Trained Immunity-Promoting Nanobiologic Therapy Suppresses Tumor Growth and Potentiates Checkpoint Inhibition.

Trained immunity, a functional state of myeloid cells, has been proposed as a compelling immune-oncological target. Its efficient induction requires direct engagement of myeloid progenitors in the bone marrow. For this purpose, we developed a bone marrow-avid nanobiologic platform designed specifically to induce trained immunity. We established the potent anti-tumor capabilities of our lead candidate MTP10-HDL in a B16F10 mouse melanoma model. These anti-tumor effects result from trained immunity-induced myelopoiesis caused by epigenetic rewiring of multipotent progenitors in the bone marrow, which overcomes the immunosuppressive tumor microenvironment. Furthermore, MTP10-HDL nanotherapy potentiates checkpoint inhibition in this melanoma model refractory to anti-PD-1 and anti-CTLA-4 therapy. Finally, we determined MTP10-HDL's favorable biodistribution and safety profile in non-human primates. In conclusion, we show that rationally designed nanobiologics can promote trained immunity and elicit a durable anti-tumor response either as a monotherapy or in combination with checkpoint inhibitor drugs.

Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping.

PURPOSE: This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. METHODS: An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). RESULTS: ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. CONCLUSION: This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging.

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis.

Background: Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19. Methods: [18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann-Whitney U-tests and logistic regression. Features were retained if the P-value was <0.00053, the AUC was >0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers. Results: The Mann-Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax ) showed a high area under the curve (AUC) and accuracy with small P-values (<0.00053), but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, the Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier to individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively). Conclusion: Using radiomic features may prove useful in identifying individuals with CS. Some features showed promising results in differentiating between PC and CS. By automating the analysis, the patient management process can be accelerated and improved.

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images.

The current standard for measuring coronary artery calcification to determine the extent of atherosclerosis is by calculating the Agatston score from computed tomography (CT). However, the Agatston score disregards pixel values less than 130 Hounsfield Units (HU) and calcium regions less than 1 mm2. Due to this thresholding, the score is not sensitive to small, weakly attenuating regions of calcium deposition and may not detect nascent micro-calcification. A recently proposed metric called the spatially weighted calcium score (SWCS) also utilizes CT but does not include a threshold for HU and does not require elevated signals in contiguous pixels. Thus, the SWCS is sensitive to weakly attenuating, smaller calcium deposits and may improve the measurement of coronary heart disease risk. Currently, the SWCS is underutilized owing to the added computational complexity. To promote translation of the SWCS into clinical research and reliable, repeatable computation of the score, the aim of this study was to develop a semi-automatic graphical tool that calculates both the SWCS and the Agatston score. The program requires gated cardiac CT scans with a calcium hydroxyapatite phantom in the field of view. The phantom allows for deriving a weighting function, from which each pixel's weight is adjusted, allowing for the mitigation of signal variations and variability between scans. With all three anatomical views visible simultaneously, the user traces the course of the four main coronary arteries by placing points or regions of interest. Features such as scroll-to-zoom, double-click to delete, and brightness/contrast adjustment, along with written guidance at every step, make the program user-friendly and easy to use. Once tracing the arteries is complete, the program generates reports, which include the scores and snapshots of any visible calcium. The SWCS may reveal the presence of subclinical disease, which may be used for early intervention and lifestyle changes.

Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis.

BACKGROUND: The aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS). METHODS: Subjects were classified into active cardiac sarcoidosis (CSactive) and inactive cardiac sarcoidosis (CSinactive) based on PET-CMR imaging. CSactive was classified as featuring patchy [18F]fluorodeoxyglucose ([18F]FDG) uptake on PET and presence of late gadolinium enhancement (LGE) on CMR, while CSinactive was classified as featuring no [18F]FDG uptake in the presence of LGE on CMR. Among those screened, thirty CSactive and thirty-one CSinactive patients met these criteria. A total of 94 radiomic features were subsequently extracted using PyRadiomics. The values of individual features were compared between CSactive and CSinactive using the Mann-Whitney U test. Subsequently, machine learning (ML) approaches were tested. ML was applied to two sub-sets of radiomic features (signatures A and B) that were selected by logistic regression and PCA, respectively. RESULTS: Univariate analysis of individual features showed no significant differences. Of all features, gray level co-occurrence matrix (GLCM) joint entropy had a good area under the curve (AUC) and accuracy with the smallest confidence interval, suggesting it may be a good target for further investigation. Some ML classifiers achieved reasonable discrimination between CSactive and CSinactive patients. With signature A, support vector machine and k-neighbors showed good performance with AUC (0.77 and 0.73) and accuracy (0.67 and 0.72), respectively. With signature B, decision tree demonstrated AUC and accuracy around 0.7; Conclusion: CMR radiomic analysis in CS provides promising results to distinguish patients with active and inactive disease.

