Abstract 2322: Multiplatform metabolomics studies of human cancers with NMR and mass spectrometry imaging

Unfortunately, at present, there is no single technique that possesses all the characteristics needed to be considered an ideal global metabolite profiling tool. Thus, the use of multiple analytical platforms, such as combining the strengths of Mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), for metabolic profiling can maximize coverage and generate more global metabolomic profiles. In this study, we demonstrate the utilities of the combined NMR and MSI multiplatform in our metabolomics results on human prostate and lung cancers.

Statistical data on the natural history of prostate cancer (PCa) show that >70% of patients diagnosed by PSA screening will likely experience indolent disease with little impact on well-being. For about 17% of newly PSA-diagnosed patients, however, aggressive PCa proliferation ensues, truncating life expectancy. At present, no reliable clinical test can differentiate between these two groups. Using HRMAS 1HNMR followed by quantitative histology, we showed statistically significant correlations between concentrations of Spm and the amount of histologically-benign epithelial (Hb Epi) prostatic cells and glands in human cancerous prostates. However, as above discussed that using HRMAS NMR alone we cannot prove that Spm was indeed generated or resided in the Hb Epi cells. Nevertheless, using MALDI MSI, we were able to locate Spm (m/z: 203.223 ± 0.001Da) onto Hb Epi, where spermine on the PCa lesions appeared below detection limits. From these maps, for the first time, we could visualize and confirm the differential localizations of Spm in prostates. This proof of Sym relationship to prostate pathologies and its proposed PCa inhibitory effects may support further studies that are critical in differentiating aggressive from indolent PCa for disease evaluations and patient personalized treatment strategies.

To search for such screening metabolomics biomarkers in lung cancer, we used HRMAS NMR to analyze 93 pairs of human LuCa tissue and serum samples, and 29 healthy human sera. A number of potential metabolite candidates capable to differentiate LuCa characteristics were identified, including glutamate, lipids, alanine, glycerylphosphorylcholine, glutamine, phosphorylcholine, etc. This list can be further expanded by analyzing metabolite composition in the serum of cancer patients and control healthy subjects using LC-MS, which offers a dramatic increase in sensitivity compared to HRMAS NMR and, therefore, is better suited for the biomarker discovery. In addition to acquiring high-resolution mass data, the high data acquisition rate allows the fragment ion mass spectra (MS/MS) to be generated for the most abundant ionic species in each chromatographic peak. This feature allows specific classes of tumor-attenuated metabolites to be identified based on the presence of unique structurally diagnostic fragment ions in MS/MS spectra.

Citation Format: Leo L. Cheng, Anya B. Zhong, Isabella H. Muti, Stephen J. Eyles, Richard W. Vachet, Sylwia A. Stopka, Kristen N. Sikora, Cedric E. Bobst, Jeffrey N. Agar, Mari A. Mino-Kenudson, Chin-Lee Wu, David C. Christiani, Igor A. Kaltashov, Nathalie Y. Agar. Multiplatform metabolomics studies of human cancers with NMR and mass spectrometry imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2322.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

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.