Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. PURPOSE: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. METHOD: Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10 min static FDG-PET acquisition, 50 min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. RESULTS: ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMean and SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). CONCLUSION: ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

Evaluation of Copanlisib in Combination with Eribulin in Triple-negative Breast Cancer Patient-derived Xenograft Models.

The PI3K pathway regulates essential cellular functions and promotes chemotherapy resistance. Activation of PI3K pathway signaling is commonly observed in triple-negative breast cancer (TNBC). However previous studies that combined PI3K pathway inhibitors with taxane regimens have yielded inconsistent results. We therefore set out to examine whether the combination of copanlisib, a clinical grade pan-PI3K inhibitor, and eribulin, an antimitotic chemotherapy approved for taxane-resistant metastatic breast cancer, improves the antitumor effect in TNBC. A panel of eight TNBC patient-derived xenograft (PDX) models was tested for tumor growth response to copanlisib and eribulin, alone or in combination. Treatment-induced signaling changes were examined by reverse phase protein array, immunohistochemistry (IHC) and 18F-fluorodeoxyglucose PET (18F-FDG PET). Compared with each drug alone, the combination of eribulin and copanlisib led to enhanced tumor growth inhibition, which was observed in both eribulin-sensitive and -resistant TNBC PDX models, regardless of PI3K pathway alterations or PTEN status. Copanlisib reduced PI3K signaling and enhanced eribulin-induced mitotic arrest. The combination enhanced induction of apoptosis compared with each drug alone. Interestingly, eribulin upregulated PI3K pathway signaling in PDX tumors, as demonstrated by increased tracer uptake by 18F-FDG PET scan and AKT phosphorylation by IHC. These changes were inhibited by the addition of copanlisib. These data support further clinical development for the combination of copanlisib and eribulin and led to a phase I/II trial of copanlisib and eribulin in patients with metastatic TNBC. SIGNIFICANCE: In this research, we demonstrated that the pan-PI3K inhibitor copanlisib enhanced the cytotoxicity of eribulin in a panel of TNBC PDX models. The improved tumor growth inhibition was irrespective of PI3K pathway alteration and was corroborated by the enhanced mitotic arrest and apoptotic induction observed in PDX tumors after combination therapy compared with each drug alone. These data provide the preclinical rationale for the clinical testing in TNBC.

TMEM135 links peroxisomes to the regulation of brown fat mitochondrial fission and energy homeostasis.

Mitochondrial morphology, which is controlled by mitochondrial fission and fusion, is an important regulator of the thermogenic capacity of brown adipocytes. Adipose-specific peroxisome deficiency impairs thermogenesis by inhibiting cold-induced mitochondrial fission due to decreased mitochondrial membrane content of the peroxisome-derived lipids called plasmalogens. Here, we identify TMEM135 as a critical mediator of the peroxisomal regulation of mitochondrial fission and thermogenesis. Adipose-specific TMEM135 knockout in mice blocks mitochondrial fission, impairs thermogenesis, and increases diet-induced obesity and insulin resistance. Conversely, TMEM135 overexpression promotes mitochondrial division, counteracts obesity and insulin resistance, and rescues thermogenesis in peroxisome-deficient mice. Mechanistically, thermogenic stimuli promote association between peroxisomes and mitochondria and plasmalogen-dependent localization of TMEM135 in mitochondria, where it mediates PKA-dependent phosphorylation and mitochondrial retention of the fission factor Drp1. Together, these results reveal a previously unrecognized inter-organelle communication regulating mitochondrial fission and energy homeostasis and identify TMEM135 as a potential target for therapeutic activation of BAT.

Discovery and Targeting of a Noncanonical Mechanism of Sarcoma Resistance to ADI-PEG20 Mediated by the Microenvironment.

