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Complete tumor removal during breast conserving surgery (BCS) remains a challenge for even the most experienced surgeons due to difficulties associated with detecting residual disease at the margin. Additionally, there is limited technology to evaluate resected tissue while the patient is under anesthesia. Mass spectrometry (MS) has been previously used for rapid molecular characterization of healthy and diseased tissue and has shown potential for intraoperative use. We have developed an MS-based technology that couples a handheld and biocompatible device, the MasSpec Pen, to a mass spectrometer for non-destructive ex vivo and in vivo analysis of cancer tissue in a matter of seconds. We have used the MasSpec Pen with multivariate statistical analysis to discriminate the molecular patterns of normal breast and lymph node from invasive cancer tissue, achieving accuracies over 88%. Our results showcase the potential of the MasSpec Pen to provide surgical guidance during BCS. A total of 213 banked human tissues including normal breast and lymph node, IDC, and IDC to lymph node were obtained from the Cooperative Human Tissue Network and stored at -80°C prior to analysis. Samples were then thawed and analyzed using a mass spectrometer coupled to the MasSpec Pen. During MasSpec Pen analysis, a single water droplet is held in contact with the tissue to extract molecules that are then analyzed by a mass spectrometer, yielding a molecular fingerprint. The molecular information obtained is analyzed by the statistical model to provide a predictive diagnosis in seconds. The least absolute shrinkage and selection operator (Lasso) statistical method was used to generate predictive models comprised of molecular features indicative of healthy and diseased tissue. Analyzed regions of tissue were demarcated with a surgical ink marker, snap frozen, and sectioned at a thickness of 5µm. Tissue sections were H&E stained for pathological evaluation. The MasSpec Pen was used to analyze 79 normal and 64 breast cancer tissues. Various molecular species involved in cell metabolism, including metabolites, fatty acids, and glycerophospholipids were observed in the mass spectra. For example, glutamate was detected at a high relative abundance in breast cancer tissue while hexose was detected at a high abundance in normal breast tissue. The primary breast cancer model yielded a sensitivity and specificity over 95% for the training set (n=68) and validation set (n=22). The diagnostic capabilities of the model were further evaluated by predicting on an independent test set of samples (n=53). A sensitivity and specificity of 83.9% and 100% were achieved, demonstrating the ability of the MasSpec Pen to discriminate between normal and cancerous breast tissue. The MasSpec Pen was used to detect metastatic breast cancer in lymph node. Normal lymph nodes (n=26) and nodes with metastatic disease (n=44) were analyzed with the MasSpec Pen. For the metastatic breast cancer model, overall accuracies of 88.7% and 94.1% were achieved for the training set (n=46) and validation set (n=17), respectively. We envision the MasSpec Pen to be used in the operating room (OR) for the in vivo analysis of breast cancer. Recently, we have implemented the MasSpec Pec in the OR to evaluate the feasibility of this technology for intraoperative analysis of breast and lymph node tissue. Similar molecular species are detected in spectra obtained from the in vivo and ex vivo analysis of human breast and lymph node tissue when compared to spectra obtained from banked tissue. Continuous effort is being made to continue patient accrual and testing of the technology. Collectively, these results showcase the capability of the MasSpec Pen for breast cancer detection and its potential as an intraoperative tool for rapid breast cancer diagnosis and surgical margin evaluation during BCS. Citation Format: Kyana Y Garza, Jialing Zhang, John Q Lin, Stacey Carter, James Suliburk, Chandandeep Nagi, Livia S Eberlin. Advanced development of the MasSpec Pen technology to aid in breast cancer surgical margin evaluation and diagnosis during surgery [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-20-04.

Chemotherapy resistance represents a major problem in ovarian cancer prognosis and reliable indicators of chemotherapy sensitivity remain an unmet need. Here, we examined metabolic profiles of pre- and post-neoadjuvant chemotherapy (NACT) tumor samples from patients with high-grade serous ovarian cancer (HGSC). We performed spatially resolved, desorption electrospray ionization-mass spectrometry (DESI-MS) on sections from clinically annotated HGSC samples collected according to a laparoscopic triage algorithm from patients pre- and post-NACT. Patients were considered to be either excellent (ER) if they presented complete response or only microscopic disease left at interval surgery, or poor responders (PR) if they presented stable or progressive disease after NACT. Tumor tissues from 50 patients (29 ER and 21 PR, 13 ER matched pre and post NACT, 13 PR matched pre and post-NACT) were eligible for the analyses Data relative to the detected metabolites, were collected from epithelium and stroma separately. We first identified differentially upregulated metabolites in ER and PR groups and performed pathway analysis. The over-represented pathways in the chemo-naïve PR tissues included the nucleotide metabolism pathway in epithelial areas, and degradation of cysteine and homocysteine, metabolism of amino acids and derivatives, as well as sulfur amino acids metabolism in the stromal areas (p values FDR adjusted 0.045). In general, both the epithelium and stroma of PR tissues of chemo-naïve tumors, contained a higher number and a higher variety of lipid species compared with ER tissues. In post-NACT tumor epithelial areas, there was global downregulation of metabolites across all patient samples, compared to the pre-chemo specimens; this was more evident in ER patients. In post-NACT tumors from PR patients, glutathione synthesis and recycling pathway, and sulfite oxidation to sulfate pathway and many glycerolipids species were upregulated in the epithelium, while phosphatidic acids were upregulated in the stroma. Lastly, we evaluated the predictive power of the metabolic data extracted by the epithelial areas of chemo-naïve tissues, in classifying patients as ER or PR. We used 10-fold cross-validation to create a ridge regression model for the classification of ER and PR samples from pre-NACT tumor tissues. This model, based on 78 differentially expressed metabolites (small molecules and lipids), correctly classified patients as ER or PR with an accuracy of 76.2% (sensitivity 84.6% and specificity 62.5%). Our findings indicate altered metabolic pathways in ovarian cancer tissues based on response to NACT. The identification of differentially activated pathways, such as sulfate metabolism and glutathione metabolism in tumors with poor response, suggest novel actionable targets to overcome chemotherapy resistance in patients with HGSC. Citation Format: Sara Corvigno, Meredith Spraldin, Michael Keating, Sunil Badal, Igor Pereira, Elaine Stur, Nicholas Bateman, Waleed Barakat, Thomas Conrads, Sanghoon Lee, George Maxwell, Susan K. Lutgendorf, Premal Thaker, Jinsong Liu, Nicole Fleming, Katheleen Darcy, Livia S. Eberlin, Anil K. Sood. Metabolic profiling of pre and post treatment ovarian cancer tissues using in situ 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 2331.

