Magnetic Resonance Imaging Is More Sensitive Than PET for Detecting Treatment-Induced Cell Death-Dependent Changes in Glycolysis.

Metabolic imaging has been widely used to measure the early responses of tumors to treatment. Here, we assess the abilities of PET measurement of [18F]FDG uptake and MRI measurement of hyperpolarized [1-13C]pyruvate metabolism to detect early changes in glycolysis following treatment-induced cell death in human colorectal (Colo205) and breast adenocarcinoma (MDA-MB-231) xenografts in mice. A TRAIL agonist that binds to human but not mouse cells induced tumor-selective cell death. Tumor glycolysis was assessed by injecting [1,6-13C2]glucose and measuring 13C-labeled metabolites in tumor extracts. Injection of hyperpolarized [1-13C]pyruvate induced rapid reduction in lactate labeling. This decrease, which correlated with an increase in histologic markers of cell death and preceded decrease in tumor volume, reflected reduced flux from glucose to lactate and decreased lactate concentration. However, [18F]FDG uptake and phosphorylation were maintained following treatment, which has been attributed previously to increased [18F]FDG uptake by infiltrating immune cells. Quantification of [18F]FDG uptake in flow-sorted tumor and immune cells from disaggregated tumors identified CD11b+/CD45+ macrophages as the most [18F]FDG-avid cell type present, yet they represented <5% of the cells present in the tumors and could not explain the failure of [18F]FDG-PET to detect treatment response. MRI measurement of hyperpolarized [1-13C]pyruvate metabolism is therefore a more sensitive marker of the early decreases in glycolytic flux that occur following cell death than PET measurements of [18F]FDG uptake. SIGNIFICANCE: These findings demonstrate superior sensitivity of MRI measurement of hyperpolarized [1-13C]pyruvate metabolism versus PET measurement of 18F-FDG uptake for detecting early changes in glycolysis following treatment-induced tumor cell death

Assessing risks and risk management.

Risk management is presently possible to management by a number of techniques, whether it is personal, natural disaster, business, and computer risk. Risk management can be broken down even further to prevention, assessment, and contingency planning. Through research and development, this project details and tests a risk procedure that focuses on the previously mentioned tasks. This procedure will be tested with various on-campus buildings. Conclusions will be mentioned on the potential risks of the buildings

Product pricing at CGU Life

CGU LIFE is looking to sell universal life insurance in the structured settlements market. To properly price this product, the following tasks were completed: calculate pricing mortality for streamlined underwriting, manipulate COI rates and loadspread to attain desired profits, perform sensitivity analysis, and complete NAIC and nonforfeiture law testing. The steps taken, software used and problems encountered are described in this report

Global maps of the CRUST 2.0 crustal components stripped gravity disturbances

We use the CRUST 2.0 crustal model and the EGM08 geopotential model to compile global maps of the gravity disturbances corrected for the gravitational effects (attractions) of the topography and of the density contrasts of the oceans, sediments, ice, and the remaining crust down to the Moho discontinuity. Techniques for a spherical harmonic analysis of the gravity field are used to compute both the gravity disturbances and the topographic and bathymetric corrections with a spectral resolution complete to degree 180 of the spherical harmonics. The ice stripping correction is computed with a spectral resolution complete to degree 90. The sediment and consolidated crust stripping corrections are computed in spatial form by forward modeling their respective attractions. All data are evaluated on a 1 × 1 arc degree grid at the Earth's surface and provided in Data Sets S1–S5 in the auxiliary material for the scientific community for use in global geophysical studies. The complete crust-stripped gravity disturbances (globally having a range of 1050 mGal) contain the gravitational signal coming dominantly from the global mantle lithosphere (upper mantle) morphology and density composition and partially from the sublithospheric density heterogeneities. Large errors are expected because of uncertainties of the CRUST 2.0 model (i.e., deviations of the CRUST 2.0 model density from the real Earth's crustal density heterogeneities and the Moho relief uncertainties).Remote SensingAerospace Engineerin

[18F]fluoroethyltyrosine-induced Cerenkov Luminescence Improves Image-Guided Surgical Resection of Glioma.

The extent of surgical resection is significantly correlated with outcome in glioma; however, current intraoperative navigational tools are useful only in a subset of patients. We show here that a new optical intraoperative technique, Cerenkov luminescence imaging (CLI) following intravenous injection of O‑(2-[18F]fluoroethyl)-L-tyrosine (FET), can be used to accurately delineate glioma margins, performing better than the current standard of fluorescence imaging with 5-aminolevulinic acid (5-ALA). Methods: Rats implanted orthotopically with U87, F98 and C6 glioblastoma cells were injected with FET and 5-aminolevulinic acid (5-ALA). Positive and negative tumor regions on histopathology were compared with CL and fluorescence images. The capability of FET CLI and 5-ALA fluorescence imaging to detect tumor was assessed using receptor operator characteristic curves and optimal thresholds (CLIOptROC and 5-ALAOptROC) separating tumor from healthy brain tissue were determined. These thresholds were used to guide prospective tumor resections, where the presence of tumor cells in the resected material and in the remaining brain were assessed by Ki-67 staining. Results: FET CLI signal was correlated with signal in preoperative PET images (y = 1.06x - 0.01; p 92% and specificity >91%, and resulted in a more complete tumor resection. Conclusion: FET CLI can be used to accurately delineate glioblastoma tumor margins, performing better than the current standard of fluorescence imaging following 5-ALA administration, and is therefore a promising technique for clinical translation.Cancer Research U