PURPOSE: Many cancers lack argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme of arginine biosynthesis. This deficiency causes arginine auxotrophy, targetable by extracellular arginine-degrading enzymes such as ADI-PEG20. Long-term tumor resistance has thus far been attributed solely to ASS1 reexpression. This study examines the role of ASS1 silencing on tumor growth and initiation and identifies a noncanonical mechanism of resistance, aiming to improve clinical responses to ADI-PEG20. EXPERIMENTAL DESIGN: Tumor initiation and growth rates were measured for a spontaneous Ass1 knockout (KO) murine sarcoma model. Tumor cell lines were generated, and resistance to arginine deprivation therapy was studied in vitro and in vivo. RESULTS: Conditional Ass1 KO affected neither tumor initiation nor growth rates in a sarcoma model, contradicting the prevalent idea that ASS1 silencing confers a proliferative advantage. Ass1 KO cells grew robustly through arginine starvation in vivo, while ADI-PEG20 remained completely lethal in vitro, evidence that pointed toward a novel mechanism of resistance mediated by the microenvironment. Coculture with Ass1-competent fibroblasts rescued growth through macropinocytosis of vesicles and/or cell fragments, followed by recycling of protein-bound arginine through autophagy/lysosomal degradation. Inhibition of either macropinocytosis or autophagy/lysosomal degradation abrogated this growth support effect in vitro and in vivo. CONCLUSIONS: Noncanonical, ASS1-independent tumor resistance to ADI-PEG20 is driven by the microenvironment. This mechanism can be targeted by either the macropinocytosis inhibitor imipramine or the autophagy inhibitor chloroquine. These safe, widely available drugs should be added to current clinical trials to overcome microenvironmental arginine support of tumors and improve patient outcomes.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

An Online Repository for Pre-Clinical Imaging Protocols (PIPs).

Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.

Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials.

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom.

Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.

Positron Emission Tomography Imaging of Vessel Wall Matrix Metalloproteinase Activity in Abdominal Aortic Aneurysm.

BACKGROUND: Matrix metalloproteinases (MMPs) play a key role in the pathogenesis of abdominal aortic aneurysm (AAA). Imaging aortic MMP activity, especially using positron emission tomography to access high sensitivity, quantitative data, could potentially improve AAA risk stratification. Here, we describe the design, synthesis, characterization, and evaluation in murine AAA and human aortic tissue of a first-in-class MMP-targeted positron emission tomography radioligand, 64Cu-RYM2. METHODS: The broad spectrum MMP inhibitor, RYM2 was synthetized, and its potency as an MMP inhibitor was evaluated by a competitive inhibition assay. Toxicology studies were performed. Tracer biodistribution was evaluated in a murine model of AAA induced by angiotensin II infusion in Apolipoprotein E-deficient mice. 64Cu-RYM2 binding to normal and aneurysmal human aortic tissues was assessed by autoradiography. RESULTS: RYM2 functioned as an MMP inhibitor with nanomolar affinities. Toxicology studies showed no adverse reaction in mice. Upon radiolabeling with Cu-64, the resulting tracer was stable in murine and human blood in vitro. Biodistribution and metabolite analysis in mice showed rapid renal clearance and acceptable in vivo stability. In vivo positron emission tomography/computed tomography in a murine model of AAA showed a specific aortic signal, which correlated with ex vivo measured MMP activity and Cd68 gene expression. 64Cu-RYM2 specifically bound to normal and aneurysmal human aortic tissues in correlation with MMP activity. CONCLUSIONS: 64Cu-RYM2 is a first-in-class MMP-targeted positron emission tomography tracer with favorable stability, biodistribution, performance in preclinical AAA, and importantly, specific binding to human tissues. These data set the stage for 64Cu-RYM2-based translational imaging studies of vessel wall MMP activity, and indirectly, inflammation, in AAA.

First-in-Humans Evaluation of Safety and Dosimetry of 64Cu-LLP2A for PET Imaging.