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. Significance: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

Precise removal of pancreatic ductal adenocarcinoma (PDAC) with microscopically negative margins, commonly assessed by frozen section analysis, is associated with longer disease-free survival. However, histologic complexities and tissue-processing artifacts can render frozen section analysis of PDAC margins a challenging and time-consuming task, with reported accuracies dependent on the skillset and subspecialty of the pathologist on call. We developed the MasSpec Pen, a handheld device coupled to a mass spectrometer, for rapid (~15 s) and nondestructive molecular analysis and diagnosis of tissues. The MasSpec Pen supplies a discrete water droplet onto a tissue's surface, allowing diagnostic metabolites and lipids to be extracted into the droplet and then transmitted into a mass spectrometer for analysis. Here, we evaluate the performance of the MasSpec Pen for intraoperative diagnosis of PDAC in human pancreatic and bile duct margins. Pancreatic and bile duct tissue samples (N=157) were obtained from the Cooperative Human Tissue Network and Baylor College of Medicine and stored at -80°C prior to analysis. A Q Exactive mass spectrometer (Thermo Scientific) coupled to the MasSpec Pen was used for analysis of thawed samples in the negative ion mode. Tissues were then cryo-sectioned, H&E stained, and blindly evaluated by a pathologist. Based on the distinct molecular profiles acquired, we generated two statistical classifiers using lasso penalized logistic regression for distinguishing PDAC from healthy pancreas and bile duct tissue based on a sparse set of molecular features indicative of disease state. For distinguishing normal pancreas from PDAC, an overall accuracy of 91.5%, sensitivity of 95.5%, and specificity of 89.7% was achieved for training, validation, and test sets. Classification results for discriminating normal bile duct from PDAC had an overall accuracy of 95%, sensitivity of 92%, and specificity of 100% in training and validation. We have begun clinical testing of the MasSpec Pen in human surgeries following its successful translation to an operating room at Texas Medical Center. To date, the MasSpec Pen has been used to analyze in vivo and fresh ex vivo tissue in 19 pancreatic surgeries. When predicting on 64 intraoperative analyses using classification models built on banked data, 93.8% agreement with final postoperative pathology reports was achieved. While further validation studies are needed, our results show that the MasSpec Pen can distinguish PDAC from normal pancreas and bile duct tissues with high accuracy and is compatible for in vivo use, suggesting this technology may be valuable for near real-time margin evaluation during pancreatic oncologic surgeries. Citation Format: Mary E. King, Jialing Zhang, John Q. Lin, Kyana Y. Garza, Rachel J. DeHoog, Clara L. Feider, Alena V. Bensussan, Marta Sans, Anna Krieger, Sunil Badal, Michael F. Keating, Sadhna Dhingra, Wendong Yu, George Van Buren, Christopher Pirko, Kirtan Brahmbhatt, William E. Fisher, James W. Suliburk, Livia S. Eberlin. Clinical evaluation of the MasSpec Pen technology for rapid diagnosis and margin assessment in pancreatic cancer surgery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 624.

Oncocytic tumors are characterized by an excessive eosinophilic, granular cytoplasm due to aberrant accumulation of mitochondria. Mutations in mitochondrial DNA occur in oncocytic thyroid tumors, but there is no information about their lipid composition, which might reveal candidate theranostic molecules. Here, we used desorption electrospray ionization mass spectrometry (DESI-MS) to image and chemically characterize the lipid composition of oncocytic thyroid tumors, as compared with nononcocytic thyroid tumors and normal thyroid samples. We identified a novel molecular signature of oncocytic tumors characterized by an abnormally high abundance and chemical diversity of cardiolipins (CL), including many oxidized species. DESI-MS imaging and IHC experiments confirmed that the spatial distribution of CLs overlapped with regions of accumulation of mitochondria-rich oncocytic cells. Fluorescent imaging and mitochondrial isolation showed that both mitochondrial accumulation and alteration in CL composition of mitochondria occurred in oncocytic tumors cells, thus contributing the aberrant molecular signatures detected. A total of 219 molecular ions, including CLs, other glycerophospholipids, fatty acids, and metabolites, were found at increased or decreased abundance in oncocytic, nononcocytic, or normal thyroid tissues. Our findings suggest new candidate targets for clinical and therapeutic use against oncocytic tumors. Cancer Res; 76(22); 6588–97. ©2016 AACR.

Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade, and concentration features generated with lipidomic data showed high recognition capability with more than 97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79% of tested features. Together, our findings offer proof of concept that intraoperative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors. Cancer Res; 72(3); 645–54. ©2011 AACR.