Untargeted lipidomics reveals racial differences in lipid species among women.

Understanding the biological mechanisms underlying racial differences in diseases is crucial to developing targeted prevention and treatment. There is, however, limited knowledge of the impact of race on lipids. To address this, we performed comprehensive lipidomics analyses to evaluate racial differences in lipid species among 506 non-Hispanic White (NHW) and 163 non-Hispanic Black (NHB) women. Plasma lipidomic profiling quantified 982 lipid species. We used multivariable linear regression models, adjusted for confounders, to identify racial differences in lipid species and corrected for multiple testing using a Bonferroni-adjusted p-value < 10-5. We identified 248 lipid species that were significantly associated with race. NHB women had lower levels of several lipid species, most notably in the triacylglycerols sub-pathway (N = 198 out of 518) with 46 lipid species exhibiting an absolute percentage difference ≥ 50% lower in NHB compared with NHW women. We report several novel differences in lipid species between NHW and NHB women, which may underlie racial differences in health and have implications for disease prevention.

SHP2 as a primordial epigenetic enzyme expunges histone H3 pTyr-54 to amend androgen receptor homeostasis.

Mutations that decrease or increase the activity of the tyrosine phosphatase, SHP2 (encoded by PTPN11), promotes developmental disorders and several malignancies by varying phosphatase activity. We uncovered that SHP2 is a distinct class of an epigenetic enzyme; upon phosphorylation by the kinase ACK1/TNK2, pSHP2 was escorted by androgen receptor (AR) to chromatin, erasing hitherto unidentified pY54-H3 (phosphorylation of histones H3 at Tyr54) epigenetic marks to trigger a transcriptional program of AR. Noonan Syndrome with Multiple Lentigines (NSML) patients, SHP2 knock-in mice, and ACK1 knockout mice presented dramatic increase in pY54-H3, leading to loss of AR transcriptome. In contrast, prostate tumors with high pSHP2 and pACK1 activity exhibited progressive downregulation of pY54-H3 levels and higher AR expression that correlated with disease severity. Overall, pSHP2/pY54-H3 signaling acts as a sentinel of AR homeostasis, explaining not only growth retardation, genital abnormalities and infertility among NSML patients, but also significant AR upregulation in prostate cancer patients.

Baseline levels and longitudinal changes in plasma Aß42/40 among Black and white individuals.

Blood-based biomarkers of Alzheimer disease (AD) may facilitate testing of historically under-represented groups. The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to compare AD biomarkers in participants who identify their race as either Black or white. Plasma samples from 324 Black and 1,547 white participants underwent analysis with C2N Diagnostics' PrecivityAD test for Aß42 and Aß40. Compared to white individuals, Black individuals had higher average plasma Aß42/40 levels at baseline, consistent with a lower average level of amyloid pathology. Interestingly, this difference resulted from lower average levels of plasma Aß40 in Black participants. Despite the differences, Black and white individuals had similar longitudinal rates of change in Aß42/40, consistent with a similar rate of amyloid accumulation. Our results agree with multiple recent studies demonstrating a lower prevalence of amyloid pathology in Black individuals, and additionally suggest that amyloid accumulates consistently across both groups.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.

Objective.Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently.Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation.Main results.According to the NEMA IQ phantom simulation, FAST-PET's simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures.Significance.FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

Prostate-Specific Membrane Antigen-Targeted Imaging and Its Correlation with HOXB13 Expression.

