Marta Sans1, Jialing Zhang1, John Q Lin1, Clara L Feider1, Noah Giese1, Michael T Breen2, Katherine Sebastian3, Jinsong Liu4, Anil K Sood5, Livia S Eberlin6. 1. Department of Chemistry, The University of Texas at Austin, Austin, TX. 2. Department of Women's Health, Dell Medical School, The University of Texas at Austin, Austin, TX. 3. Department of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX. 4. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX. 5. Department of Gynecologic Oncology and Reproductive Medicine, and the Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX. 6. Department of Chemistry, The University of Texas at Austin, Austin, TX; liviase@utexas.edu.
Abstract
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.
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.
Authors: Menelaos Tzafetas; Anita Mitra; Maria Paraskevaidi; Zsolt Bodai; Ilkka Kalliala; Sarah Bowden; Konstantinos Lathouras; Francesca Rosini; Marcell Szasz; Adele Savage; Julia Balog; James McKenzie; Deirdre Lyons; Phillip Bennett; David MacIntyre; Sadaf Ghaem-Maghami; Zoltan Takats; Maria Kyrgiou Journal: Proc Natl Acad Sci U S A Date: 2020-03-16 Impact factor: 11.205
Authors: Hannah Marie Brown; Clint M Alfaro; Valentina Pirro; Mahua Dey; Eyas M Hattab; Aaron A Cohen-Gadol; R Graham Cooks Journal: J Appl Lab Med Date: 2021-07-07
Authors: Pierre-Maxence Vaysse; Imke Demers; Mari F C M van den Hout; Wouter van de Worp; Ian G M Anthony; Laura W J Baijens; Bing I Tan; Martin Lacko; Lauretta A A Vaassen; Auke van Mierlo; Ramon C J Langen; Ernst-Jan M Speel; Ron M A Heeren; Tiffany Porta Siegel; Bernd Kremer Journal: Anal Chem Date: 2022-05-03 Impact factor: 8.008
Authors: Mary E King; Jialing Zhang; John Q Lin; Kyana Y Garza; Rachel J DeHoog; Clara L Feider; Alena Bensussan; Marta Sans; Anna Krieger; Sunil Badal; Michael F Keating; Spencer Woody; Sadhna Dhingra; Wendong Yu; Christopher Pirko; Kirtan A Brahmbhatt; George Van Buren; William E Fisher; James Suliburk; Livia S Eberlin Journal: Proc Natl Acad Sci U S A Date: 2021-07-13 Impact factor: 11.205