There remains an unmet need for molecularly targeted imaging agents for multiple myeloma (MM). The integrin very late antigen 4 (VLA4), is differentially expressed in malignant MM cells and in pathogenic inflammatory microenvironmental cells. [64Cu]Cu-CB-TE1A1P-LLP2A (64Cu-LLP2A) is a VLA4-targeted, high-affinity radiopharmaceutical with promising utility for managing patients diagnosed with MM. Here, we evaluated the safety and human radiation dosimetry of 64Cu-LLP2A for potential use in MM patients. Methods: A single-dose [natCu]Cu-LLP2A (Cu-LLP2A) tolerability and toxicity study was performed on CD-1 (Hsd:ICR) male and female mice. 64Cu-LLP2A was synthesized in accordance with good-manufacturing-practice-compliant procedures. Three MM patients and six healthy participants underwent 64Cu-LLP2A-PET/CT or PET/MRI at up to 3 time points to help determine tracer biodistribution, pharmacokinetics, and radiation dosimetry. Time-activity curves were plotted for each participant. Mean organ-absorbed doses and effective doses were calculated using the OLINDA software. Tracer bioactivity was evaluated via cell-binding assays, and metabolites from human blood samples were analyzed with analytic radio-high-performance liquid chromatography. When feasible, VLA4 expression was evaluated in the biopsy tissues using 14-color flow cytometry. Results: A 150-fold mass excess of the desired imaging dose was tolerated well in male and female CD-1 mice (no observed adverse effect level). Time-activity curves from human imaging data showed rapid tracer clearance from blood via the kidneys and bladder. The effective dose of 64Cu-LLP2A in humans was 0.036 ± 0.006 mSv/MBq, and the spleen had the highest organ uptake, 0.142 ± 0.034 mSv/MBq. Among all tissues, the red marrow demonstrated the highest residence time. Image quality analysis supports an early imaging time (4-5 h after injection of the radiotracer) as optimal. Cell studies showed statistically significant blocking for the tracer produced for all human studies (82.42% ± 13.47%). Blood metabolism studies confirmed a stable product peak (>90%) up to 1 h after injection of the radiopharmaceutical. No clinical or laboratory adverse events related to 64Cu-LLP2A were observed in the human participants. Conclusion: 64Cu-LLP2A exhibited a favorable dosimetry and safety profile for use in humans.

Pilot Study: PARP1 Imaging in Advanced Prostate Cancer.

PURPOSE: PARP inhibitor (PARPi) therapy is approved for patients with metastatic castration-resistant prostate cancer (mCRPC) and homologous recombination repair (HRR) genomic aberrations. However, only a fraction of patients with BRCA1/2 mutations respond to PARPi therapy. In this pilot study, we assess PARP-1 expression in prostate cancer patients with and without HRR genomic alternations using a novel PARP-based imaging agent. PROCEDURES: Nine advanced prostate cancer patients were studied with PET/CT and [18F]FluorThanatrace (FTT), an analogue of the PARPi rucaparib. Images were analyzed using maximum standardized uptake values (SUVmax). PARP expression was assessed by immunohistochemistry (IHC) when feasible (n = 4). RESULTS: We found great variability in FTT uptake (SUVmax range: 2.3-15.4). Patients with HRR mutations had a significantly higher SUVmax (p = 0.0379) than patients with non-HRR mutations although there was an overlap in FTT uptake between groups. Three patients without HRR and one with HRR mutations had similarly high PARP1 IHC expression. CONCLUSIONS: FTT-PET/CT may serve as an alternate biomarker for PARP1 expression and a potential method for PARPi treatment selection.

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.

PURPOSE: We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. METHODS: TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. RESULTS: Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. CONCLUSIONS: We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

Multi-band echo-planar spectroscopic imaging of hyperpolarized 13 C probes in a compact preclinical PET/MR scanner.

PURPOSE: Hyperpolarized (HP) 13 C MRI has enabled real-time imaging of specific enzyme-catalyzed metabolic reactions, but advanced pulse sequences are necessary to capture the dynamic, localized metabolic information. Herein we describe the design, implementation, and testing of a rapid and efficient HP 13 C pulse sequence strategy on a cryogen-free simultaneous positron emission tomography/MR molecular imaging platform with compact footprint. METHODS: We developed an echo planar spectroscopic imaging pulse sequence incorporating multi-band spectral-spatial radiofrequency (SSRF) pulses for spatially coregistered excitation of 13 C metabolites with differential individual flip angles. Excitation profiles were measured in phantoms, and the SSRF-echo planar spectroscopic imaging sequence was tested in rats in vivo and compared to conventional echo planar spectroscopic imaging. The new sequence was applied for 2D dynamic metabolic imaging of HP [1-13 C]pyruvate and its molecular analog [1-13 C] α -ketobutyrate at a spatial resolution of 5 mm × 5 mm × 20 mm and temporal resolution of 4 s. We also obtained simultaneous 18 F-fluorodeoxyglucose positron emission tomography data for comparison with HP [1-13 C]pyruvate data acquired during the same scan session. RESULTS: Measured SSRF excitation profiles corresponded well to Bloch simulations. Multi-band SSRF excitation facilitated efficient sampling of the multi-spectral kinetics of [1-13 C]pyruvate and [1-13 C] α - ketobutyrate . Whereas high pyruvate to lactate conversion was observed in liver, corresponding reduction of α -ketobutyrate to [1-13 C] α -hydroxybutyrate ( α HB) was largely restricted to the kidneys and heart, consistent with the known expression pattern of lactate dehydrogenase B. CONCLUSION: Advanced 13 C SSRF imaging approaches are feasible on our compact positron emission tomography/MR platform, maximizing the potential of HP 13 C technology and facilitating direct comparison with positron emission tomography.

