1
|
Grajales D, Le WT, Tran T, David S, Dallaire F, Ember K, Leblond F, Ménard C, Kadoury S. Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room. Int J Comput Assist Radiol Surg 2024; 19:1103-1111. [PMID: 38573566 DOI: 10.1007/s11548-024-03100-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence. METHODS A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth. RESULTS The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively. CONCLUSION Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.
Collapse
Affiliation(s)
- David Grajales
- Polytechnique Montréal, Montréal, QC, Canada.
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada.
| | - William T Le
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Trang Tran
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Sandryne David
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Katherine Ember
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
- Institut du Cancer de Montréal, Montréal, QC, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Samuel Kadoury
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| |
Collapse
|
2
|
Cafarella C, Mangraviti D, Rigano F, Dugo P, Mondello L. Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications. J Sep Sci 2024; 47:e2400155. [PMID: 38772742 DOI: 10.1002/jssc.202400155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.
Collapse
Affiliation(s)
- Cinzia Cafarella
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Domenica Mangraviti
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Francesca Rigano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Paola Dugo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| |
Collapse
|
3
|
Vaysse PM, van den Hout MFCM, Engelen SME, Keymeulen KBMI, Bemelmans MHA, Heeren RMA, Olde Damink SWM, Porta Siegel T. Lipid profiling of electrosurgical vapors for real-time assistance of soft tissue sarcoma resection. J Surg Oncol 2024; 129:499-508. [PMID: 38050894 DOI: 10.1002/jso.27502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/03/2023] [Accepted: 10/15/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Soft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient-tailored surgical resection. Real-time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative-tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue. METHODS Twenty-seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis-linear discriminant analysis) and leave-one patient-out cross-validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS. RESULTS In total, 1200 histopathologically-validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well-differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals. CONCLUSION Lipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.
Collapse
Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mari F C M van den Hout
- Department of Pathology, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sanne M E Engelen
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Marc H A Bemelmans
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
| | - Steven W M Olde Damink
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany
- NUTRIM School of Nutrition and Translational Research in Metabolism Faculty of Health, Maastricht University, Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging institute (M4i), University of Maastricht, Maastricht, The Netherlands
| |
Collapse
|
4
|
Vlocskó M, Piffkó J, Janovszky Á. Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy. Cancers (Basel) 2023; 16:121. [PMID: 38201548 PMCID: PMC10777979 DOI: 10.3390/cancers16010121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.
Collapse
Affiliation(s)
| | | | - Ágnes Janovszky
- Department of Oral and Maxillofacial Surgery, Albert Szent-Györgyi Medical School, University of Szeged, Kálvária 57, H-6725 Szeged, Hungary; (M.V.); (J.P.)
| |
Collapse
|
5
|
Akbari B, Huber BR, Sherman JH. Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Crit Rev Anal Chem 2023:1-30. [PMID: 37847593 DOI: 10.1080/10408347.2023.2266838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.
