Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer Marta Sans, Jialing Zhang, John Q. Lin, Clara L. Feider, Noah Giese, Michael T. Breen, Katherine Sebastian, Jinsong Liu, Anil K. Sood, and Livia S. Eberlin Clinical Chemistry 2019
Background: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. Methods: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. Results: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. Conclusions: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
Multicenter Study Using Desorption-Electrospray-Ionization-Mass-Spectrometry Imaging for Breast-Cancer Diagnosis Andreia M. Porcari, Jialing Zhang, Kyana Y. Garza, Raquel M. Rodrigues-Peres, John Q. Lin, Jonathan H. Young, Robert Tibshirani, Chandandeep Nagi, Geisilene R. Paiva, Stacey A. Carter, Luis Otávio Sarian, Marcos N. Eberlin, and Livia S. Eberlin Analytical Chemistry 2018
The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios (m/z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (>89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.
Desorption Electrospray Ionization Coupled with Ultraviolet Photodissociation for Characterization of Phospholipid Isomers in Tissue Sections Dustin R. Klein, Clara L. Feider, Kyana Y. Garza, John Q. Lin, Livia S. Eberlin, and Jennifer S. Brodbelt Analytical Chemistry 2018
Desorption electrospray ionization (DESI) mass spectrometry imaging has become a powerful strategy for analysis of tissue sections, enabling differentiation of normal and diseased tissue based on changes in the lipid profiles. The most common DESI workflow involves collection of MS1 spectra as the DESI spray is rastered over a tissue section. Relying on MS1 spectra inherently limits the ability to differentiate isobaric and isomeric species or evaluate variations in the relative abundances of key isomeric lipids, such as double-bond positional isomers which may distinguish normal and diseased tissues. Here, 193 nm ultraviolet photodissociation (UVPD), a technique capable of differentiating double-bond positional isomers, is coupled with DESI to map differences in the double-bond isomer composition in tissue sections in a fast, high throughput manner compatible with imaging applications.
Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system Jialing Zhang, John Rector, John Q. Lin, Jonathan H. Young, Marta Sans, Nitesh Katta, Noah Giese, Wendong Yu, Chandandeep Nagi, James Suliburk, Jinsong Liu, Alena Bensussan, Rachel J. DeHoog, Kyana Y. Garza, Benjamin Ludolph, Anna G. Sorace, Anum Syed, Aydin Zahedivash, Thomas E. Milner, and Livia S. Eberlin Science Translational Medicine 2017
Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis.
Cardiolipins Are Biomarkers of Mitochondria-Rich Thyroid Oncocytic Tumors Jialing Zhang, Wendong Yu, Seung Woo Ryu, John Lin, Gerardo Buentello, Robert Tibshirani, James Suliburk, and Livia S. Eberlin Cancer Research 2016
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.
Development of the MasSpec Pen Technology Towards Clinical Translation John Q. Lin, Jialing Zhang, Marta Sans, Noah Giese, Clara Feider, Nitesh Katta, Kevin Jian Yee, Rachel J. DeHoog, Kyana Garza, Mary King, Anna C. Krieger, Alena Bensussan, Qiuyu Li, Wendong Yu, James Suliburk, Thomas Milner, and Livia S. Eberlin Oral at 53rd Annual ACLPS Meeting 2018
Lipid Characterization in Genetically Engineered Yarrowia lipolytica by Desorption Electrospray Ionization Mass Spectrometry John Q. Lin, Lauren T. Cordova, Hal S. Alper, and Livia S. Eberlin Poster at ASMS 65th Conference on Mass Spectrometry and Allied Topics 2017