Ambient Ionization Mass Spectrometry for Intraoperative and High-Throughput Brain Cancer Diagnostics
Creators: name: Brown, Hannah Marie
Publisher: name: Purdue University Graduate School
Year: 2022
Subjects: Analytical chemistry not elsewhere classified
Analytical biochemistry
Oncology and carcinogenesis not elsewhere classified
Medical biochemistry and metabolomics not elsewhere classified
Cancer diagnosis
Clinical chemistry (incl. diagnostics)
Medical biochemistry - amino acids and metabolites
Medical biochemistry - lipids

description: My research has focused on the development and translation of ambient ionization mass spectrometry (MS)-based platforms in clinical and surgical settings, specifically in the area of brain cancer diagnostics and surgical decision making. Ambient ionization MS methods, such as those described herein, generate and analyze gas phase ions with high sensitivity and specificity from minimally prepared samples in near-real-time, on the order of seconds to minutes, rendering them well suited to point-of-care applications. We used ambient ionization MS methods, specifically desorption electrospray ionization mass spectrometry (DESI-MS) and extraction nanoelectrospray ionization mass spectrometry (nESI-MS) to molecularly characterize brain cancer biopsies. The characterization was made using diagnostic compounds identified as markers of disease state, tissue composition, tumor type, and genotype in human brain tissue. Methods were developed and validated offline in the laboratory and translated to clinical and surgical settings, thereby generating chemical information on prognostic features intraoperatively and providing valuable information that would be otherwise unavailable. We believe that, with approval, the methodologies described can assist physicians and improve patient outcomes by providing analytical tools and molecular information that can inform surgical decision making and adjuvant treatment strategies, complementing and not interfering with standard of care protocols. <br> We have successfully demonstrated the use of desorption electrospray ionization mass spectrometry (DESI-MS) for the expedient molecular assessment of human glioma tissue biopsies based on lipid profiles and prognostic metabolites, both at the tumor core and near surgical margins, in two small-scale, clinical studies. Maximal surgical resection of gliomas that avoids non-infiltrated tissue is associated with survival benefit in patients with glioma. The infiltrative nature of gliomas, as well as their morphological and genetic diversity, renders treatment difficult and demands an integrated imaging and diagnostic approach during surgery to guide clinicians in achieving maximal tumor resection. Further, the estimation of tumor cell percentage (TCP), a measure of tumor infiltration at surgical margins, is not routinely assessed intraoperatively. We have previously shown that rapid, offline molecular assessment of tumor infiltration in tissue biopsies is possible and believe that the same assessment performed intraoperatively in biopsied tissue near surgical margins could improve resection and better inform patient management strategies, including postoperative radiotherapy. Using a DESI-MS spectral library of normal brain tissue and glioma biopsies to generate a statistical model to classify brain tissue biopsies intraoperatively, multivariate statistical approaches were used to predict the disease state and tumor cell percentage (TCP) of each biopsy, thereby providing an measure of tumor infiltration at surgical margins via molecular indicators. In addition to assessment of tumor infiltration, we have developed DESI-MS assays for detecting the oncometabolite 2-Hydroxyglutarate (2HG) to detect isocitrate dehydrogenase (IDH) mutations in gliomas intraoperatively. Knowledge of IDH genotypes at the time of surgical resection could improve patient outcomes, as more aggressive tumor resection of IDH-mutated gliomas is associated with increased survival. While assessments of IDH genotype are typically not available until days after surgery, we have demonstrated the ability to provide this information is less than five minutes. An intraoperative DESI-MS system has successfully been used in a proof-of-concept clinical study and intraoperative performance validation of this platform is ongoing. The findings of these two studies as well as strengths, weaknesses, and areas of improvement for upcoming future iterations of the research are discussed. <br> Point-of-care applications necessitate the adaptation of MS methodologies to smaller devices. Miniature mass spectrometers (Mini MS) boast small footprints, simple operation, and low power consumption, noise levels, and cost, making them attractive candidates for point-of-care use. In a small-scale clinical study, we demonstrated the first application of a Mini MS for determination of IDH mutation status in gliomas intraoperatively. This study paves a path forward for the application of Mini MS in the OR. With its small footprint and low power consumption and noise level, this application of miniature mass spectrometers represents a simple and cost-effective platform for an important intraoperative measurement. <br> While MS-based methods of tissue analysis can detect molecular features of interest and rapidly produce large quantities of data, their inherent speed is rarely utilized because they are traditionally coupled with time-consuming separation techniques (e.g., chromatography). Ambient ionization MS, specifically DESI-MS, is well suited for high-throughput applications due to its lack of sample preparation and purification techniques. In an attempt to rapidly characterize microarrays of tissue biopsies, we developed a high-throughput DESI-MS (HT-DESI-MS) method for the rapid characterization of disease state, human brain tumor type, glioma classification, and detection of IDH mutations in tissue microarrays (TMA) of banked and fresh human brain tissue biopsies. We anticipate that HT-DESI-MS analysis of TMAs could become a standard tool for the generation of spectral libraries for sample classification, the identification of biomarkers through large-scale studies, the correlation of molecular features with anatomical features when coupled to digital pathology, and the assessment of drug efficacy.