Laura J. Muirhead

Intraoperative Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry

A mass spectrometric approach was developed for intraoperative identification of cancerous tissue, in near–real-time. Diagnosing the Masses One of the best options for curing cancer is surgery. Yet, surgeons can leave cancerous tissue behind by not seeing the “tumor margins”—or edges of the tumor—clearly. If a surgeon isn’t sure whether tissue is normal or cancerous, the tissue is sent to a pathologist for testing. During this time (20 to 30 min), the patient remains under anesthesia, and, quite often, additional samples are required. To ensure that all malignant tissue is removed in the operating room, Balog and colleagues developed a mass spectrometry–based approach that identifies cancer during surgery. After analyzing ex vivo samples of cancerous, healthy, and benign/inflammatory tissue with rapid evaporative ionization mass spectrometry (REIMS), the authors created a database of the nearly 3000 tissue-specific mass spectra. These spectra were unique for each cancer type, with lipids such as phosphatidylcholine and phosphotidylinositol showing different ratios. Using these ratios, Balog et al. were even able to identify the origin of metastatic tumors ex vivo. To adapt this technology for use in vivo, during surgery, the authors created the “intelligent knife” (iKnife), which samples surgical smoke for mass spectrometric analysis. More than 800 spectra were acquired with the iKnife from 81 patients. These spectra, when matched against the previously created database, confirmed the results of normal histology, with low rates of false-positive and false-negative readouts. This first-in-human demonstration shows that the iKnife technology is ready for widespread use in the operating room to improve the accuracy of surgical intervention in cancer. 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.

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.

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.

XMS: Cross-Platform Normalization Method for Multimodal Mass Spectrometric Tissue Profiling

AbstractHere 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. Graphical Abstractᅟ

Abstract 3977: iKnife: Rapid evaporative ionization mass spectrometry (REIMS) enables real-time chemical analysis of the mucosal lipidome for diagnostic and prognostic use in colorectal cancer

Background Real time electrospray ionization mass spectrometry (REIMS) enables detailed analysis of tumour lipid chemistry, based on real time analysis of electrocautery smoke plumes. Methods: This was a prospective, observational study performed at St. Mary9s Hospital, London, UK. Patients undergoing elective surgical resections for colorectal cancer were recruited and fresh samples were analyzed ex-vivo using a typical electrosurgery hand piece and monopolar diathermy. Sampling was performed using cutting mode with a standard generator and 30W of output power (ValleylabTM). The hand piece was modified to allow aspiration of the electrosurgical aerosol to a Xevo G2-S iKnife QTof mass spectrometer (Waters Corporation). Raw mass spectrometric data were converted to imzML format (MSConvert) and imported into MATLAB (R2014a) for pre-processing. A prospective database of healthy, dysplastic and malignant colorectal tissues was built and multivariate analysis was performed using principal component analysis and linear discriminant analysis. Classification of each individual tissue type was performed using leave-one-patient-out cross-validation. Results 40 consecutive patients were recruited (22 male, median age 68y, range 47-90). Of the 23 tumor samples 10 were rectal adenocarcinoma and 13 colonic adenocarcinoma. TNM staging of the tumour samples was as follows: T2 (8), T3 (11) T4 (4), N0 (12), N1 (6) N2 (5), M0 (22), M1 (1). Distinction of healthy and malignant colorectal tissue for the whole data set demonstrated an overall classification accuracy of 94.4% and a sensitivity of 92.4%, Specificity 96.8% (ROC AUC 0.98). The diagnostic accuracy for dysplasia was 93.7% (Specificity 95.1%, sensitivity 85.7%, AUC 0.97). Increases in glycerophospholipids (p Conclusion REIMS chemical histology provides near real time diagnostic and prognostic information for stratifying oncological and surgical therapy. Citation Format: James Macalister Kinross, Laura Muirhead, James Alexander, Julia Balog, Cristina Guallar-Hoya, Abigail Speller, Ottmar Golff, Rob Goldin, Ara Darzi, Jeremy Nicholson, Zoltan Takats. iKnife: Rapid evaporative ionization mass spectrometry (REIMS) enables real-time chemical analysis of the mucosal lipidome for diagnostic and prognostic use in colorectal cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3977.

Precision Surgery and Surgical Spectroscopy

Surgical practice is largely based on 20th century principles, and little, if any, biological data is provided to the clinician either preoperatively or intraoperatively to assist in decision making. Therefore novel technologies are urgently required to deliver the vision of personalized health care in surgery. Metabolic phenotyping has distinct advantages over other “-omics” based technologies in surgery, as the analysis of thousands of metabolites is possible in near real time, and it is able to provide critical data on tissue phenotypes and on the functional biochemistry of surgical pathology during surgery. The inference is that surgeons, pathologists, oncologists, and physicians will be able to augment current clinical strategies with chemical analysis at the patient bedside or in the operating theater. This chapter explores emerging technologies in this field and provides practical applications of their use in several areas of surgery and perioperative care. Particular attention is paid to oncology and the application of ambient mass spectrometry technologies in this field.