Differentiation of patients with and without prostate cancer using urine 1 H NMR metabolomics.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate-key metabolites involved in cellular proliferation and microbiome effects, respectively-were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.

13 C NMR quantification of polyamine syntheses in rat prostate.

Currently, many prostate cancer patients, detected through the prostate specific antigen test, harbor organ-confined indolent disease that cannot be differentiated from aggressive cancer according to clinically and pathologically known measures. Spermine has been considered as an endogenous inhibitor for prostate-confined cancer growth and its expression has shown correlation with prostate cancer growth rates. If established clinically, measurements of spermine bio-synthesis rates in prostates may predict prostate cancer growth and patient outcomes. Using rat models, we tested the feasibility of quantifying spermine bio-synthesis rates with 13 C NMR. Male Copenhagen rats (10 weeks, n = 6) were injected with uniformly 13 C-labeled L-ornithine HCl, and were sacrificed in pairs at 10, 30, and 60 min after injection. Another two rats were injected with saline and sacrificed at 30 min as controls. Prostates were harvested and extracted with perchloric acid and the neutralized solutions were examined by 13 C NMR at 600 MHz. 13 C NMR revealed measurable ornithine, as well as putrescine-spermidine-spermine syntheses in rat prostates, allowing polyamine bio-synthetic and ornithine bio-catabolic rates to be calculated. Our study demonstrated the feasibility of 13 C NMR for measuring bio-synthesis rates of ornithine to spermine enzymatic reactions in rat prostates. The current study established a foundation upon which future investigations of protocols that differentiate prostate cancer growth rates according to the measure of ornithine to spermine bio-synthetic rates may be developed.

Magnetic Resonance Imaging Characteristics of Molecular Subgroups in Pediatric H3 K27M Mutant Diffuse Midline Glioma.

PURPOSE: Recent research identified histone H3 K27M mutations to be associated with a dismal prognosis in pediatric diffuse midline glioma (pDMG); however, data on detailed MRI characteristics with respect to H3 K27 mutation status and molecular subgroups (H3.1 and H3.3 K27M mutations) are limited. METHODS: Standardized magnetic resonance imaging (MRI) parameters and epidemiologic data of 68 pDMG patients (age <18 years) were retrospectively reviewed and compared in a) H3 K27M mutant versus H3 K27 wildtype (WT) tumors and b) H3.1 versus H3.3 K27M mutant tumors. RESULTS: Intracranial gliomas (n = 58) showed heterogeneous phenotypes with isointense to hyperintense signal in T2-weighted images and frequent contrast enhancement. Hemorrhage and necrosis may be present. Comparing H3 K27M mutant to WT tumors, there were significant differences in the following parameters: i) tumor localization (p = 0.001), ii) T2 signal intensity (p = 0.021), and iii) T1 signal homogeneity (p = 0.02). No significant imaging differences were found in any parameter between H3.1 and H3.3 K27M mutant tumors; however, H3.1 mutant tumors occurred at a younger age (p = 0.004). Considering spinal gliomas (n = 10) there were no significant imaging differences between the analyzed molecular groups. CONCLUSION: With this study, we are the first to provide detailed MR imaging data on H3 K27M mutant pDMG with respect to molecular subgroup status in a large patient cohort. Our findings may support diagnosis and future targeted therapeutic trials of pDMG within the framework of the radiogenomics concept.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

Do prisoners trust the healthcare system?

BACKGROUND: Individuals who are incarcerated have greater healthcare needs than non-justice-involved individuals, yet incarcerated individuals often report substandard care. There are disproportionate numbers of black, indigenous, and people of color (BIPOC) in prison, who, even in general society face greater obstacles to accessing healthcare and have worse health outcomes due to structural racism. Regardless of race, people with criminal justice involvement often report stigma from the non-carceral healthcare system. Providing sufficient healthcare in carceral settings themselves is complicated by lack of privacy and the inherent dialectic of prisons that restrict freedom and providers focusing on healing and health. Based on these adverse experiences, people who are incarcerated may have decreased distrust in the healthcare system, deterring individuals from getting adequate medical care. METHODS: In this exploratory study, health care system distrust was evaluated among 200 people who were incarcerated using the Revised Health Care System Distrust scale, a community-validated, 9-item measure comprised of 2 subscales (values and competence distrust). RESULTS: Distrust was moderately and positively associated with participant age (rs = 0.150, p = 0.034), with the second-oldest quintile (33 to 42-year-olds) reporting the highest level of overall and competence distrust. Participants identifying as Non-Latinx White reported higher competence distrust compared to Latinx and Non-Latinx/Non-White respondents. CONCLUSIONS: These preliminary findings suggest that select groups of prisoners may be less likely to trust the healthcare system, highlighting an impediment to receiving adequate care while incarcerated. Further study of this topic is warranted.

Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer.

Low-dose CT has shown promise in detecting early stage lung cancer. However, concerns about the adverse health effects of radiation and high cost prevent its use as a population-wide screening tool. Effective and feasible screening methods to triage suspicious patients to CT are needed. We investigated human lung cancer metabolomics from 93 paired tissue-serum samples with magnetic resonance spectroscopy and identified tissue and serum metabolomic markers that can differentiate cancer types and stages. Most interestingly, we identified serum metabolomic profiles that can predict patient overall survival for all cases (p = 0.0076), and more importantly for Stage I cases alone (n = 58, p = 0.0100), a prediction which is significant for treatment strategies but currently cannot be achieved by any clinical method. Prolonged survival is associated with relative overexpression of glutamine, valine, and glycine, and relative suppression of glutamate and lipids in serum.

Cancer metabolomic markers in urine: evidence, techniques and recommendations.

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer.

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

MRI Phenotype of RELA-fused Pediatric Supratentorial Ependymoma.

PURPOSE: Epigenetic profiling has recently identified clinically and molecularly distinct subgroups of ependymoma. The 2016 World Health Organization (WHO) classification recognized supratentorial ependymomas (ST-EPN) with REL-associated protein/p65 (RELA) fusion as a clinicopathological entity. These tumors represent 70% of pediatric ST-EPN characterized by recurrent C11orf95-RELA fusion transcripts, which lead to pathological activation of the nuclear factor 'kappa-light-chain-enhancer' of activated B-cells (NF-κB) signaling pathway. Cyclin-dependent kinase inhibitor 2A (CDKN2A) inactivation has also been reported to correlate with poor prognosis. Here, we systematically describe magnetic resonance imaging (MRI) characteristics of RELA-fused ST-EPN, with respect to CDKN2A deletion status. METHODS: Our cohort of patients with ST-EPN (n = 57) was obtained from the database of the German Brain Tumor Reference Center of the German Society for Neuropathology and Neuroanatomy (DGNN), and tumors were diagnosed according to the 2016 WHO classification. Molecular characterization identified 47 RELA-fused tumors. We analyzed the preoperative MRI according to standardized criteria, and comparison was performed between CDKN2A altered (n = 21) and CDKN2A wild type (n = 26) tumors. RESULTS: The RELA-fused ST-EPN showed a spectrum of predominantly hemispheric tumors with cysts and necrosis. Statistical analysis on CDKN2A status revealed significant differences in terms of younger manifestation age (p =0.002) and more intratumoral hemorrhage in T2-weighted imaging (T2WI) (p =0.010) in wild type tumors; however, the location was not a parameter for differentiation. CONCLUSION: This study first provides comprehensive MRI data for RELA-fused ST-EPN as a distinct entity, with further interest on CDKN2A genomic status. Patient stratification by morphological MRI alone seems difficult at present. The results may support ongoing research in ST-EPN within the framework of the radiogenomics concept.

Magnetic resonance imaging surrogates of molecular subgroups in atypical teratoid/rhabdoid tumor.

Background: Recently, 3 molecular subgroups of atypical teratoid/rhabdoid tumor (ATRT) were identified, but little is known of their clinical and magnetic resonance imaging (MRI) characteristics. Methods: A total of 43 patients with known molecular subgroup status (ATRT-sonic hedgehog [SHH], n = 17; ATRT-tyrosine [TYR], n = 16; ATRT-myelocytomatosis oncogene [MYC], n = 10) were retrieved from the EU-RHAB Registry and analyzed for clinical and MRI features. Results: On MRI review, differences in preferential tumor location were confirmed, with ATRT-TYR being predominantly located infratentorially (P < 0.05). Peritumoral edema was more pronounced in ATRT-MYC compared with ATRT-SHH (P < 0.05) and ATRT-TYR (P < 0.05). Conversely, peripheral tumor cysts were found more frequently in ATRT-SHH (71%) and ATRT-TYR (94%) compared with ATRT-MYC (40%, P < 0.05). Contrast enhancement was absent in 29% of ATRT-SHH (0% of ATRT-TYR; 10% of ATRT-MYC; P < 0.05), and there was a trend toward strong contrast enhancement in ATRT-TYR and ATRT-MYC. We found the characteristic (bandlike) enhancement in 28% of ATRT as well as restricted diffusion in the majority of tumors. A midline/off-midline location in the posterior fossa was also not subgroup specific. Visible meningeal spread (M2) at diagnosis was rare throughout all subgroups. Conclusion: These exploratory findings suggest that MRI features vary across the 3 molecular subgroups of ATRT. Within future prospective trials, MRI may aid diagnosis and treatment stratification.

