotype (SASP). Despite its clinical relevance, senescence remains challenging to detect non-invasively due to its heterogeneous nature and the lack of universal biomarkers. Recent advances in the development of specific imaging probes for positron emission tomography (PET) enable in vivo visualization of senescence-associated pathways across key organs, such as the lung, heart, kidney, and metabolic processes. For instance, [¹⁸F]FPyGal, a β-galactosidase-targeted tracer, has demonstrated selective accumulation in senescent cells in both preclinical and early clinical studies, while FAP-targeted radioligands are emerging as tools for imaging fibrotic remodeling in the lung, liver, kidney, and myocardium. This review examines a new generation of PET radioligands targeting hallmark features of senescence, with the potential to track and measure the process, the ability to be translated into clinical interventions for early diagnosis, and longitudinal monitoring of senescence-driven pathologies. By integrating organ-specific imaging biomarkers with molecular insights, PET probes are poised to transform our ability to manage and treat age-related diseases through personalized approaches. Full article

The highly heterogeneous and invasive nature characteristic of high-grade gliomas (HGG) has historically limited the efficacy of standard-of-care approaches, resulting in poor prognosis and treatment outcomes. Novel immunotherapies have shown remarkable potential to promote antitumoral immune responses and allow for long-term tumor remission. However, the complexity of the HGG tumor microenvironment and the dynamic immunological changes associated with immunotherapy response can limit the diagnostic utility of conventional magnetic resonance imaging (MRI) and positron emission tomography (PET) approaches. Consequently, distinguishing true tumor progression from immunotherapy-related effects often requires prolonged clinical follow-up over several months. To address this, novel quantitative MRI and PET-based approaches are being evaluated in preclinical studies and clinical trials. These advanced imaging methods target key biological features of the tumor microenvironment, including vascularity, cellularity, intratumoral habitats, tracer pharmacokinetics and immune infiltration, and can provide metrics to stratify patient response at earlier timepoints to support clinical decision making and improve treatment outcomes. This review highlights key HGG biological characteristics, describes standard-of-care and emerging therapeutic strategies, and discusses both conventional and advanced imaging methods to characterize immunotherapeutic responses. Full article

Tumor-associated macrophages (TAMs) are pivotal immune cells within the tumor stroma, whose dynamic alterations significantly impact tumor progression and therapeutic responses. Conventional methods for TAM detection, such as biopsy, are invasive and incapable of whole-body dynamic monitoring. In contrast, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) offer a non-invasive imaging approach by targeting TAM-specific biomarkers like CD206, TSPO, and CCR2. This review comprehensively summarizes the advancements in TAM-targeted imaging probes, including cell surface markers, metabolic/functional markers, and multifunctional nanoprobe, while assessing their potential in tumor immune surveillance and tumor targeting therapeutic applications. While current probes, including ⁶⁸Ga-NOTA-anti-CD206 and ⁶⁴Cu-Macrin, have exhibited high specificity and theragnostic potential in preclinical and early clinical trials, challenges such as target heterogeneity, off-target effects, and clinical translation persist. Moving forward, the advancement of multi-target probes, optimization of pharmacokinetics, and incorporation of multimodal imaging technologies are anticipated to further enhance the impact of TAM-targeted imaging in precision medicine and tumor immunotherapy, fostering the refinement of personalized treatment strategies and improving patient outcomes. Full article

Background: The recent introduction of whole-body positron emission tomography/ computed tomography (PET/CT) scanners and multi-bed, multi-time point acquisition technique enable calculating fluorodeoxyglucose (FDG) kinetics in the whole body. However, validating parametric, Patlak-derived data is difficult on phantoms. Methods: This prospective study investigated the effect of quantification methods mean, max, and peak on the metabolic rate (MR-FDG) and distribution volume (DV-FDG) quantification, as well as the diagnostic accuracy of parametric Patlak FDG-PET scans in diagnosing lung lesions and lymph node metastases, using histopathology and follow-up as reference standards. Dynamic whole-body FDG PET was acquired for 80 minutes in 34 patients with indeterminate lung lesions and kinetic parameters extracted from lung lesions and representative mediastinal and hilar lymph nodes. Results: All quantification methods—mean, max, and peak—demonstrated high diagnostic accuracy (AUC: MR-FDG: 0.987–0.991 and 0.893–0.905; DV-FDG: 0.948–0.975 and 0.812–0.825) for differentiating benign from malignant lymph nodes and lung lesions. Differences in the magnitude of MR-FDG (−4.76–14.09) and DV-FDG (−10.64–46.10%) were substantial across methods. Variability was more pronounced in lymph nodes (MR-FDG: 1.37–3.48) than in lung lesions (MR-FDG: 3.31–5.04). The variability was lowest between mean and max quantification, with percentage differences of 40.87 ± 5.69% for MR-FDG and 39.26 ± 7.68% for DV-FDG. Conclusions: The choice of method to measure MR-FDG and DV-FDG greatly influences the results, especially in smaller lesions with large and systematic differences. For lung lesions, a conversion factor between mean and max methods of 40% provides acceptable agreement, facilitating retrospective comparisons of measurements, e.g., in meta-analyses. Full article