Multiparametric Immunoimaging Maps Inflammatory Signatures in Murine Myocardial Infarction Models.

In the past 2 decades, research on atherosclerotic cardiovascular disease has uncovered inflammation to be a key driver of the pathophysiological process. A pressing need therefore exists to quantitatively and longitudinally probe inflammation, in preclinical models and in cardiovascular disease patients, ideally using non-invasive methods and at multiple levels. Here, we developed and employed in vivo multiparametric imaging approaches to investigate the immune response following myocardial infarction. The myocardial infarction models encompassed either transient or permanent left anterior descending coronary artery occlusion in C57BL/6 and Apoe-/-mice. We performed nanotracer-based fluorine magnetic resonance imaging and positron emission tomography (PET) imaging using a CD11b-specific nanobody and a C-C motif chemokine receptor 2-binding probe. We found that immune cell influx in the infarct was more pronounced in the permanent occlusion model. Further, using 18F-fluorothymidine and 18F-fluorodeoxyglucose PET, we detected increased hematopoietic activity after myocardial infarction, with no difference between the models. Finally, we observed persistent systemic inflammation and exacerbated atherosclerosis in Apoe-/- mice, regardless of which infarction model was used. Taken together, we showed the strengths and capabilities of multiparametric imaging in detecting inflammatory activity in cardiovascular disease, which augments the development of clinical readouts.

Exploring the Utility of Radiomic Feature Extraction to Improve the Diagnostic Accuracy of Cardiac Sarcoidosis Using FDG PET.

Background: This study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS). Methods: Forty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney U-test and a logistic regression classifier were used to compare the individual features of the study groups. Results: For segmentation A, the SUVmin had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value < 0.00061 (after Bonferroni correction), AUC >0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy (0.9 and 0.8, respectively). Conclusions: Radiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols.

Pulmonary Artery 18F-Fluorodeoxyglucose Uptake by PET/CMR as a Marker of Pulmonary Hypertension in Sarcoidosis.

OBJECTIVES: This study investigated whether pulmonary artery (PA) 18F-FDG uptake is associated with hypertension, and if it correlates to elevated pulmonary pressures. BACKGROUND: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography or cardiac magnetic resonance (CMR) has been used to assess inflammation mostly in large arteries of the systemic circulation. Much less is known about inflammation of the vasculature of the pulmonary system and its relationship to pulmonary hypertension (PH). METHODS: In a single-center cohort of 175 patients with suspected cardiac sarcoidosis, who underwent hybrid thoracic PET/CMR, 18F-FDG uptake in the PA was quantified according to maximum standardized uptake value (SUVmax) and target-to-background ratio (TBR) and compared with available results from right heart catheterization (RHC) or transthoracic echocardiography (TTE). RESULTS: Thirty-three subjects demonstrated clear 18F-FDG uptake in the PA wall. In the subgroup of patients who underwent RHC (n = 10), the mean PA pressure was significantly higher in the group with PA 18F-FDG uptake compared with the group without uptake (34.4 ± 7.2 mm Hg vs 25.6 ± 9.3 mm Hg; P = 0.003), and 9 (90%) patients with PA 18F-FDG uptake had PH when a mean PA pressure cutoff of 25 mm Hg was used compared with 18 (45%) in the nonuptake group (P < 0.05). In the subgroup that underwent TTE, signs of PH were present in a significantly higher number of patients with PA 18F-FDG uptake (14 [51.9%] vs 37 [29.8%]; P < 0.05). Qualitative assessment of 18F-FDG uptake in the PA wall showed a sensitivity of 33% and specificity of 96% for separating patients with PH based on RHC-derived PA pressures. SUVmax and TBR in the PA wall correlated with PA pressure derived from RHC and/or TTE. CONCLUSIONS: We demonstrate that 18F-FDG uptake by PET/CMR in the PA is associated with PH and that its intensity correlates with PA pressure.