Noninvasive stratification of colon cancer by multiplex PET imaging

Purpose: The current approach for molecular subtyping of colon cancer relies on gene expression profiling, which is invasive and has limited ability to reveal dynamics and spatial heterogeneity. Molecular imaging techniques, such as PET, present a noninvasive alternative for visualizing biological information from tumors. However, the factors influencing PET imaging phenotype, the suitable PET radiotracers for differentiating tumor subtypes, and the relationship between PET phenotypes and tumor genotype or gene expression–based subtyping remain unknown. Experimental Design: In this study, we conducted 126 PET scans using four different metabolic PET tracers, [18F]fluorodeoxy-D-glucose ([18F]FDG), O-(2-[18F]fluoroethyl)-l-tyrosine ([18F]FET), 3′-deoxy-3′-[18F]fluorothymidine ([18F]FLT), and [11C]acetate ([11C]ACE), using a spectrum of five preclinical colon cancer models with varying genetics (BMT, AKPN, AK, AKPT, KPN), at three sites (subcutaneous, orthograft, autochthonous) and at two tumor stages (primary vs. metastatic). Results: The results demonstrate that imaging signatures are influenced by genotype, tumor environment, and stage. PET imaging signatures exhibited significant heterogeneity, with each cancer model displaying distinct radiotracer profiles. Oncogenic Kras and Apc loss showed the most distinctive imaging features, with [18F]FLT and [18F]FET being particularly effective, respectively. The tissue environment notably impacted [18F]FDG uptake, and in a metastatic model, [18F]FET demonstrated higher uptake. Conclusions: By examining factors contributing to PET-imaging phenotype, this study establishes the feasibility of noninvasive molecular stratification using multiplex radiotracer PET. It lays the foundation for further exploration of PET-based subtyping in human cancer, thereby facilitating noninvasive molecular diagnosis

Figure 5 from Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging

PET imaging phenotypic difference between primary and metastatic tumors. A, The generation of the KrasG12D/+ Trp53fl/fl Rosa26N1icd/+ (KPN) and KPN liver metastasis organoid lines and subsequent implantation. One pair of lines, generated from a matched mouse primary tumor and liver metastasis, which were then propagated and injected subcutaneously into recipient mice (n = 5). B, Transverse and coronal PET/MRI slice images showing uptake of four PET tracers ([18F]FDG, [18F]FET, [18F]FLT, [18F]ACE) in subcutaneously implanted KPN primary and KPN liver metastasis organoids. KPN primary tumor-bearing mice are the same four PET ([18F]FDG, [18F]FET, [18F]FLT, [18F]ACE) images (primary) as displayed in Figs. 2B and 4B and D. C, Standard uptake peak values (SUVpeak) PET quantification from images in B. Sample size (n) is displayed on the bars. Error bars represent SD. Data compared using unpaired t test. Details of all mice used in these studies are presented in Supplementary Table S1. D, Representative GLUT-1 IHC and Lat-1/Slc7a5 ISH. Black scale bars represent 50 μm (*, P P A, Created with BioRender.com.)</p

Table S1 from Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging

Models Overview</p

Figure 3 from Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging

PET imaging can distinguish different colon subcutaneous organoid cancer models and individual driver genes. A, The data processing workflow for comparing PET radiotracer discriminatory power and the model/gene uniqueness. B, Separation matrix and statistics of the area under the ROC curves for each tracer and model. C, Red highlighted box showing boxplot and ROC curves for [18F]FDG in the BMT (n = 6 subcutaneous organoid allografts) compared with other models (n = 19), each point represents a mouse. Numbers inside bars show sample size, n. Data compared using unpaired t test. D, Separation matrix and statistics of the area under the ROC curves for each tracer and gene. Tgfbr1/Alk5 fl/fl and Tgfbr2 fl/fl are combined as TGFb for this analysis. E, Red highlighted box showing boxplot and ROC curves for [18F]FLT in the Kras (n = 18) compared to other subcutaneous models (n = 6), each point represents a mouse. Numbers inside bars show n. Data compared using unpaired t test. Error bars in C and D represent SD. *, P P P t tests and AUC ROC. Each analysis stands on its own; no multiple comparison testing was used. See extended datasets in Supplementary Fig. S4. (A, Created with BioRender.com.)</p

Figure 2 from Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging

Distinct intermodel heterogeneity in PET imaging signatures. A, In the experimental imaging protocol, five colon cancer organoid models and four PET tracers were used to determine imaging signatures. Details of all mice used in these studies are presented in Supplementary Table S1. B, Representative transverse PET images from each model and tracer. The [18F]FDG PET/MR images of the KPN subcutaneous model are reproduced again in Figs. 4B and D and 5B for comparison against other tumors at different sites and stages. C, Imaging signature heatmap showing mean tracer uptake, models with highest tracer update highlighted with black outline (representation of the data matrix analyzed with two-way ANOVA). D, Correlation matrix of each tracer uptake based on Pearson correlation coefficient. E, Heatmap illustrating correlation of PET tracer uptake with gene expression in the Molecular Signatures Database (MSigDB) hallmark dataset, sorted by hierarchal clustering. (A, Created with BioRender.com.)</p