Collapse
Affiliation(s)
- Behnaz Akbari
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | - Bertrand Russell Huber
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- US Department of Veteran Affairs, VA Boston Healthcare System, Boston, Massachusetts USA
- US Department of Veterans Affairs, National Center for PTSD, Boston, Massachusetts USA
| | - Janet Hope Sherman
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts, USA
| |
Collapse
|
6
|
DeHoog RJ, King ME, Keating MF, Zhang J, Sans M, Feider CL, Garza KY, Bensussan A, Krieger A, Lin JQ, Badal S, Alore E, Pirko C, Brahmbhatt K, Yu W, Grogan R, Eberlin LS, Suliburk J. Intraoperative Identification of Thyroid and Parathyroid Tissues During Human Endocrine Surgery Using the MasSpec Pen. JAMA Surg 2023; 158:1050-1059. [PMID: 37531134 PMCID: PMC10398548 DOI: 10.1001/jamasurg.2023.3229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 08/03/2023]
Abstract
Importance Intraoperative identification of tissues through gross inspection during thyroid and parathyroid surgery is challenging yet essential for preserving healthy tissue and improving outcomes for patients. Objective To evaluate the performance and clinical applicability of the MasSpec Pen (MSPen) technology for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively. Design, Setting, and Participants In this diagnostic/prognostic study, the MSPen was used to analyze 184 fresh-frozen thyroid, parathyroid, and lymph node tissues in the laboratory and translated to the operating room to enable in vivo and ex vivo tissue analysis by endocrine surgeons in 102 patients undergoing thyroidectomy and parathyroidectomy procedures. This diagnostic study was conducted between August 2017 and March 2020. Fresh-frozen tissues were analyzed in a laboratory. Clinical analyses occurred in an operating room at an academic medical center. Of the analyses performed on 184 fresh-frozen tissues, 131 were included based on sufficient signal and postanalysis pathologic diagnosis. From clinical tests, 102 patients undergoing surgery were included. A total of 1015 intraoperative analyses were performed, with 269 analyses subject to statistical classification. Statistical classifiers for discriminating thyroid, parathyroid, and lymph node tissues were generated using training sets comprising both laboratory and intraoperative data and evaluated on an independent test set of intraoperative data. Data were analyzed from July to December 2022. Main Outcomes and Measures Accuracy for each tissue type was measured for classification models discriminating thyroid, parathyroid, and lymph node tissues using MSPen data compared to gross analysis and final pathology results. Results Of the 102 patients in the intraoperative study, 80 were female (78%) and the median (IQR) age was 52 (42-66) years. For discriminating thyroid and parathyroid tissues, an overall accuracy, defined as agreement with pathology, of 92.4% (95% CI, 87.7-95.4) was achieved using MSPen data, with 82.6% (95% CI, 76.5-87.4) accuracy achieved for the independent test set. For distinguishing thyroid from lymph node and parathyroid from lymph node, overall training set accuracies of 97.5% (95% CI, 92.8-99.1) and 96.1% (95% CI, 91.2-98.3), respectively, were achieved. Conclusions and Relevance In this study, the MSPen showed high performance for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively, suggesting this technology may be useful for providing near real-time feedback on tissue type to aid in surgical decision-making.
Collapse
Affiliation(s)
- Rachel J. DeHoog
- Department of Surgery, Baylor College of Medicine, Houston, Texas
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Mary E. King
- Department of Surgery, Baylor College of Medicine, Houston, Texas
- Department of Chemistry, The University of Texas at Austin, Austin
| | | | - Jialing Zhang
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Marta Sans
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Clara L. Feider
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Kyana Y. Garza
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Alena Bensussan
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Anna Krieger
- Department of Chemistry, The University of Texas at Austin, Austin
| | - John Q. Lin
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Sunil Badal
- Department of Chemistry, The University of Texas at Austin, Austin
| | - Elizabeth Alore
- Department of Surgery, Baylor College of Medicine, Houston, Texas
| | | | | | - Wendong Yu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas
| | - Raymon Grogan
- Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - Livia S. Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - James Suliburk
- Department of Surgery, Baylor College of Medicine, Houston, Texas
| |
Collapse
|
7
|
Zhai Y, Fu X, Xu W. Miniature mass spectrometers and their potential for clinical point-of-care analysis. MASS SPECTROMETRY REVIEWS 2023. [PMID: 37610153 DOI: 10.1002/mas.21867] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023]
Abstract
Mass spectrometry (MS) has become a powerful technique for clinical applications with high sensitivity and specificity. Different from conventional MS diagnosis in laboratory, point-of-care (POC) analyses in clinics require mass spectrometers and analytical procedures to be friendly for novice users and applicable for on-site clinical diagnosis. The recent decades have seen the progress in the development of miniature mass spectrometers, providing a promising solution for clinical POC applications. In this review, we report recent advances of miniature mass spectrometers and their exploration in clinical applications, mainly including the rapid analysis of illegal drugs, on-site monitoring of therapeutic drugs, and detection of biomarkers. With improved analytical performance, miniature mass spectrometers are also expected to apply to more and more clinical applications. Some promising POC analyses that can be performed by miniature mass spectrometers in the future are discussed. Lastly, we also provide our perspectives on the challenges in technical development of miniature mass spectrometers for clinical POC analysis.