Systematically evaluating DOTATATE and FDG as PET immuno-imaging tracers of cardiovascular inflammation.

In recent years, cardiovascular immuno-imaging by positron emission tomography (PET) has undergone tremendous progress in preclinical settings. Clinically, two approved PET tracers hold great potential for inflammation imaging in cardiovascular patients, namely FDG and DOTATATE. While the former is a widely applied metabolic tracer, DOTATATE is a relatively new PET tracer targeting the somatostatin receptor 2 (SST2). In the current study, we performed a detailed, head-to-head comparison of DOTATATE-based radiotracers and [18F]F-FDG in mouse and rabbit models of cardiovascular inflammation. For mouse experiments, we labeled DOTATATE with the long-lived isotope [64Cu]Cu to enable studying the tracer's mode of action by complementing in vivo PET/CT experiments with thorough ex vivo immunological analyses. For translational PET/MRI rabbit studies, we employed the more widely clinically used [68Ga]Ga-labeled DOTATATE, which was approved by the FDA in 2016. DOTATATE's pharmacokinetics and timed biodistribution were determined in control and atherosclerotic mice and rabbits by ex vivo gamma counting of blood and organs. Additionally, we performed in vivo PET/CT experiments in mice with atherosclerosis, mice subjected to myocardial infarction and control animals, using both [64Cu]Cu-DOTATATE and [18F]F-FDG. To evaluate differences in the tracers' cellular specificity, we performed ensuing ex vivo flow cytometry and gamma counting. In mice subjected to myocardial infarction, in vivo [64Cu]Cu-DOTATATE PET showed higher differential uptake between infarcted (SUVmax 1.3, IQR, 1.2-1.4, N = 4) and remote myocardium (SUVmax 0.7, IQR, 0.5-0.8, N = 4, p = 0.0286), and with respect to controls (SUVmax 0.6, IQR, 0.5-0.7, N = 4, p = 0.0286), than [18F]F-FDG PET. In atherosclerotic mice, [64Cu]Cu-DOTATATE PET aortic signal, but not [18F]F-FDG PET, was higher compared to controls (SUVmax 1.1, IQR, 0.9-1.3 and 0.5, IQR, 0.5-0.6, respectively, N = 4, p = 0.0286). In both models, [64Cu]Cu-DOTATATE demonstrated preferential accumulation in macrophages with respect to other myeloid cells, while [18F]F-FDG was taken up by macrophages and other leukocytes. In a translational PET/MRI study in atherosclerotic rabbits, we then compared [68Ga]Ga-DOTATATE and [18F]F-FDG for the assessment of aortic inflammation, combined with ex vivo radiometric assays and near-infrared imaging of macrophage burden. Rabbit experiments showed significantly higher aortic accumulation of both [68Ga]Ga-DOTATATE and [18F]F-FDG in atherosclerotic (SUVmax 0.415, IQR, 0.338-0.499, N = 32 and 0.446, IQR, 0.387-0.536, N = 27, respectively) compared to control animals (SUVmax 0.253, IQR, 0.197-0.285, p = 0.0002, N = 10 and 0.349, IQR, 0.299-0.423, p = 0.0159, N = 11, respectively). In conclusion, we present a detailed, head-to-head comparison of the novel SST2-specific tracer DOTATATE and the validated metabolic tracer [18F]F-FDG for the evaluation of inflammation in small animal models of cardiovascular disease. Our results support further investigations on the use of DOTATATE to assess cardiovascular inflammation as a complementary readout to the widely used [18F]F-FDG.

The influence of lack of reference conditions on dosimetry in pre-clinical radiotherapy with medium energy x-ray beams.

Despite well-established dosimetry in clinical radiotherapy, dose measurements in pre-clinical and radiobiology studies are frequently inadequate, thus undermining the reliability and reproducibility of published findings. The lack of suitable dosimetry protocols, coupled with the increasing complexity of pre-clinical irradiation platforms, undermines confidence in preclinical studies and represents a serious obstacle in the translation to clinical practice. To accurately measure output of a pre-clinical radiotherapy unit, appropriate Codes of Practice (CoP) for medium energy x-rays needs to be employed. However, determination of absorbed dose to water (Dw) relies on application of backscatter factor (Bw) employing in-air method or carrying out in-phantom measurement at the reference depth of 2 cm in a full backscatter (i.e. 30 × 30 × 30 cm3) condition. Both of these methods require thickness of at least 30 cm of underlying material, which are never fulfilled in typical pre-clinical irradiations. This work is focused on evaluation the effects of the lack of recommended reference conditions in dosimetry measurements for pre-clinical settings and is aimed at extending the recommendations of the current CoP to practical experimental conditions and highlighting the potential impact of the lack of correct backscatter considerations on radiobiological studies.