Metabolomic Prediction of Human Prostate Cancer Aggressiveness: Magnetic Resonance Spectroscopy of Histologically Benign Tissue.

Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. Magnetic resonance spectroscopy (MRS)-based metabolomics of histologically-benign tissues from cancerous prostates can predict disease aggressiveness, offering clinically-translatable prognostic information. This retrospective study of 185 patients (2002-2009) included prostate tissues from prostatectomies (n = 365), benign prostatic hyperplasia (BPH) (n = 15), and biopsy cores from cancer-negative patients (n = 14). Tissues were measured with high resolution magic angle spinning (HRMAS) MRS, followed by quantitative histology using the Prognostic Grade Group (PGG) system. Metabolic profiles, measured solely from 338 of 365 histologically-benign tissues from cancerous prostates and divided into training-testing cohorts, could identify tumor grade and stage, and predict recurrence. Specifically, metabolic profiles: (1) show elevated myo-inositol, an endogenous tumor suppressor and potential mechanistic therapy target, in patients with highly-aggressive cancer, (2) identify a patient sub-group with less aggressive prostate cancer to avoid overtreatment if analysed at biopsy; and (3) subdivide the clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. Metabolomics information can help transform a morphology-based diagnostic system by invoking cancer biology to improve evaluation of histologically-benign tissues in cancer environments.

Prostate cancer diagnosis and characterization with mass spectrometry imaging.

BACKGROUND: Prostate cancer (PCa), the most common cancer and second leading cause of cancer death in American men, presents the clinical challenge of distinguishing between indolent and aggressive tumors for proper treatment. PCa presents significant alterations in metabolic pathways that can potentially be measured using techniques like mass spectrometry (MS) or MS imaging (MSI) and used to characterize PCa aggressiveness. MS quantifies metabolomic, proteomic, and lipidomic profiles of biological systems that can be further visualized for their spatial distributions through MSI. METHODS: PubMed was queried for all publications relating to MS and MSI in human PCa from April 2007 to April 2017. With the goal of reviewing the utility of MSI in diagnosis and prognostication of human PCa, MSI articles that reported investigations of PCa-specific metabolites or metabolites indicating PCa aggressiveness were selected for inclusion. Articles were included that covered MS and MSI principles, limitations, and applications in PCa. RESULTS: We identified nine key studies on MSI in intact human prostate tissue specimens that determined metabolites which could either differentiate between benign and malignant prostate tissue or indicate PCa aggressiveness. These MSI-detected biomarkers show promise in reliably identifying PCa and determining disease aggressiveness. CONCLUSIONS: MSI represents an innovative technique with the ability to interrogate cancer biomarkers in relation to tissue pathologies and investigate tumor aggressiveness. We propose MSI as a powerful adjuvant histopathology imaging tool for prostate tissue evaluations, where clinical translation of this ex vivo technique could make possible the use of MSI for personalized medicine in diagnosis and prognosis of PCa. Moreover, the knowledge provided from this technique can majorly contribute to the understanding of molecular pathogenesis of PCa and other malignant diseases.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases.

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

Applications of high-resolution magic angle spinning MRS in biomedical studies I-cell line and animal models.

High-resolution magic angle spinning (HRMAS) MRS allows for direct measurements of non-liquid tissue and cell specimens to present valuable insights into the cellular metabolisms of physiological and pathological processes. HRMAS produces high-resolution spectra comparable to those obtained from solutions of specimen extracts but without complex metabolite extraction processes, and preserves the tissue cellular structure in a form suitable for pathological examinations following spectroscopic analysis. The technique has been applied in a wide variety of biomedical and biochemical studies and become one of the major platforms of metabolomic studies. By quantifying single metabolites, metabolite ratios, or metabolic profiles in their entirety, HRMAS presents promising possibilities for diagnosis and prediction of clinical outcomes for various diseases, as well as deciphering of metabolic changes resulting from drug therapies or xenobiotic interactions. In this review, we evaluate HRMAS MRS results on animal models and cell lines reported in the literature, and present the diverse applications of the method for the understanding of pathological processes and the effectiveness of therapies, development of disease animal models, and new progress in HRMAS methodology.