Collapse
Affiliation(s)
- Yanbing Zhai
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Xinyan Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Wei Xu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| |
Collapse
|
8
|
Vande Voorde J, Steven RT, Najumudeen AK, Ford CA, Dexter A, Gonzalez-Fernandez A, Nikula CJ, Xiang Y, Ford L, Maneta Stavrakaki S, Gilroy K, Zeiger LB, Pennel K, Hatthakarnkul P, Elia EA, Nasif A, Murta T, Manoli E, Mason S, Gillespie M, Lannagan TRM, Vlahov N, Ridgway RA, Nixon C, Raven A, Mills M, Athineos D, Kanellos G, Nourse C, Gay DM, Hughes M, Burton A, Yan B, Sellers K, Wu V, De Ridder K, Shokry E, Huerta Uribe A, Clark W, Clark G, Kirschner K, Thienpont B, Li VSW, Maddocks ODK, Barry ST, Goodwin RJA, Kinross J, Edwards J, Yuneva MO, Sumpton D, Takats Z, Campbell AD, Bunch J, Sansom OJ. Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target. Nat Metab 2023; 5:1303-1318. [PMID: 37580540 PMCID: PMC10447251 DOI: 10.1038/s42255-023-00857-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC, and oncogenic mutations such as mutant KRAS. Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras. Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in ApcMin/+ mice indicating its potential as a metabolic drug target in CRC.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Yuchen Xiang
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Lauren Ford
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Stefania Maneta Stavrakaki
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | | | - Lucas B Zeiger
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Kathryn Pennel
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | | | | | - Eftychios Manoli
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Sam Mason
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Michael Gillespie
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | | | - Colin Nixon
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | - Megan Mills
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | | | - Craig Nourse
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - David M Gay
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Københavns Universitet, BRIC, Copenhagen, Denmark
| | - Mark Hughes
- Cancer Research UK Beatson Institute, Glasgow, UK
| | - Amy Burton
- National Physical Laboratory, London, UK
| | - Bin Yan
- National Physical Laboratory, London, UK
| | - Katherine Sellers
- The Francis Crick Institute, London, UK
- Rheos Medicines, Cambridge, MA, USA
| | - Vincen Wu
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Kobe De Ridder
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | - Engy Shokry
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | | | - Graeme Clark
- Cancer Research UK Beatson Institute, Glasgow, UK
| | | | - Bernard Thienpont
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
| | | | | | - Simon T Barry
- Bioscience, Early Oncology, AstraZeneca, Cambridge, UK
| | - Richard J A Goodwin
- Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - James Kinross
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Joanne Edwards
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | - Zoltan Takats
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Biological Mass Spectrometry, Rosalind Franklin Institute, Didcot, UK
| | | | - Josephine Bunch
- National Physical Laboratory, London, UK
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Biological Mass Spectrometry, Rosalind Franklin Institute, Didcot, UK
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow, UK.
- School of Cancer Sciences, University of Glasgow, Glasgow, UK.
| |
Collapse
|
9
|
Sarpe C, Ciobotea ER, Morscher CB, Zielinski B, Braun H, Senftleben A, Rüschoff J, Baumert T. Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning. Sci Rep 2023; 13:9250. [PMID: 37291175 PMCID: PMC10250396 DOI: 10.1038/s41598-023-36155-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023] Open
Abstract
In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.
Collapse
Affiliation(s)
- Cristian Sarpe
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Elena Ramela Ciobotea
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | | | - Bastian Zielinski
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Hendrike Braun
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Arne Senftleben
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany
| | - Josef Rüschoff
- Institut für Pathologie Nordhessen, Germaniastr. 7, 34119, Kassel, Germany
| | - Thomas Baumert
- Institut für Physik, Universität Kassel, Heinrich-Plett-Str. 40, 34132, Kassel, Germany.
| |
Collapse
|
10
|
Stadlhofer R, Moritz M, Fuh MM, Heeren J, Zech H, Clauditz TS, Schlüter H, Betz CS, Eggert D, Böttcher A, Hahn J. Lipidome Analysis of Oropharyngeal Tumor Tissues Using Nanosecond Infrared Laser (NIRL) Tissue Sampling and Subsequent Mass Spectrometry. Int J Mol Sci 2023; 24:ijms24097820. [PMID: 37175533 PMCID: PMC10178251 DOI: 10.3390/ijms24097820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/16/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Ultrashort pulse infrared lasers can simultaneously sample and homogenize biological tissue using desorption by impulsive vibrational excitation (DIVE). With growing attention on alterations in lipid metabolism in malignant disease, mass spectrometry (MS)-based lipidomic analysis has become an emerging topic in cancer research. In this pilot study, we investigated the feasibility of tissue sampling with a nanosecond infrared laser (NIRL) for the subsequent lipidomic analysis of oropharyngeal tissues, and its potential to discriminate oropharyngeal squamous cell carcinoma (OPSCC) from non-tumorous oropharyngeal tissue. Eleven fresh frozen oropharyngeal tissue samples were ablated. The produced aerosols were collected by a glass fiber filter, and the lipidomes were analyzed with mass spectrometry. Data was evaluated by principal component analysis and Welch's t-tests. Lipid profiles comprised 13 lipid classes and up to 755 lipid species. We found significant inter- and intrapatient alterations in lipid profiles for tumor and non-tumor samples (p-value < 0.05, two-fold difference). Thus, NIRL tissue sampling with consecutive MS lipidomic analysis is a feasible and promising approach for the differentiation of OPSCC and non-tumorous oropharyngeal tissue and may provide new insights into lipid composition alterations in OPSCC.
Collapse
Affiliation(s)
- Rupert Stadlhofer
- Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Manuela Moritz
- Section/Core Facility Mass Spectrometric Proteomics, Diagnostic Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Marceline M Fuh
- Department of Biochemistry and Molecular Cell Biology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Jörg Heeren
- Department of Biochemistry and Molecular Cell Biology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Henrike Zech
- Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Till S Clauditz
- Department of Pathology, Diagnostic Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Hartmut Schlüter
- Section/Core Facility Mass Spectrometric Proteomics, Diagnostic Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Christian S Betz
- Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Dennis Eggert
- Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Arne Böttcher
- Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Jan Hahn
- Section/Core Facility Mass Spectrometric Proteomics, Diagnostic Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| |
Collapse
|
11
|
Mason SE, Manoli E, Alexander JL, Poynter L, Ford L, Paizs P, Adebesin A, McKenzie JS, Rosini F, Goldin R, Darzi A, Takats Z, Kinross JM. Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry. Ann Surg 2023; 277:e569-e577. [PMID: 34387206 DOI: 10.1097/sla.0000000000005164] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Rapid evaporative ionization mass spectrometry (REIMS) is a metabolomic technique analyzing tissue metabolites, which can be applied intraoperatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification. SUMMARY BACKGROUND DATA Metabolic dysregulation is a hallmark feature of carcinogenesis; however, it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease. METHODS Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600 to 1000 m/z were used for univariate and multivariate functions including linear discriminant analysis. RESULTS A total of 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96.0% accuracy and 91.8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93.5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and mono-unsaturated fatty acid-containing phospholipids. CONCLUSIONS The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to riskstratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.