Tumor Targeting by αvß3-Integrin-Specific Lipid Nanoparticles Occurs via Phagocyte Hitchhiking.

Although the first nanomedicine was clinically approved more than two decades ago, nanoparticles' (NP) in vivo behavior is complex and the immune system's role in their application remains elusive. At present, only passive-targeting nanoformulations have been clinically approved, while more complicated active-targeting strategies typically fail to advance from the early clinical phase stage. This absence of clinical translation is, among others, due to the very limited understanding for in vivo targeting mechanisms. Dynamic in vivo phenomena such as NPs' real-time targeting kinetics and phagocytes' contribution to active NP targeting remain largely unexplored. To better understand in vivo targeting, monitoring NP accumulation and distribution at complementary levels of spatial and temporal resolution is imperative. Here, we integrate in vivo positron emission tomography/computed tomography imaging with intravital microscopy and flow cytometric analyses to study αvß3-integrin-targeted cyclic arginine-glycine-aspartate decorated liposomes and oil-in-water nanoemulsions in tumor mouse models. We observed that ligand-mediated accumulation in cancerous lesions is multifaceted and identified "NP hitchhiking" with phagocytes to contribute considerably to this intricate process. We anticipate that this understanding can facilitate rational improvement of nanomedicine applications and that immune cell-NP interactions can be harnessed to develop clinically viable nanomedicine-based immunotherapies.

Multimodal imaging of bacterial-host interface in mice and piglets with Staphylococcus aureus endocarditis.

Acute bacterial endocarditis is a rapid, difficult to manage, and frequently lethal disease. Potent antibiotics often cannot efficiently kill Staphylococcus aureus that colonizes the heart's valves. S. aureus relies on virulence factors to evade therapeutics and the host's immune response, usurping the host's clotting system by activating circulating prothrombin with staphylocoagulase and von Willebrand factor-binding protein. An insoluble fibrin barrier then forms around the bacterial colony, shielding the pathogen from immune cell clearance. Targeting virulence factors may provide previously unidentified avenues to better diagnose and treat endocarditis. To tap into this unused therapeutic opportunity, we codeveloped therapeutics and multimodal molecular imaging to probe the host-pathogen interface. We introduced and validated a family of small-molecule optical and positron emission tomography (PET) reporters targeting active thrombin in the fibrin-rich environment of bacterial colonies. The imaging agents, based on the clinical thrombin inhibitor dabigatran, are bound to heart valve vegetations in mice. Using optical imaging, we monitored therapy with antibodies neutralizing staphylocoagulase and von Willebrand factor-binding protein in mice with S. aureus endocarditis. This treatment deactivated bacterial defenses against innate immune cells, decreased in vivo imaging signal, and improved survival. Aortic or tricuspid S. aureus endocarditis in piglets was also successfully imaged with clinical PET/magnetic resonance imaging. Our data map a route toward adjuvant immunotherapy for endocarditis and provide efficient tools to monitor this drug class for infectious diseases.

Imaging-assisted nanoimmunotherapy for atherosclerosis in multiple species.

Nanomedicine research produces hundreds of studies every year, yet very few formulations have been approved for clinical use. This is due in part to a reliance on murine studies, which have limited value in accurately predicting translational efficacy in larger animal models and humans. Here, we report the scale-up of a nanoimmunotherapy from mouse to large rabbit and porcine atherosclerosis models, with an emphasis on the solutions we implemented to overcome production and evaluation challenges. Specifically, we integrated translational imaging readouts within our workflow to both analyze the nanoimmunotherapeutic's in vivo behavior and assess treatment response in larger animals. We observed our nanoimmunotherapeutic's anti-inflammatory efficacy in mice, as well as rabbits and pigs. Nanoimmunotherapy-mediated reduction of inflammation in the large animal models halted plaque progression, supporting the approach's translatability and potential to acutely treat atherosclerosis.

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data.

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, 18F-FDG and 68Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions.