Data Acquisition and Intraoperative Tissue Analysis on a Mobile, Battery-Operated, Orbitrap Mass Spectrometer.

Mass spectrometry has been increasingly explored in intraoperative studies as a potential technology to help guide surgical decision making. Yet, intraoperative experiments using high-performance mass spectrometry instrumentation present a unique set of operational challenges. For example, standard operating rooms are often not equipped with the electrical requirements to power a commercial mass spectrometer and are not designed to accommodate their permanent installation. These obstacles can impact progress and patient enrollment in intraoperative clinical studies because implementation of MS instrumentation becomes limited to specific operating rooms that have the required electrical connections and space. To expand our intraoperative clinical studies using the MasSpec Pen technology, we explored the feasibility of transporting and acquiring data on Orbitrap mass spectrometers operating on battery power in hospital buildings. We evaluated the effect of instrument movement including acceleration and rotational speeds on signal stability and mass accuracy by acquiring data using direct infusion electrospray ionization. Data were acquired while rolling the systems in/out of operating rooms and while descending/ascending a freight elevator. Despite these movements and operating the instrument on battery power, the relative standard deviation of the total ion current was <5% and the magnitude of the mass error relative to the internal calibrant never exceeded 5.06 ppm. We further evaluated the feasibility of performing intraoperative MasSpec Pen analysis while operating the Orbitrap mass spectrometer on battery power during an ovarian cancer surgery. We observed that the rich and tissue-specific molecular profile commonly detected from ovarian tissues was conserved when running on battery power. Together, these results demonstrate that Orbitrap mass spectrometers can be operated and acquire data on battery power while in motion and in rotation without losses in signal stability or mass accuracy. Furthermore, Orbitrap mass spectrometers can be used in conjunction to the MasSpec Pen while on battery power for intraoperative tissue analysis.

Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen.

Importance: Surgery with complete tumor resection remains the main treatment option for patients with breast cancer. Yet, current technologies are limited in providing accurate assessment of breast tissue in vivo, warranting development of new technologies for surgical guidance. Objective: To evaluate the performance of the MasSpec Pen for accurate intraoperative assessment of breast tissues and surgical margins based on metabolic and lipid information. Design, Setting, and Participants: In this diagnostic study conducted between February 23, 2017, and August 19, 2021, the mass spectrometry-based device was used to analyze healthy breast and invasive ductal carcinoma (IDC) banked tissue samples from adult patients undergoing breast surgery for ductal carcinomas or nonmalignant conditions. Fresh-frozen tissue samples and touch imprints were analyzed in a laboratory. Intraoperative in vivo and ex vivo breast tissue analyses were performed by surgical staff in operating rooms (ORs) within 2 different hospitals at the Texas Medical Center. Molecular data were used to build statistical classifiers. Main Outcomes and Measures: Prediction results of tissue analyses from classification models were compared with gross assessment, frozen section analysis, and/or final postoperative pathology to assess accuracy. Results: All data acquired from the 143 banked tissue samples, including 79 healthy breast and 64 IDC tissues, were included in the statistical analysis. Data presented rich molecular profiles of healthy and IDC banked tissue samples, with significant changes in relative abundances observed for several metabolic species. Statistical classifiers yielded accuracies of 95.6%, 95.5%, and 90.6% for training, validation, and independent test sets, respectively. A total of 25 participants enrolled in the clinical, intraoperative study; all were female, and the median age was 58 years (IQR, 44-66 years). Intraoperative testing of the technology was successfully performed by surgical staff during 25 breast operations. Of 273 intraoperative analyses performed during 25 surgical cases, 147 analyses from 22 cases were subjected to statistical classification. Testing of the classifiers on 147 intraoperative mass spectra yielded 95.9% agreement with postoperative pathology results. Conclusions and Relevance: The findings of this diagnostic study suggest that the mass spectrometry-based system could be clinically valuable to surgeons and patients by enabling fast molecular-based intraoperative assessment of in vivo and ex vivo breast tissue samples and surgical margins.

Adipose Triglyceride Lipase Is a Therapeutic Target in Advanced Prostate Cancer That Promotes Metabolic Plasticity.