Homeobox 13 (HOXB13) is an oncogenic transcription factor that directly regulates expression of folate hydrolase 1, which encodes prostate-specific membrane antigen (PSMA). HOXB13 is expressed in primary and metastatic prostate cancers (PCs) and promotes androgen-independent PC growth. Since HOXB13 promotes resistance to androgen receptor (AR)-targeted therapies and regulates the expression of folate hydrolase 1, we investigated whether SUVs on PSMA PET would correlate with HOXB13 expression. Methods: We analyzed 2 independent PC patient cohorts who underwent PSMA PET/CT for initial staging or for biochemical recurrence. In the discovery cohort, we examined the relationship between HOXB13, PSMA, and AR messenger RNA (mRNA) expression in prostate biopsy specimens from 179 patients who underwent PSMA PET/CT with 18F-piflufolastat. In the validation cohort, we confirmed the relationship between HOXB13, PSMA, and AR by comparing protein expression in prostatectomy and lymph node (LN) sections from 19 patients enrolled in 18F-rhPSMA-7.3 PET clinical trials. Correlation and association analyses were also used to confirm the relationship between the markers, LN positivity, and PSMA PET SUVs. Results: We observed a significant correlation between PSMA and HOXB13 mRNA (P < 0.01). The association between HOXB13 and 18F-piflufolastat SUVs was also significant (SUVmax, P = 0.0005; SUVpeak, P = 0.0006). Likewise, the PSMA SUVmax was significantly associated with the expression of HOXB13 protein in the 18F-rhPSMA-7.3 PET cohort (P = 0.008). Treatment-naïve patients with LN metastases demonstrated elevated HOXB13 and PSMA levels in their tumors as well as higher PSMA tracer uptake and low AR expression. Conclusion: Our findings demonstrate that HOXB13 correlates with PSMA expression and PSMA PET SUVs at the mRNA and protein levels. Our study suggests that the PSMA PET findings may reflect oncogenic HOXB13 transcriptional activity in PC, thus potentially serving as an imaging biomarker for more aggressive disease.

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.

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.

Aspirin Metabolites and Mammographic Breast Density in Premenopausal Women.

BACKGROUND: Studies investigating the associations of self-reported aspirin use and mammographic breast density (MBD) have reported conflicting results. Therefore, we investigated the associations of aspirin metabolites with MBD in premenopausal women. METHODS: We performed this study on 705 premenopausal women who had a fasting blood draw for metabolomic profiling. We performed covariate-adjusted linear regression models to calculate the least square means of volumetric measures of MBD [volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV)] by quartiles of aspirin metabolites [salicyluric glucuronide, 2-hydroxyhippurate (salicylurate), salicylate, and 2,6-dihydroxybenzoic acid]. RESULTS: Approximately 13% of participants reported taking aspirin in the past 12 months. Aspirin users had higher levels of 2-hydroxyhippurate (salicylurate), salicylate, and salicyluric glucuronide (peak area) than nonusers, but only the mean peak area of salicyluric glucuronide was increased by both dose (1-2 tablets per day = 1,140,663.7 and ≥3 tablets per day = 1,380,476.0) and frequency (days per week: 1 day = 888,129.3, 2-3 days = 1,199,897.9, and ≥4 days = 1,654,637.0). Aspirin metabolites were not monotonically associated with VPD, DV, or NDV. CONCLUSIONS: Given the null results, additional research investigating the associations of aspirin metabolites in breast tissue and MBD is necessary. Impact: Elucidating the determinants of MBD, a strong risk factor for breast cancer, can play an important role in breast cancer prevention. Future studies should determine the associations of nonaspirin NSAID metabolites with MBD.

ISIT-QA: In Silico Imaging Trial to Evaluate a Low-Count Quantitative SPECT Method Across Multiple Scanner-Collimator Configurations for 223Ra-Based Radiopharmaceutical Therapies.