Collapse
Affiliation(s)
- Sam E Mason
- Department of Surgery and Cancer, Imperial College, London
| | | | - James L Alexander
- Department of Metabolism, Digestion and Reproduction, Imperial College, London; and
| | - Liam Poynter
- Department of Surgery and Cancer, Imperial College, London
| | - Lauren Ford
- Department of Surgery and Cancer, Imperial College, London
| | - Petra Paizs
- Department of Metabolism, Digestion and Reproduction, Imperial College, London; and
| | - Afeez Adebesin
- Department of Surgery and Cancer, Imperial College, London
| | - James S McKenzie
- Department of Metabolism, Digestion and Reproduction, Imperial College, London; and
| | | | - Rob Goldin
- Department of Metabolism, Digestion and Reproduction, Imperial College, London; and
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College, London
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College, London; and
| | | |
Collapse
|
12
|
De Graeve M, Birse N, Hong Y, Elliott CT, Hemeryck LY, Vanhaecke L. Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation. Food Chem 2023; 404:134632. [DOI: 10.1016/j.foodchem.2022.134632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/20/2022] [Accepted: 10/13/2022] [Indexed: 11/22/2022]
|
13
|
Hristova J, Svinarov D. Enhancing precision medicine through clinical mass spectrometry platform. BIOTECHNOL BIOTEC EQ 2022. [DOI: 10.1080/13102818.2022.2053342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Julieta Hristova
- Alexander University Hospital, Faculty of Medicine, Medical University of Sofia, Sofia, Bulgaria
| | - Dobrin Svinarov
- Alexander University Hospital, Faculty of Medicine, Medical University of Sofia, Sofia, Bulgaria
| |
Collapse
|
14
|
Kanatas A, Walshaw EG, Wu J, Fabbroni G, Chengot P. Prognostic factors in oral cancer surgery - results from a UK tertiary centre. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2022; 49:755-759. [PMID: 36509628 DOI: 10.1016/j.ejso.2022.11.595] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Oral cancer surgery is complicated by the diverse nature of clinical and histopathological presentations that occur. Current National guidance recognises the significant role that surgical margin status plays in the overall survival of patients. Many other histopathological factors influence patient survival, the importance of which varies between the literature. MATERIALS AND METHODS In this prospective longitudinal study, all patients diagnosed with squamous cell carcinoma who had primary surgical treatment under general anaesthesia were included. Surgery was performed by one surgical team within this tertiary referral centre. Patients were followed up for a maximum of 7 years following their surgery. RESULTS A total of 250 patients were included from 2015 to 2022. Patients were 61.44 years old (SD 13.23) at diagnosis, and 56.4% were male (n = 141). Pathology was mainly pT1 (39.1%) and the most common sites were the border of tongue (31.2%) and floor of mouth (18.8%). 43.4% of patients had clear surgical margins, with overall survival being significantly associated with margin status (p = 0.0079). Extra-capsular spread was significantly associated with higher risk of death from metastatic head and neck cancer (p = 0.014), whereas presence of high-grade dysplasia at surgical margins and depth of invasion of tumour were not. CONCLUSION This study has reinforced the importance of surgical margin clearance and as such the development of intra-operative techniques to ensure this is imperative. The significance of extra-capsular spread in survival has also been demonstrated. Discussion regarding the current deficiency in accurate pre-operative diagnostic methods for extra capsular spread is covered.
Collapse
Affiliation(s)
- Anastasios Kanatas
- Leeds Teaching Hospitals and St James Institute of Oncology, Leeds Dental Institute and Leeds General Infirmary, Leeds, UK.
| | - Emma G Walshaw
- University of Leeds. Worsley Building, University of Leeds, Woodhouse, Leeds, LS2 9JT, UK.
| | - Jianhua Wu
- University of Leeds, School of Dentistry and Leeds Institute for Data Analytics, UK.
| | - Gillon Fabbroni
- Leeds Teaching Hospitals and St James Institute of Oncology, Leeds Dental Institute and Leeds General Infirmary, Leeds, UK.
| | - Preetha Chengot
- Leeds Teaching Hospitals and St James Institute of Oncology, Leeds Dental Institute and Leeds General Infirmary, Leeds, UK.
| |
Collapse
|
15
|
Wang J, Pursell ME, DeVor A, Awoyemi O, Valentine SJ, Li P. Portable mass spectrometry system: instrumentation, applications, and path to 'omics analysis. Proteomics 2022; 22:e2200112. [PMID: 36349734 PMCID: PMC10278091 DOI: 10.1002/pmic.202200112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/11/2022]
Abstract
Mass spectrometry (MS) is an information rich analytical technique and plays a key role in various 'omics studies. Standard mass spectrometers are bulky and operate at high vacuum, which hinder their adoption by the broader community and utility in field applications. Developing portable mass spectrometers can significantly expand the application scope and user groups of MS analysis. This review discusses the basics and recent advancements in the development of key components of portable mass spectrometers including ionization source, mass analyzer, detector, and vacuum system. Further, major areas where portable mass spectrometers are applied are also discussed. Finally, a perspective on the further development of portable mass spectrometers including the potential benefits for 'omics analysis is provided.