Lipid metabolism plays a central role in prostate cancer. To date, the major focus has centered on de novo lipogenesis and lipid uptake in prostate cancer, but inhibitors of these processes have not benefited patients. A better understanding of how cancer cells access lipids once they are created or taken up and stored could uncover more effective strategies to perturb lipid metabolism and treat patients. Here, we identified that expression of adipose triglyceride lipase (ATGL), an enzyme that controls lipid droplet homeostasis and a previously suspected tumor suppressor, correlates with worse overall survival in men with advanced, castration-resistant prostate cancer (CRPC). Molecular, genetic, or pharmacologic inhibition of ATGL impaired human and murine prostate cancer growth in vivo and in cell culture or organoids under conditions mimicking the tumor microenvironment. Mass spectrometry imaging demonstrated that ATGL profoundly regulates lipid metabolism in vivo, remodeling membrane composition. ATGL inhibition induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by cotargeting both metabolic pathways. Patient-derived phosphoproteomics identified ATGL serine 404 as a target of CAMKK2-AMPK signaling in CRPC cells. Mutation of serine 404 did not alter the lipolytic activity of ATGL but did decrease CRPC growth, migration, and invasion, indicating that noncanonical ATGL activity also contributes to disease progression. Unbiased immunoprecipitation/mass spectrometry suggested that mutation of serine 404 not only disrupts existing ATGL protein interactions but also leads to new protein-protein interactions. Together, these data nominate ATGL as a therapeutic target for CRPC and provide insights for future drug development and combination therapies. SIGNIFICANCE: ATGL promotes prostate cancer metabolic plasticity and progression through both lipase-dependent and lipase-independent activity, informing strategies to target ATGL and lipid metabolism for cancer treatment.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy.

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Intraoperative Identification of Thyroid and Parathyroid Tissues During Human Endocrine Surgery Using the MasSpec Pen.

Importance: Intraoperative identification of tissues through gross inspection during thyroid and parathyroid surgery is challenging yet essential for preserving healthy tissue and improving outcomes for patients. Objective: To evaluate the performance and clinical applicability of the MasSpec Pen (MSPen) technology for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively. Design, Setting, and Participants: In this diagnostic/prognostic study, the MSPen was used to analyze 184 fresh-frozen thyroid, parathyroid, and lymph node tissues in the laboratory and translated to the operating room to enable in vivo and ex vivo tissue analysis by endocrine surgeons in 102 patients undergoing thyroidectomy and parathyroidectomy procedures. This diagnostic study was conducted between August 2017 and March 2020. Fresh-frozen tissues were analyzed in a laboratory. Clinical analyses occurred in an operating room at an academic medical center. Of the analyses performed on 184 fresh-frozen tissues, 131 were included based on sufficient signal and postanalysis pathologic diagnosis. From clinical tests, 102 patients undergoing surgery were included. A total of 1015 intraoperative analyses were performed, with 269 analyses subject to statistical classification. Statistical classifiers for discriminating thyroid, parathyroid, and lymph node tissues were generated using training sets comprising both laboratory and intraoperative data and evaluated on an independent test set of intraoperative data. Data were analyzed from July to December 2022. Main Outcomes and Measures: Accuracy for each tissue type was measured for classification models discriminating thyroid, parathyroid, and lymph node tissues using MSPen data compared to gross analysis and final pathology results. Results: Of the 102 patients in the intraoperative study, 80 were female (78%) and the median (IQR) age was 52 (42-66) years. For discriminating thyroid and parathyroid tissues, an overall accuracy, defined as agreement with pathology, of 92.4% (95% CI, 87.7-95.4) was achieved using MSPen data, with 82.6% (95% CI, 76.5-87.4) accuracy achieved for the independent test set. For distinguishing thyroid from lymph node and parathyroid from lymph node, overall training set accuracies of 97.5% (95% CI, 92.8-99.1) and 96.1% (95% CI, 91.2-98.3), respectively, were achieved. Conclusions and Relevance: In this study, the MSPen showed high performance for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively, suggesting this technology may be useful for providing near real-time feedback on tissue type to aid in surgical decision-making.

Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.

PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Evaluating the Generalizability of Predictive Classifiers Built from DESI Imaging Lipid Data across Mass Spectrometry Platforms.

In this study, we evaluate the generalizability of predictive classifiers built from DESI lipid data for thyroid fine needle aspiration (FNA) biopsy analysis and classification using two high-performance mass spectrometers (time-of-flight and orbitrap) suited with different DESI imaging sources operated by different users. The molecular profiles obtained from thyroid samples with the different platforms presented similar trends, although specific differences in ion abundances were observed. When using a previously published statistical model built to discriminate thyroid cancer from benign thyroid tissues to predict on a new independent data set obtained, agreement for 24 of the 30 samples across the imaging platforms was achieved. We also tested the classifier on six clinical FNAs and obtained agreement between the predictive results and clinical diagnosis for the different conditions. Altogether, our results provide evidence that statistical classifiers generated from DESI lipid data are applicable across different high-resolution mass spectrometry platforms for thyroid FNA classification.

Mass Spectrometry Imaging Reveals Abnormalities in Cardiolipin Composition and Distribution in Astrocytoma Tumor Tissues.