Personalized dose-based treatment planning requires accurate and reproducible noninvasive measurements to ensure safety and effectiveness. Dose estimation using SPECT is possible but challenging for alpha (α)-particle-emitting radiopharmaceutical therapy (α-RPT) because of complex γ-emission spectra, extremely low counts, and various image-degrading artifacts across a plethora of scanner-collimator configurations. Through the incorporation of physics-based considerations and skipping of the potentially lossy voxel-based reconstruction step, a recently developed projection-domain low-count quantitative SPECT (LC-QSPECT) method has the potential to provide reproducible, accurate, and precise activity concentration and dose measures across multiple scanners, as is typically the case in multicenter settings. To assess this potential, we conducted an in silico imaging trial to evaluate the LC-QSPECT method for a 223Ra-based α-RPT, with the trial recapitulating patient and imaging system variabilities. Methods: A virtual imaging trial titled In Silico Imaging Trial for Quantitation Accuracy (ISIT-QA) was designed with the objectives of evaluating the performance of the LC-QSPECT method across multiple scanner-collimator configurations and comparing performance with a conventional reconstruction-based quantification method. In this trial, we simulated 280 realistic virtual patients with bone-metastatic castration-resistant prostate cancer treated with 223Ra-based α-RPT. The trial was conducted with 9 simulated SPECT scanner-collimator configurations. The primary objective of this trial was to evaluate the reproducibility of dose estimates across multiple scanner-collimator configurations using LC-QSPECT by calculating the intraclass correlation coefficient. Additionally, we compared the reproducibility and evaluated the accuracy of both considered quantification methods across multiple scanner-collimator configurations. Finally, the repeatability of the methods was evaluated in a test-retest study. Results: In this trial, data from 268 223RaCl2 treated virtual prostate cancer patients, with a total of 2,903 lesions, were used to evaluate LC-QSPECT. LC-QSPECT provided dose estimates with good reproducibility across the 9 scanner-collimator configurations (intraclass correlation coefficient > 0.75) and high accuracy (ensemble average values of recovery coefficients ranged from 1.00 to 1.02). Compared with conventional reconstruction-based quantification, LC-QSPECT yielded significantly improved reproducibility across scanner-collimator configurations, accuracy, and test-retest repeatability ([Formula: see text] Conclusion: LC-QSPECT provides reproducible, accurate, and repeatable dose estimations in 223Ra-based α-RPT as evaluated in ISIT-QA. These findings provide a strong impetus for multicenter clinical evaluations of LC-QSPECT in dose quantification for α-RPTs.

Multimodal neuro-nanotechnology: Challenging the existing paradigm in glioblastoma therapy.

Integrating multimodal neuro- and nanotechnology-enabled precision immunotherapies with extant systemic immunotherapies may finally provide a significant breakthrough for combatting glioblastoma (GBM). The potency of this approach lies in its ability to train the immune system to efficiently identify and eradicate cancer cells, thereby creating anti-tumor immune memory while minimizing multi-mechanistic immune suppression. A critical aspect of these therapies is the controlled, spatiotemporal delivery of structurally defined nanotherapeutics into the GBM tumor microenvironment (TME). Architectures such as spherical nucleic acids or poly(beta-amino ester)/dendrimer-based nanoparticles have shown promising results in preclinical models due to their multivalency and abilities to activate antigen-presenting cells and prime antigen-specific T cells. These nanostructures also permit systematic variation to optimize their distribution, TME accumulation, cellular uptake, and overall immunostimulatory effects. Delving deeper into the relationships between nanotherapeutic structures and their performance will accelerate nano-drug development and pave the way for the rapid clinical translation of advanced nanomedicines. In addition, the efficacy of nanotechnology-based immunotherapies may be enhanced when integrated with emerging precision surgical techniques, such as laser interstitial thermal therapy, and when combined with systemic immunotherapies, particularly inhibitors of immune-mediated checkpoints and immunosuppressive adenosine signaling. In this perspective, we highlight the potential of emerging treatment modalities, combining advances in biomedical engineering and neurotechnology development with existing immunotherapies to overcome treatment resistance and transform the management of GBM. We conclude with a call to action for researchers to leverage these technologies and accelerate their translation into the clinic.

Analysis of anemia and iron supplementation among glioblastoma patients reveals sex-biased association between anemia and survival.

The association between anemia and outcomes in glioblastoma patients is unclear. We analyzed data from 1346 histologically confirmed adult glioblastoma patients in the TriNetX Research Network. Median hemoglobin and hematocrit levels were quantified for 6 months following diagnosis and used to classify patients as anemic or non-anemic. Associations of anemia and iron supplementation of anemic patients with median overall survival (median-OS) were then studied. Among 1346 glioblastoma patients, 35.9% of male and 40.5% of female patients were classified as anemic using hemoglobin-based WHO guidelines. Among males, anemia was associated with reduced median-OS compared to matched non-anemic males using hemoglobin (HR 1.24; 95% CI 1.00-1.53) or hematocrit-based cutoffs (HR 1.28; 95% CI 1.03-1.59). Among females, anemia was not associated with median-OS using hemoglobin (HR 1.00; 95% CI 0.78-1.27) or hematocrit-based cutoffs (HR: 1.10; 95% CI 0.85-1.41). Iron supplementation of anemic females trended toward increased median-OS (HR 0.61; 95% CI 0.32-1.19) although failing to reach statistical significance whereas no significant association was found in anemic males (HR 0.85; 95% CI 0.41-1.75). Functional transferrin-binding assays confirmed sexually dimorphic binding in resected patient samples indicating underlying differences in iron biology. Anemia among glioblastoma patients exhibits a sex-specific association with survival.

Baseline levels and longitudinal rates of change in plasma Aß42/40 among self-identified Black/African American and White individuals.

Objective: The use of blood-based biomarkers of Alzheimer disease (AD) may facilitate access to biomarker testing of groups that have been historically under-represented in research. We evaluated whether plasma Aß42/40 has similar or different baseline levels and longitudinal rates of change in participants racialized as Black or White. Methods: The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to evaluate for potential differences in AD biomarkers between individuals racialized as Black or White. Plasma samples collected at three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama-Birmingham) underwent analysis with C2N Diagnostics' PrecivityAD™ blood test for Aß42 and Aß40. General linear mixed effects models were used to estimate the baseline levels and rates of longitudinal change for plasma Aß measures in both racial groups. Analyses also examined whether dementia status, age, sex, education, APOE ε4 carrier status, medical comorbidities, or fasting status modified potential racial differences. Results: Of the 324 Black and 1,547 White participants, there were 158 Black and 759 White participants with plasma Aß measures from at least two longitudinal samples over a mean interval of 6.62 years. At baseline, the group of Black participants had lower levels of plasma Aß40 but similar levels of plasma Aß42 as compared to the group of White participants. As a result, baseline plasma Aß42/40 levels were higher in the Black group than the White group, consistent with the Black group having lower levels of amyloid pathology. Racial differences in plasma Aß42/40 were not modified by age, sex, education, APOE ε4 carrier status, medical conditions (hypertension and diabetes), or fasting status. Despite differences in baseline levels, the Black and White groups had a similar longitudinal rate of change in plasma Aß42/40. Interpretation: Black individuals participating in AD research studies had a higher mean level of plasma Aß42/40, consistent with a lower level of amyloid pathology, which, if confirmed, may imply a lower proportion of Black individuals being eligible for AD clinical trials in which the presence of amyloid is a prerequisite. However, there was no significant racial difference in the rate of change in plasma Aß42/40, suggesting that amyloid pathology accumulates similarly across racialized groups.

Correlational analyses of biomarkers that are harmonized through a bridging study due to measurement errors.

Evaluating correlations between disease biomarkers and clinical outcomes is crucial in biomedical research. During the early stages of many chronic diseases, changes in biomarkers and clinical outcomes are often subtle. A major challenge to detecting subtle correlations is that studies with large sample sizes are usually needed to achieve sufficient statistical power. This challenge is even greater when biofluid and imaging biomarker data are used because the required procedures are burdensome, perceived as invasive, and/or expensive, limiting sample sizes in individual studies. Combining data across multiple studies may increase statistical power, but biomarker data may be generated using different assay platforms, scanner types, or processing protocols, which may affect measured biomarker values. Therefore, harmonizing biomarker data is essential to combining data across studies. Bridging studies involve re-processing of a subset of samples or imaging scans to evaluate how biomarker values vary by studies. This presents an analytic challenge on how to best harmonize biomarker data across studies to allow unbiased and optimal estimates of their correlations with standardized clinical outcomes. We conceptualize that a latent biomarker underlies the observed biomarkers across studies, and propose a novel approach that integrates the data in the bridging study with the study-specific biomarker data for estimating the biological correlations between biomarkers and clinical outcomes. Through extensive simulations, we compare our method to several alternative methods/algorithms often used to estimate the correlations. Finally, we demonstrate the application of this methodology to a real-world multi-center Alzheimer's disease biomarker study to correlate cerebrospinal fluid biomarker concentrations with cognitive outcomes.

Relationships of Cognitive Measures with Cerebrospinal Fluid but Not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals.