In vivo quantitative assessment of therapeutic response to bortezomib therapy in disseminated animal models of multiple myeloma with [18F]FDG and [64Cu]Cu-LLP2A PET.

BACKGROUND: Multiple myeloma (MM) is a disease of cancerous plasma cells in the bone marrow. Imaging-based timely determination of therapeutic response is critical for improving outcomes in MM patients. Very late antigen-4 (VLA4, CD49d/CD29) is overexpressed in MM cells. Here, we evaluated [18F]FDG and VLA4 targeted [64Cu]Cu-LLP2A for quantitative PET imaging in disseminated MM models of variable VLA4 expression, following bortezomib therapy. METHODS: In vitro and ex vivo VLA4 expression was evaluated by flow cytometry. Human MM cells, MM.1S-CG and U266-CG (C: luciferase and G: green fluorescent protein), were injected intravenously in NOD-SCID gamma mice. Tumor progression was monitored by bioluminescence imaging (BLI). Treatment group received bortezomib (1 mg/kg, twice/week) intraperitoneally. All cohorts (treated, untreated and no tumor) were longitudinally imaged with [18F]FDG (7.4-8.0 MBq) and [64Cu]Cu-LLP2A (2-3 MBq; Molar Activity: 44.14 ± 1.40 MBq/nmol) PET, respectively. RESULTS: Flow cytometry confirmed high expression of CD49d in U266 cells (> 99%) and moderate expression in MM.1S cells (~ 52%). BLI showed decrease in total body flux in treated mice. In MM.1S-CG untreated versus treated mice, [64Cu]Cu-LLP2A localized with a significantly higher SUVmean in spine (0.58 versus 0.31, p < 0.01) and femur (0.72 versus 0.39, p < 0.05) at week 4 post-tumor inoculation. There was a four-fold higher uptake of [64Cu]Cu-LLP2A (SUVmean) in untreated U266-CG mice compared to treated mice at 3 weeks post-treatment. Compared to [64Cu]Cu-LLP2A, [18F]FDG PET detected treatment-related changes at later time points. CONCLUSION: [64Cu]Cu-LLP2A is a promising tracer for timely in vivo assessment of therapeutic response in disseminated models of MM.

InVitro and In Vivo Investigation of S1PR1 Expression in the Central Nervous System Using [3H]CS1P1 and [11C]CS1P1.

Sphingosine-1-phosphate receptor 1 (S1PR1) is ubiquitously expressed among all tissues and plays key roles in many physiological and cellular processes. In the central nervous system (CNS), S1PR1 is expressed in different types of cells including neurons, astrocytes, and oligodendrocyte precursor cells. S1PR1 has been recognized as a novel therapeutic target in multiple sclerosis and other diseases. We previously reported a promising S1PR1-specific radioligand, [11C]CS1P1 (previously named [11C]TZ3321), which is under clinical investigation for human use. In the current study, we performed a detailed characterization of [3H]CS1P1 for its binding specificity to S1PR1 in CNS using autoradiography and immunohistochemistry in human and rat CNS tissues. Our data indicate that [3H]CS1P1 binds to S1PR1 in human frontal cortex tissue with a Kd of 3.98 nM and a Bmax of 172.5 nM. The distribution of [3H]CS1P1 in human and rat CNS tissues is consistent with the distribution of S1PR1 detected by immunohistochemistry studies. Our microPET studies of [11C]CS1P1 in a nonhuman primate (NHP) show a standardized uptake value of 2.4 in the NHP brain, with test-retest variability of 0.23% among six different NHPs. Radiometabolite analysis in the plasma samples of NHP and rat, as well as in rat brain samples, showed that [11C]CS1P1 was stable in vivo. Kinetic modeling studies using a two-compartment tissue model showed that the positron emission tomography (PET) data fit the model well. Overall, our study provides a detailed characterization of [3H]CS1P1 binding to S1PR1 in the CNS. Combined with our microPET studies in the NHP brain, our data suggest that [11C]CS1P1 is a promising radioligand for PET imaging of S1PR1 in the CNS.

Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary.

Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1-3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (-0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.

Mapping nephron mass in vivo using positron emission tomography.

Nephron number varies widely in humans. A low nephron endowment at birth or a loss of functioning nephrons is strongly linked to increased susceptibility to chronic kidney disease. In this work, we developed a contrast agent, radiolabeled cationic ferritin (RadioCF), to map functioning glomeruli in vivo in the kidney using positron emission tomography (PET). PET radiotracers can be detected in trace doses (<30 nmol), making them useful for rapid clinical translation. RadioCF is formed from cationic ferritin (CF) and with a radioisotope, Cu-64, incorporated into the ferritin core. We showed that RadioCF binds specifically to kidney glomeruli after intravenous injection in mice, whereas radiolabeled noncationic ferritin (RadioNF) and free Cu-64 do not. We then showed that RadioCF-PET can distinguish kidneys in healthy wild-type (WT) mice from kidneys in mice with oligosyndactylism (Os/+), a model of congenital hypoplasia and low nephron mass. The average standardized uptake value (SUV) measured by PET 90 min after injection was 21% higher in WT mice than in Os/+ mice, consistent with the higher glomerular density in WT mice. The difference in peak SUV from SUV at 90 min correlated with glomerular density in male mice from both WT and Os/+ cohorts (R2 = 0.98). Finally, we used RadioCF-PET to map functioning glomeruli in a donated human kidney. SUV within the kidney correlated with glomerular number (R2= 0.78) measured by CF-enhanced magnetic resonance imaging in the same locations. This work suggests that RadioCF-PET appears to accurately detect nephron mass and has the potential for clinical translation.

Galuminox: Preclinical validation of a novel PET tracer for non-invasive imaging of oxidative stress in vivo.

Overproduction of reactive oxygen species (ROS) is a well-established indicator of ongoing tissue inflammation. However, there is a scarcity of molecular imaging probes capable of providing noninvasive sensitive detection of ROS for allowing longitudinal studies of disease pathology and/or monitoring therapeutic efficacy of ROS scavengers. Herein, we report synthesis and chemical characterization of a novel metalloprobe, Galuminox, a moderately fluorescent agent that detects superoxide and hydrogen peroxide generation. Using live-cell fluorescence imaging analysis, Galuminox demonstrates ability to detect superoxide and monitor effects of ROS-attenuating agents, such as Carvedilol, Dexrazoxane, and mitoTempo in lung epithelial A549 cells. Furthermore, LPS stimulation of A549 cells that either express the mitochondria targeted fluorescent protein Keima or are stained with MitoSOX, a mitochondria-specific superoxide probe, indicates preferential co-localization of Galuminox with mitochondria producing elevated amounts of superoxide. Dynamic PET/CT scans 45 min post tail-vein administration of 68Ga-Galuminox show 4-fold higher uptake and stable retention in lungs of LPS treated mice compared to their saline-only treated counterparts. Post preclinical PET imaging, quantitative biodistribution studies also correlate with 4-fold higher retention of the radiotracer in lungs of LPS treated mice compared with their saline-only treated control counterparts. Consistent with these observations, lung cells isolated from LPS-treated mice demonstrated elevated ROS production deploying CellROX, the ROS probe. Finally, Galuminox uptake correlates with histological and physiological evidence of acute lung injury as evident by polynuclear infiltration, thickening of the alveolar epithelial membranes and increased bronchioalveolar lavage protein content. Taken collectively, these data indicate that 68Ga-Galuminox tracer uptake is a measure of ROS activity in acutely injured lungs and suggests its potential utility in monitoring oxidative stress in other diseases.

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging.

BACKGROUND: Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial. METHODS: Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images. FINDINGS: Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects. INTERPRETATION: Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.