Cardiolipin (CL) is a mitochondrial lipid with diverse roles in cellular respiration, signaling, and organelle membrane structure. CL content and composition are essential for proper mitochondrial function. Deranged mitochondrial energy production and signaling are key components of glial cell cancers and altered CL molecular species have been observed in mouse brain glial cell xenograft tumors. The objective of this study was to describe CL structural diversity trends in human astrocytoma tumors of varying grades and correlate these trends with histological regions within the heterogeneous astrocytoma microenvironment. To this aim, we applied desorption electrospray ionization coupled with high field asymmetric ion mobility mass spectrometry (DESI-FAIMS-MS) to map CL molecular species in human normal cortex (N = 29), lower-grade astrocytoma (N = 19), and glioblastoma (N = 28) tissues. With this platform, we detected 46 CL species and 12 monolysocardiolipin species from normal cortex samples. CL profiles detected from glioblastoma tissues lacked diversity and abundance of longer chain polyunsaturated fatty acid containing CL species when compared to CL detected from normal and lower-grade tumors. CL profiles correlated with trends in tumor viability and tumor infiltration. Structural characterization of the CL species by tandem MS experiments revealed differences in fatty acid and double bond isomer composition among astrocytoma tissues compared with normal cortex and glioblastoma tissues. The GlioVis platform was used to analyze astrocytoma gene expression data from the CGGA dataset. Decreased expression of several mitochondrial respiratory enzyme encoding-genes was observed for higher-grade versus lower-grade tumors, however no significant difference was observed for cardiolipin synthesis enzyme CRLS1.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis.

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Exploiting metabolic vulnerabilities after anti-VEGF antibody therapy in ovarian cancer.

Despite modest clinical improvement with anti-vascular endothelial growth factor antibody (AVA) therapy in ovarian cancer, adaptive resistance is ubiquitous and additional options are limited. A dependence on glutamine metabolism, via the enzyme glutaminase (GLS), is a known mechanism of adaptive resistance and we aimed to investigate the utility of a GLS inhibitor (GLSi). Our in vitro findings demonstrated increased glutamine abundance and a significant cytotoxic effect in AVA-resistant tumors when GLSi was administered in combination with bevacizumab. In vivo, GLSi led to a reduction in tumor growth as monotherapy and when combined with AVA. Furthermore, GLSi initiated after the emergence of resistance to AVA therapy resulted in a decreased metabolic conversion of pyruvate to lactate as assessed by hyperpolarized magnetic resonance spectroscopy and demonstrated robust antitumor effects with a survival advantage. Given the increasing population of patients receiving AVA therapy, these findings justify further development of GLSi in AVA resistance.

Long-chain polyunsaturated lipids associated with responsiveness to anti-PD-1 therapy are colocalized with immune infiltrates in the tumor microenvironment.

The programmed cell death protein-1 (PD-1) is highly expressed on the surface of antigen-specific exhausted T cells and, upon interaction with its ligand PD-L1, can result in inhibition of the immune response. Anti-PD-1 treatment has been shown to extend survival and result in durable responses in several cancers, yet only a subset of patients benefit from this therapy. Despite the implication of metabolic alteration following cancer immunotherapy, mechanistic associations between antitumor responses and metabolic changes remain unclear. Here, we used desorption electrospray ionization mass spectrometry imaging to examine the lipid profiles of tumor tissue from three syngeneic murine models with varying treatment sensitivity at the baseline and at three time points post-anti-PD-1 therapy. These imaging experiments revealed specific alterations in the lipid profiles associated with the degree of response to treatment and allowed us to identify a significant increase of long-chain polyunsaturated lipids within responsive tumors following anti-PD-1 therapy. Immunofluorescence imaging of tumor tissues also demonstrated that the altered lipid profile associated with treatment response is localized to dense regions of tumor immune infiltrates. Overall, these results indicate that effective anti-PD-1 therapy modulates lipid metabolism in tumor immune infiltrates, and we thereby propose that further investigation of the related immune-metabolic pathways may be useful for better understanding success and failure of anti-PD-1 therapy.

Classification of High-Grade Serous Ovarian Cancer Using Tumor Morphologic Characteristics.

Importance: Despite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics. Objective: To develop and characterize a gross morphologic classification system for HGSOC. Design, Setting, and Participants: This cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021. Exposures: Gross tumor morphologic characteristics. Main Outcomes and Measures: Clinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared. Results: Of 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10-24), hypoxia (FDR q-value, 1.52 × 10-5), and angiogenesis pathways (FDR q-value, 2.11 × 10-2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10-9) and cell cycle progression (FDR q-value, 1.10 × 10-5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes. Conclusions and Relevance: This study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging.