OBJECTIVE: Biomarkers of Alzheimer disease vary between groups of self-identified Black and White individuals in some studies. This study examined whether the relationships between biomarkers or between biomarkers and cognitive measures varied by racialized groups. METHODS: Cerebrospinal fluid (CSF), amyloid positron emission tomography (PET), and magnetic resonance imaging measures were harmonized across four studies of memory and aging. Spearman correlations between biomarkers and between biomarkers and cognitive measures were calculated within each racialized group, then compared between groups by standard normal tests after Fisher's Z-transformations. RESULTS: The harmonized dataset included at least one biomarker measurement from 495 Black and 2,600 White participants. The mean age was similar between racialized groups. However, Black participants were less likely to have cognitive impairment (28% vs 36%) and had less abnormality of some CSF biomarkers including CSF Aß42/40, total tau, p-tau181, and neurofilament light. CSF Aß42/40 was negatively correlated with total tau and p-tau181 in both groups, but at a smaller magnitude in Black individuals. CSF Aß42/40, total tau, and p-tau181 had weaker correlations with cognitive measures, especially episodic memory, in Black than White participants. Correlations of amyloid measures between CSF (Aß42/40, Aß42) and PET imaging were also weaker in Black than White participants. Importantly, no differences based on race were found in correlations between different imaging biomarkers, or in correlations between imaging biomarkers and cognitive measures. INTERPRETATION: Relationships between CSF biomarkers but not imaging biomarkers varied by racialized groups. Imaging biomarkers performed more consistently across racialized groups in associations with cognitive measures. ANN NEUROL 2024;95:495-506.

Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women.

BACKGROUND: Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD. OBJECTIVE: We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women. METHODS: We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level. RESULTS: Participants' mean VPD was 10.5%. Two vitamin A metabolites (ß-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of ß-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and ß-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV. CONCLUSION: We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.

Female-specific pancreatic cancer survival from CT imaging of visceral fat implicates glutathione metabolism in solid tumors.

RATIONALE AND OBJECTIVES: To identify if body composition, assessed with preoperative CT-based visceral fat ratio quantification as well as tumor metabolic gene expression, predicts sex-dependent overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: This was a retrospective analysis of preoperative CT in 98 male and 107 female patients with PDAC. Relative visceral fat (rVFA; visceral fat normalized to total fat) was measured automatically using software and corrected manually. Median and optimized rVFA thresholds were determined according to published methods. Kaplan Meier and log-rank tests were used to estimate OS. Multivariate models were developed to identify interactions between sex, rVFA, and OS. Unsupervised gene expression analysis of PDAC tumors from The Cancer Genome Atlas (TCGA) was performed to identify metabolic pathways with similar survival patterns to rVFA. RESULTS: Optimized preoperative rVFA threshold of 38.9% predicted significantly different OS in females with a median OS of 15 months (above threshold) vs 24 months (below threshold; p = 0.004). No significant threshold was identified in males. This female-specific significance was independent of age, stage, and presence of chronic pancreatitis (p = 0.02). Tumor gene expression analysis identified female-specific stratification from a five-gene signature of glutathione S-transferases. This was observed for PDAC as well as clear cell renal carcinoma and glioblastoma. CONCLUSION: CT-based assessments of visceral fat can predict pancreatic cancer OS in females. Glutathione S-transferase expression in tumors predicts female-specific OS in a similar fashion.

Proximity proteomic analysis of the NRF family reveals the Parkinson's disease protein ZNF746/PARIS as a co-complexed repressor of NRF2.

The nuclear factor erythroid 2-related factor 2 (NRF2) transcription factor activates cytoprotective and metabolic gene expression in response to various electrophilic stressors. Constitutive NRF2 activity promotes cancer progression, whereas decreased NRF2 function contributes to neurodegenerative diseases. We used proximity proteomic analysis to define protein networks for NRF2 and its family members NRF1, NRF3, and the NRF2 heterodimer MAFG. A functional screen of co-complexed proteins revealed previously uncharacterized regulators of NRF2 transcriptional activity. We found that ZNF746 (also known as PARIS), a zinc finger transcription factor implicated in Parkinson's disease, physically associated with NRF2 and MAFG, resulting in suppression of NRF2-driven transcription. ZNF746 overexpression increased oxidative stress and apoptosis in a neuronal cell model of Parkinson's disease, phenotypes that were reversed by chemical and genetic hyperactivation of NRF2. This study presents a functionally annotated proximity network for NRF2 and suggests a link between ZNF746 overexpression in Parkinson's disease and inhibition of NRF2-driven neuroprotection.

