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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.

Supplementary Figures and Supplementary Figure legends. Supplementary Fig. 1.- Tandem mass spectrum of m/z 810.527 and the tentative assignment, Supplementary Fig. 2.- Tandem mass spectrum of m/z 723.478 and the tentative assignment, Supplementary Fig. 3.- Comparison of mass spectra of kidney cortex tissue sections from normal patient, Supplementary Fig. 4.- Comparison of mass spectra from normal patients which contain both cortex and medulla, Supplementary Fig. 5.- PCA analysis of normal kidney tissues containing both cortex and medulla and the biplot, Supplementary Fig. 6.- The graphical summary from PCA of cortex and medulla tissue for six selected ions, Supplementary Fig. 7.- Tandem mass spectrum of m/z 887.557 and the tentative assignment, Supplementary Fig. 8.- Tentative peak assignment for m/z 215.032, Supplementary Fig. 9.- Tandem mass spectrum of m/z 175.025 and the tentative assignment, Supplementary Fig. 10.- Tandem mass spectrum of m/z 885.547 and the tentative assignment, Supplementary Fig. 11.- Tandem mass spectrum of m/z 773.532 and the tentative assignment, Supplementary Fig. 12.- Tandem mass spectrum of m/z 766.538 and the tentative assignment, Supplementary Fig. 13.- Tandem mass spectrum of m/z 1035.772 and the tentative assignment, Supplementary Fig. 14.- Tandem mass spectrum of m/z 737.490 and the tentative assignment, Supplementary Fig. 15.- Tandem mass spectrum of m/z 464.314, m/z 697.482, and m/z 842.589 and the tentative assignment, Supplementary Fig. 16.- Tandem mass spectrum of m/z 306.077 and the tentative assignment, Supplementary Fig. 17.- Tandem mass spectrum of m/z 712.485 and the tentative assignment, Supplementary Fig. 18.- Mass spectrometric profile from normal kidney tissue at mass range from m/z 723 to m/z 728, Supplementary Fig. 19.- PCA analysis of averaged mass spectra of ROchRCC tissues and ccRCCppRCC tissues and the biplot. Supplementary Tables. Supplementary Table 1. m/z values selected by the lasso as important for characterizing normal kidney, RO and RCC, Supplementary Table 2. m/z values selected by the lasso as important for characterizing RO and chRCC, Supplementary Table 3. CL and PE related species selected by the lasso for characterizing RO and chRCC, Supplementary Table 4. m/z values selected by the lasso as important for characterizing three renal cancer subtypes (ccRCC, chRCC, and ppRCC), Supplementary Table 5. The top loading plots of PC1 and PC2 for PCA of cortex and medulla in normal kidney tissue, Supplementary Table 6. The top loading plots of PC1 and PC2 for PCA of ROchRCC vs ppRCCccRCC. Supplementary Data Supplementary Results, Supplementary Methods, Supplementary Discussion

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.

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.