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Relative Quantitation of Unsaturated Phosphatidylcholines Using 193 nm Ultraviolet Photodissociation Parallel Reaction Monitoring Mass Spectrometry.

Structural characterization of glycerophospholipids beyond the fatty acid level has become a major endeavor in lipidomics, presenting an opportunity to advance the understanding of the intricate relationship between lipid metabolism and disease state. Distinguishing subtle lipid structural features, however, remains a major challenge for high-throughput workflows that implement traditional tandem mass spectrometry (MS/MS) techniques, stunting the molecular depth of quantitative strategies. Here, reversed phase liquid chromatography is coupled to parallel reaction mass spectrometry utilizing the double bond localization capabilities of ultraviolet photodissociation (UVPD) mass spectrometry to produce double bond isomer specific responses that are leveraged for relative quantitation. The strategy provides lipidomic characterization at the double bond level for phosphatidylcholine phospholipids from biological extracts. In addition to quantifying monounsaturated lipids, quantitation of phospholipids incorporating isomeric polyunsaturated fatty acids is also achieved. Using this technique, phosphatidylcholine isomer ratios are compared across human normal and tumor breast tissue to reveal significant structural alterations related to disease state.

Clinical Translation and Evaluation of a Handheld and Biocompatible Mass Spectrometry Probe for Surgical Use.

BACKGROUND: Intraoperative tissue analysis and identification are critical to guide surgical procedures and improve patient outcomes. Here, we describe the clinical translation and evaluation of the MasSpec Pen technology for molecular analysis of in vivo and freshly excised tissues in the operating room (OR). METHODS: An Orbitrap mass spectrometer equipped with a MasSpec Pen interface was installed in an OR. A "dual-path" MasSpec Pen interface was designed and programmed for the clinical studies with 2 parallel systems that facilitated the operation of the MasSpec Pen. The MasSpec Pen devices were autoclaved before each surgical procedure and were used by surgeons and surgical staff during 100 surgeries over a 12-month period. RESULTS: Detection of mass spectral profiles from 715 in vivo and ex vivo analyses performed on thyroid, parathyroid, lymph node, breast, pancreatic, and bile duct tissues during parathyroidectomies, thyroidectomies, breast, and pancreatic neoplasia surgeries was achieved. The MasSpec Pen enabled gentle extraction and sensitive detection of various molecular species including small metabolites and lipids using a droplet of sterile water without causing apparent tissue damage. Notably, effective molecular analysis was achieved while no limitations to sequential histologic tissue analysis were identified and no device-related complications were reported for any of the patients. CONCLUSIONS: This study shows that the MasSpec Pen system can be successfully incorporated into the OR, allowing direct detection of rich molecular profiles from tissues with a seconds-long turnaround time that could be used to inform surgical and clinical decisions without disrupting tissue analysis workflows.

Rapid diagnosis and tumor margin assessment during pancreatic cancer surgery with the MasSpec Pen technology.

Intraoperative delineation of tumor margins is critical for effective pancreatic cancer surgery. Yet, intraoperative frozen section analysis of tumor margins is a time-consuming and often challenging procedure that can yield confounding results due to histologic heterogeneity and tissue-processing artifacts. We have previously described the development of the MasSpec Pen technology as a handheld mass spectrometry-based device for nondestructive tissue analysis. Here, we evaluated the usefulness of the MasSpec Pen for intraoperative diagnosis of pancreatic ductal adenocarcinoma based on alterations in the metabolite and lipid profiles in in vivo and ex vivo tissues. We used the MasSpec Pen to analyze 157 banked human tissues, including pancreatic ductal adenocarcinoma, pancreatic, and bile duct tissues. Classification models generated from the molecular data yielded an overall agreement with pathology of 91.5%, sensitivity of 95.5%, and specificity of 89.7% for discriminating normal pancreas from cancer. We built a second classifier to distinguish bile duct from pancreatic cancer, achieving an overall accuracy of 95%, sensitivity of 92%, and specificity of 100%. We then translated the MasSpec Pen to the operative room and predicted on in vivo and ex vivo data acquired during 18 pancreatic surgeries, achieving 93.8% overall agreement with final postoperative pathology reports. Notably, when integrating banked tissue data with intraoperative data, an improved agreement of 100% was achieved. The result obtained demonstrate that the MasSpec Pen provides high predictive performance for tissue diagnosis and compatibility for intraoperative use, suggesting that the technology may be useful to guide surgical decision-making during pancreatic cancer surgeries.

MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE.

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.

Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging.

BACKGROUND: Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples. METHODS: DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA. RESULTS: Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide. CONCLUSIONS: DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.