Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry.

Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis.

XMS: cross-platform normalization method for multimodal mass spectrometric tissue profiling.

Here we present a proof of concept cross-platform normalization approach to convert raw mass spectra acquired by distinct desorption ionization methods and/or instrumental setups to cross-platform normalized analyte profiles. The initial step of the workflow is database driven peak annotation followed by summarization of peak intensities of different ions from the same molecule. The resulting compound-intensity spectra are adjusted to a method-independent intensity scale by using predetermined, compound-specific normalization factors. The method is based on the assumption that distinct MS-based platforms capture a similar set of chemical species in a biological sample, though these species may exhibit platform-specific molecular ion intensity distribution patterns. The method was validated on two sample sets of (1) porcine tissue analyzed by laser desorption ionization (LDI), desorption electrospray ionization (DESI), and rapid evaporative ionization mass spectrometric (REIMS) in combination with Fourier transformation-based mass spectrometry; and (2) healthy/cancerous colorectal tissue analyzed by DESI and REIMS with the latter being combined with time-of-flight mass spectrometry. We demonstrate the capacity of our method to reduce MS-platform specific variation resulting in (1) high inter-platform concordance coefficients of analyte intensities; (2) clear principal component based clustering of analyte profiles according to histological tissue types, irrespective of the used desorption ionization technique or mass spectrometer; and (3) accurate "blind" classification of histologic tissue types using cross-platform normalized analyte profiles.

Nutritional modulation of the metabonome: applications of metabolic phenotyping in translational nutritional research.

PURPOSE OF REVIEW: Metabolic profiling technologies provide a global overview of complex dietary processes. Metabonomic analytical approaches have now been translated into multiple areas of clinical nutritional research based on the widespread adoption of high-throughput mass spectrometry and proton nuclear magnetic resonance spectroscopy. This has generated novel insights into the molecular mechanisms that shape the microbiome-dietary-chronic disease axis. RECENT FINDINGS: Metabolome-wide association studies have created a new paradigm in nutritional molecular epidemiology and they have highlighted the importance of gut microbial cometabolic processes in the development of cardiovascular disease and diabetes. Targeted analyses are helping to explain the mechanisms by which high-risk diets (such as red meat) modulate disease risk and they are generating novel biomarkers that will serve to re-define how the efficacy of nutritional interventions is assessed. Nutritional metabonome-microbiome interactions have also been defined in extreme dietary states such as obesity and starvation, and they also serve as important models for understanding how the gut microbiome modifies disease risk. Finally, nutritional systems medicine approaches are creating novel insights into the functional components of our diet, and the mechanisms by which they cause disease. SUMMARY: Diet is an important modulator of the human metabolic phenotype and the analysis of the nutritional metabolome will drive future development of personalized nutritional interventions.

Intraoperative tissue identification using rapid evaporative ionization mass spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near-real-time characterization of human tissue in vivo by analysis of the aerosol ("smoke") released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

Surgical systems biology and personalized longitudinal phenotyping in critical care.

Systems-wide molecular analysis of the metabolic, inflammatory and immune response to surgical trauma has yet to be translated into the operating room. Surgical patients are exposed to a large number of heterogeneous environmental insults that cannot only be quantified by genome-orientated 'omics platforms. Furthermore, surgery demands rapid or near real-time analysis. Systems-level metabolic phenotyping provides a novel 'global' perspective of an organism's metabolic response to surgical injury and, therefore, serves as an ideal platform for the development of personalized therapies in surgery. This article reviews current personalized approaches to healthcare in surgery and explores future directions for personalized surgical biomarker discovery and therapeutics. In particular, this article discusses our vision of 'personalized metabolic phenotyping' in surgery, and outlines next-generation technologies that will make this approach a reality.