Background/Objectives: Positron emission tomography (PET) imaging with radiolabeled amino acids is increasingly used in glioma patients for biopsy planning, tumor delineation, prognostication, and therapy response assessment. This study investigated whether baseline amino acid PET imaging could identify regions at risk of future tumor recurrence. Methods: Retrospective case series of 14 patients with high-grade glioma. Contrast-enhanced magnetic resonance imaging (MRI) data of tumor recurrence and baseline imaging (PET-MRI) were co-registered. Volumes of interest (VOIs) of the high-grade glioma were derived from contrast-enhanced MRI at baseline and follow-up and from amino acid PET at baseline. The Dice similarity coefficient (DSC) was used to assess the overlap between VOIs. Furthermore, dynamic and static PET parameters were compared between the VOIs derived from contrast-enhanced MRI at follow-up and from the region of increased amino acid transport at baseline. Results: Regions of tumor recurrence in high-grade glioma patients overlap significantly more with baseline regions of increased amino acid transport on PET compared to regions of contrast enhancement on baseline MRI (p < 0.001). However, the static and dynamic PET statistics did not differentiate between regions that would later develop tumor recurrence and other areas of increased amino acid transport at baseline. Conclusions: These findings reaffirm the ability of amino acid PET to visualize the infiltrative components of gliomas not detected by contrast-enhanced MRI. Also, this study supports the role of amino acid PET in visualizing glioma infiltration beyond the MRI-visible tumor, but also indicates that accurately predicting the specific regions of recurrence based on baseline PET remains limited. Full article

Background/Objectives: Structural magnetic resonance imaging (MRI) and 18-fluoro-deoxy-glucose positron emission tomography (PET) reveal the structural and functional information of the brain from different dimensions, demonstrating considerable clinical and practical value in the computer-aided diagnosis of Alzheimer’s disease (AD). However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. Methods: In this study, we propose a multi-modal deep domain adaptation (MM-DDA) model that integrates MRI and PET modal data, which aims to maximize the utilization of the complementarity of the multi-modal data and narrow the differences in domain distribution to boost the accuracy of AD classification. Specifically, MM-DDA comprises three primary modules: (1) the feature encoding module, which employs convolutional neural networks (CNNs) to capture detailed and abstract feature representations from MRI and PET images; (2) the multi-head attention feature fusion module, which is used to fuse MRI and PET features, that is, to capture rich semantic information between modes from multiple angles by dynamically adjusting weights, so as to achieve more flexible and efficient feature fusion; and (3) the domain transfer module, which reduces the distributional discrepancies between the source and target domains by employing adversarial learning training. Results: We selected 639 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and considered two transfer learning settings. In ADNI1→ADNI2, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 92.40%, 81.81%, 81.13%, and 85.45%, respectively. In ADNI2→ADNI1, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 94.73%, 81.48%, 85.48%, and 81.69%, respectively. Conclusions: MM-DDA is compared with other deep learning methods on two kinds of transfer learning, and the performance comparison results confirmed the superiority of the proposed method in AD prediction tasks. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. Methods: The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including cerebrospinal fluid (CSF) amyloid-beta 42 (A${\beta }_{42}$), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)—were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF A${\beta }_{42}$ levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework—comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification—on a curated dataset of 307 instances (ages 10–110) from Kaggle, the Alzheimer’s Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. Results: The study highlights the model’s potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Conclusions: Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s disease by providing a scalable computational tool for early risk assessment and intervention planning. Full article

Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [¹¹C]DPA713, which targets the 18-kDa translocator protein (TSPO), offers the ability to quantify neuroinflammation in humans in vivo. The primary objectives of this study were to (1) assess whether uptake of [¹¹C]DPA713, a metric of neuroinflammation, is higher in the neuroforamina and spinal cord of patients with painful cervical radiculopathy compared with that in pain-free participants and (2) assess whether [¹¹C]DPA713 uptake is associated with clinical parameters, such as pain intensity. Methods: Dynamic 60 min [¹¹C]DPA713 PET/CT scans were acquired, and regions of interest were defined for neuroforamina and spinal cord. Resulting time-activity curves were fitted to a single-tissue compartment model using an image-derived input function, corrected for plasma-to-whole blood ratios and parent fractions, to obtain the volume of distribution (V_T) as the primary outcome measure. Secondary neuroinflammation metrics included 1T2k V_T without metabolite correction (1T2k_WB) and Logan V_T. Results: The results indicated elevated levels of 1T2k V_T at the neuroforamina (p < 0.04) but not at the spinal cord (p = 0.16). Neuroforamina and spinal cord 1T2k V_T lack associations with clinical parameters. Secondary neuroinflammatory metrics show associations with clinical parameters such as the likelihood of neuropathic pain. Conclusions: These findings enhance our understanding of painful cervical radiculopathy’s pathophysiology, emphasizing the neuroforamina levels of neuroinflammation as a potential therapeutic target. Full article

Target modules (TMs), intermediate molecules required for UniCAR T-cell therapy, are promising molecules for immunotheranostic approaches. In the current work, we developed TMs containing a monomeric or dimeric form of the antagonist bombesin peptide (BBN2) and assessed their potential for diagnostic imaging using positron emission tomography (PET) as well as immunotherapy in combination with UniCAR T-cells to target and image GRPR expression in prostate cancer. Synthesized monomeric and dimeric BBN2 TMs retained binding to GRPR in vitro. Both BBN2 TMs specifically activated and redirected UniCAR T-cells to eradicate PC3 and LNCaP cancer cells with high efficiency and in a comparable manner. UniCAR T-cells retained a non-exhausted memory phenotype favorable to their persistence and fitness. The ⁶⁸Ga-labeled BBN2 TMs showed proof-of-target towards GRPR in PC3 and LNCaP xenografts with similar uptake profiles for both BBN2 TMs in dynamic PET experiments. Clearance occurred exclusively through renal elimination. A tremendously increased in vivo metabolic stability of the BBN2 TMs was observed compared to their counterparts without E5B9. Both monomeric and dimeric BBN2 TMs represent novel and promising immunotheranostic tools for application in prostate cancer with exceptionally high in vivo metabolic stability. Full article