Lipidome of mammographic breast density in premenopausal women.

BACKGROUND: High mammographic breast density (MBD) is a strong risk factor for breast cancer development, but the biological mechanisms underlying MBD are unclear. Lipids play important roles in cell differentiation, and perturbations in lipid metabolism are implicated in cancer development. Nevertheless, no study has applied untargeted lipidomics to profile the lipidome of MBD. Through this study, our goal is to characterize the lipidome of MBD in premenopausal women. METHODS: Premenopausal women were recruited during their annual screening mammogram at the Washington University School of Medicine in St. Louis, MO. Untargeted lipidomic profiling for 982 lipid species was performed at Metabolon (Durham, NC®), and volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) was assessed using Volpara 1.5 (Volpara Health®). We performed multivariable linear regression models to investigate the associations of lipid species with MBD and calculated the covariate-adjusted least square mean of MBD by quartiles of lipid species. MBD measures were log10 transformed, and lipid species were standardized. Linear coefficients of MBD were back-transformed and considered significant if the Bonferroni corrected p-value was < 0.05. RESULTS: Of the 705 premenopausal women, 72% were non-Hispanic white, and 23% were non-Hispanic black. Mean age, and BMI were 46 years and 30 kg/m2, respectively. Fifty-six lipid species were significantly associated with VPD (52 inversely and 4 positively). The lipid species with positive associations were phosphatidylcholine (PC)(18:1/18:1), lysophosphatidylcholine (LPC)(18:1), lactosylceramide (LCER)(14:0), and phosphatidylinositol (PI)(18:1/18:1). VPD increased across quartiles of PI(18:1/18:1): (Q1 = 7.5%, Q2 = 7.7%, Q3 = 8.4%, Q4 = 9.4%, Bonferroni p-trend = 0.02). The lipid species that were inversely associated with VPD were mostly from the triacylglycerol (N = 43) and diacylglycerol (N = 7) sub-pathways. Lipid species explained some of the variation in VPD. The inclusion of lipid species increased the adjusted R2 from 0.45, for a model that includes known determinants of VPD, to 0.59. CONCLUSIONS: We report novel lipid species that are associated with MBD in premenopausal women. Studies are needed to validate our results and the translational potential.

Lymph Node Positivity: Indication for Preoperative MRI?

BACKGROUND: The purpose was to determine what factors help predict benefit from preoperative MRI. METHODS: We conducted an IRB approved retrospective review of patients with breast cancer who underwent preoperative MRI (2018-2021). Patients were divided into a cohort of no new disease detected on MRI versus new disease detected. RESULTS: Of 420 patients with a new diagnosis of breast cancer who underwent preoperative MRI, 17% had new multicentric, multifocal, or contralateral disease detected. There was no difference between the two cohorts for age (p = 0.23), race (p = 0.45), family history (p = 0.47), breast density (p = 0.14), or hormone status (p = 0.90). In multivariate analysis, age (p = 0.61, OR 0.99), race (p = 0.58, OR 1.26), family history (p = 0.54, OR 0.82), breast density (p = 0.83, OR 0.87), grade (p = 0.87, OR 1.09), tumor size (p = 0.37, OR 0.92), and use of neoadjuvant therapy (p = 0.41, OR 0.72) were not predictive of detection of additional new disease. Presence of positive nodes on ultrasound or mammogram was associated with new or multifocal disease on MRI (p = 0.0005, OR 3.48). Pre-MRI positive nodes increased the likelihood of detection of new disease (p = 0.0002, OR 3.04). Preoperative MRI resulted in more extensive surgery than indicated for 22.2% of the no new disease detected cohort and 6.9% of the new multicentric disease cohort (p < 0.001). CONCLUSIONS: Patients with nodal disease detected in their evaluation are more likely to have new multifocal, multicentric, or contralateral disease detected on MRI. The use of preoperative MRI may be particularly helpful in patients with node-positive disease in identifying additional disease that would alter surgical management.