Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Schult TA

PROVIDER: S-EPMC8713787 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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

Schult Tjada A TA Lauer Mara J MJ Berker Yannick Y Cardoso Marcella R MR Vandergrift Lindsey A LA Habbel Piet P Nowak Johannes J Taupitz Matthias M Aryee Martin M Mino-Kenudson Mari A MA Christiani David C DC Cheng Leo L LL

Proceedings of the National Academy of Sciences of the United States of America 20211201 51

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 c ...[more]

PMID: 34903652

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Project description:Study questionCan 1H Magnetic Resonance Spectroscopy (MRS) be used to obtain information about the molecules and metabolites in live human spermatozoa?Summary answerPercoll-based density gradient centrifugation (DGC) followed by a further two washing steps, yielded enough sperm with minimal contamination (<0.01%) from seminal fluid to permit effective MRS which detected significant differences (P < 0.05) in the choline/glycerophosphocholine (GPC), lipid and lactate regions of the 1H MRS spectrum between sperm in the pellet and those from the 40%/80% interface.What is known alreadyCurrent methods to examine sperm are either limited in their value (e.g. semen analysis) or are destructive (e.g. immunohistochemistry, sperm DNA testing). A few studies have previously used MRS to examine sperm, but these have either looked at seminal plasma from men with different ejaculate qualities or at the molecules present in pooled samples of lyophilized sperm.Study design, samples/materials, methodsSperm suspended in phosphate buffered saline (PBS) at 37°C were examined by 1H MRS scanning using a 1H excitation-sculpting solvent suppression sequence after recovery from fresh ejaculates by one of three different methods: (i) simple centrifugation; (ii) DGC with one wash; or (iii) DGC with two washes. In the case of DGC, sperm were collected both from the pellet ('80%' sperm) and the 40/80 interface ('40%' sperm). Spectrum processing was carried out using custom Matlab scripts to determine; the degree of seminal plasma/Percoll contamination, the minimum sperm concentration for 1H MRS detection and differences between the 1H MRS spectra of '40%' and '80%' sperm.Main results and the role of chanceDGC with two washes minimized the 1H MRS peak intensity for both seminal plasma and Percoll/PBS solution contamination while retaining sperm specific peaks. For the MRS scanner used in this study, the minimum sperm concentration required to produce a choline/GPC 1H MRS peak greater than 3:1 signal to noise ratio (SNR) was estimated at ~3 × 106/ml. The choline/GPC and lactate/lipid regions of the 1H spectrum were significantly different by two-way ANOVA analysis (P < 0.0001; n = 20). ROC curve analysis of these region showed significant ability to distinguish between the two sperm populations: choline/GPC ROC AUC = 0.65-0.67, lactate/lipid ROC AUC = 0.86-0.87.Limitations, reasons for cautionOnly 3-4 semen samples were used to assess the efficacy of each sperm washing protocol that were examined. The estimated minimum sperm concentration required for MRS is specific to the hardware used in our study and may be different in other spectrometers. Spectrum binning is a low resolution analysis method that sums MRS peaks within a chemical shift range. This can obscure the identity of which metabolite(s) are responsible for differences between sperm populations. Further work is required to determine the relative contribution of somatic cells to the MRS spectrum from the '40%' and '80%' sperm.Wider implications of the findings1H MRS can provide information about the molecules present in live human sperm and may therefore permit the study of the underlying functional biology or metabolomics of live sperm. Given the relatively low concentration of sperm required to obtain a suitable MRS signal (~3 × 106/ml), this could be carried out on sperm from men with oligo-, astheno- or teratozoospermia. This may lead to the development of new diagnostic tests or ultimately novel treatments for male factor infertility.Study funding and competing interest(s)This work was supported by the Medical Research Council Grant MR/M010473/1. The authors declare no conflicts of interest.

Project description:Medulloblastoma (MB) is the most common malignant brain tumor occurring in childhood and rarely found in adult. Based on transcriptome profile MB are currently classified into four major molecular groups reflecting a considerable biological heterogeneity: WNT-activated, SHH-activated, group 3 and group 4. Recently, DNA methylation profiling allowed the identification of additional subgroups within the four major molecular groups associated with different clinic-pathological and molecular features. Isocitrate Dehydrogenase-1 (IDH1) mutations have been described in several tumours, including gliomas, while in MB are exceptionally reported and not routinely investigated. By mean of magnetic resonance spectroscopy (MRS) we unequivocally assessed the presence the oncometabolite D-2-Hydroxyglutarate (2HG), a marker of IDH1 and IDH2 mutation, in a case of adult MB. Immunophenotypical work-up and methylation profiling assigned the diagnosis of MB, subclass SHH-A, and molecular testing revealed the presence of the non-canonical somatic IDH1(p.R132C) mutation and an additional GNAS mutation, also exceptionally described in MB. To our knowledge this is the first reported case of MB harboring both mutations together. Of note, tumour exhibited a heterogeneous phenotype with a tumour component displaying glial differentiation, with robust GFAP expression, and a component with conventional MB features and selective presence of GNAS mutation, suggesting coexistence of two different major tumour clones. These findings draw attention to the need of a deeper genetic characterization of MB in order to get insights into their biology and improve stratification and clinical management of the patients. Moreover, reported data underling the importance of performing MRS for the identification of IDH mutations in non-glial tumours. The use of throughput molecular profiling analysis and advanced medical imaging technology will certainly increase the frequency with which rare tumour entities will be identified. Whether they have any particular therapeutic implications or prognostic relevance requires further investigations.

Dataset Information

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

Publications

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

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