Marta Sans (Presenter)
The University of Texas
Bio: Marta Sans is a doctoral candidate from the University of Texas at Austin, working under Prof. Livia Eberlin. Originally from Barcelona, Spain, Marta moved to the USA for her undergraduate studies.
Authorship: Marta Sans (1), Jialing Zhang (1), John Rector (1,2), John Lin (1), Jonathan H. Young (1), Nitesh Katta (2), Noah Giese (1), Wendong Yu (3), Chandandeep Nagi (3), James Suliburk (4), Jinsong Liu (5), Alena Bensussan (1), Rachel J. DeHoog (1), Kyana Y. Garza (1), Benjamin Ludolph (1), Anna G. Sorace (6), Anum Syed (2), Aydin Zahedivash (2), Thomas Milner (2), and Livia S. Eberlin(1)*
(1) Department of Chemistry, The University of Texas at Austin, Austin, TX (2) Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX (3) Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX (4) Department of Surgery, Baylor College of Medicine, Houston (5) Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX (6) Department of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX
Conventional methods for histopathologic tissue diagnosis can delay decision-making during diagnostic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid diagnosis of human cancer tissues. Statistical classifiers based on lipid and metabolites allowed prediction with high sensitivity (96.4%), selectivity (96.2%) and overall accuracy (96.3%) for breast, lung, thyroid and ovarian cancer. Lastly, we applied the device for in vivo cancer diagnosis during surgery performed in tumor-beating mice models. Our results suggest this technology as a potential clinical and intraoperative tool for ex vivo and in vivo cancer diagnosis.
Accurate negative margin assessment and complete tumor excision are highly desirable across all cancer surgeries as they offer the greatest potential for prolonged disease-free and overall survival (1-6). However, intraoperative assessment of the extent of tumor involvement can be challenging through conventional histopathologic analysis of frozen sections (7). Therefore, margin specimens are frequently processed post-operatively over several days as permanent specimens. Yet, when positive margins are found by final pathologic evaluation, the patient is subjected to additional surgical procedure for re-excision of the involved margin, which increases health care costs, and places the patient at risk for additional surgical complications, discomfort and anxiety (5, 6). In the last decade, a variety of ambient ionization mass spectrometry (MS) techniques have been developed for rapid molecular diagnosis of cancer tissues, but technical incompatibilities have prevented their use for fresh tissue and in vivo analyses (8,9). We developed an automated and biocompatible handheld device for direct, real-time non-destructive sampling and molecular diagnosis of tissues. The device was tested for ex vivo molecular evaluation of human tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular information including diagnostic metabolites, lipids and proteins species. Statistical analysis using the least absolute shrinkage and selector operator (Lasso) technique allowed prediction of cancer with high sensitivity and specificity (10, 11). Furthermore, we demonstrate that this technology is suited for in vivo use and diagnosis of human breast cancer during surgery performed in tumor-beating mouse model.
Human tissue samples from 254 patients including normal and cancer breast, lung, thyroid, and ovary tissues obtained from the Cooperative Human Tissue Network (CHTN), Asterand Biosciences (Detroit, MI), the MD Anderson Tissue Bank, and the Baylor College of Medicine Tissue Bank. Tissue sections were kept frozen until analysis, and analyzed at room temperature. All experiments were performed on a Q Exactive hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, San Jose, CA). Tissue sections were then H&E stained and evaluated by histopathology. The pathologic diagnosis was used as the reference for our molecular database. In vivo experiments were performed during surgical resection of tumors using murine animal models. For classification, we applied the Lasso method using the glmnet package in the CRAN R language library. Classification was performed using a leave-one-out cross-validation approach to assess the predictive accuracy within the training set. Performance of trained classifiers was measured by sensitivity, specificity, accuracy, and AUC.
We designed an automated and biocompatible handheld sampling probe that allows gentle, time- and volume-controlled extraction of molecules from a tissue sample using a discrete water droplet. The system contains a syringe pump to deliver a defined water volume to the sampling probe; polytetrafluoroethylene (PTFE) tubing conduits for controlled solvent transport from pump to tissue, and from the tissue to the mass spectrometer; and a handheld pen-sized probe for direct sampling of biological tissues. The entire process from sampling (3 s for extraction time) to mass spectral acquisition is completed in 10 s or less and is fully automated.
First, the probe was evaluated by analyzing mouse brain, thyroid and breast human thin tissue sections. Using water as a solvent to ensure biocompatibility, rich molecular information, such as metabolites, fatty acids, sphingolipids (SP), glycerolipids (GL) and glycerophospholipids (GP) and doubly charged cardiolipins (CL) were observed in the negative ion mode and positive ion modes with high reproducibility. Principal component analysis (PCA) performed on the data obtained from the human tissue sections analyzed showed separation between cancer and normal tissues. Then, the probe was applied for the analysis of soft tissue samples from different organs. We analyzed a total of 254 human tissue specimens, including 96 lung, 57 ovary, 57 thyroid, and 45 breast samples; and applied the Lasso method to build classification models diagnostic and predictive of disease state. Overall, 96.4% sensitivity, 96.2% selectivity and 96.3% accuracy was achieved for cancer diagnosis. Finally, we tested the probe for in vivo tissue analysis using a murine model of human breast cancer. The mouse tumor was analyzed during surgery with no macroscopic or microscopic damage to the tissues. Many molecular species also observed in human breast cancer were obtained in the mass spectra. These results suggest that this system is suitable for ex vivo and in vivo molecular evaluation and cancer diagnosis.
Conclusions & Discussion
We developed an automated and biocompatible handheld sampling probe that allows gentle, time- and volume-controlled extraction of molecules from a tissue sample using a discrete water droplet. Our results provide evidence that the probe is suited for rapid ex vivo and in vivo cancer diagnosis of tissue samples. Importantly, the chemical extraction process employed is gentle, so that the tissue is undamaged after molecular analysis. The mass spectra obtained from the analysis of 20 tissue sections and 254 human tissue samples presented rich molecular information diagnostic of disease state. Statistical classifiers built from the mass spectra obtained provided high sensitivity and selectivity for cancer detection (>96%). Remarkably, experiments performed in animal models demonstrate that this technology is suited for in vivo molecular evaluation of cancer and normal tissues without causing observable tissue harm or evident stress to the animal.
References & Acknowledgements:
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10. R. Tibshirani, Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B-Methodological 58, 267-288 (1996).
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This work was supported by the National Cancer Institute of the National Institutes of Health under award R00CA190783 (to L.S.E.) and the startup funds provided to L.S.E. by the University of Texas at Austin. We thank the Eberlin and Milner laboratory members for valuable discussions. We are greatful to Prof. Robert Tibshirani (Stanford University) for assistance for statistical analysis. We also thank Tim Hooper for his help with hardware design and Dr. Emily Que for use of microscope.
IP Royalty: no
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