Collapse
Affiliation(s)
- Jing Wang
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| | - Madison E. Pursell
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| | - Amanda DeVor
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| | - Olanrewaju Awoyemi
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| | - Stephen J. Valentine
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| | - Peng Li
- C. Eugene Bennett Department of Chemistry, West Virginia University, Morgantown, WV, USA
| |
Collapse
|
16
|
Bormotov DS, Shamraeva MA, Kuzin AA, Shamarina EV, Eliferov VA, Silkin SV, Zhdanova EV, Pekov SI, Popov IA. Ambient ms profiling of meningiomas: intraoperative oncometabolite-based monitoring. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2022. [DOI: 10.24075/brsmu.2022.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The primary method of initial treatment of meningiomas is radical neurosurgical intervention. Various methods of intraoperative diagnostics currently in development aim to improve resection efficiency; we focus on methods based on molecular profiling using ambient ionization mass spectrometry. Such methods have been proven effective on various tumors, but the specifics of the molecular structure and the mechanical properties of meningiomas raise the question of applicability of protocols developed for other conditions for this particular task. The study aimed to compare the potential clinical use of three methods of ambient ionization in meningioma sample analysis: spray from tissue, inline cartridge extraction, and touch spherical sampler probe spray. To this end, lipid and metabolic profiles of meningioma tissues removed in the course of planned neurosurgical intervention have been analyzed. It is shown that in clinical practice, the lipid components of the molecular profile are best analyzed using the inline cartridge extraction method, distinguished by its ease of implementation and highest informational value. Analysis of oncometabolites with low molecular mass is optimally performed with the touch spherical sampler probe spray method, which scores high in both sensitivity and mass-spectrometric complex productivity.
Collapse
Affiliation(s)
- DS Bormotov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - MA Shamraeva
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - AA Kuzin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - EV Shamarina
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - VA Eliferov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - SV Silkin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - EV Zhdanova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - SI Pekov
- Skoltech, Moscow, Russia; Siberian State Medical University, Tomsk, Russia
| | - IA Popov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| |
Collapse
|
17
|
Marcus D, Phelps DL, Savage A, Balog J, Kudo H, Dina R, Bodai Z, Rosini F, Ip J, Amgheib A, Abda J, Manoli E, McKenzie J, Yazbek J, Takats Z, Ghaem-Maghami S. Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)-A Prospective Pilot Study of Diagnostic Accuracy. Cancers (Basel) 2022; 14:5892. [PMID: 36497372 PMCID: PMC9736036 DOI: 10.3390/cancers14235892] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 11/26/2022] [Indexed: 12/03/2022] Open
Abstract
Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.
Collapse
Affiliation(s)
- Diana Marcus
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - David L. Phelps
- Department of Gynaecological Oncology, University Hospital Southampton, Coxford Road, Southampton SO16 5YA, UK
| | - Adele Savage
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Julia Balog
- Department of Metabolism, Digestion and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Hiromi Kudo
- Centre for Pathology, Imperial College London, 4th Floor Clarence Wing, St Mary’s Hospital, London W2 1NY, UK
| | - Roberto Dina
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Zsolt Bodai
- Department of Metabolism, Digestion and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Francesca Rosini
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Jacey Ip
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Ala Amgheib
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Julia Abda
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Eftychios Manoli
- Department of Metabolism, Digestion and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - James McKenzie
- Department of Metabolism, Digestion and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Joseph Yazbek
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Sadaf Ghaem-Maghami
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| |
Collapse
|
18
|
Kaufmann M, Vaysse PM, Savage A, Amgheib A, Marton A, Manoli E, Fichtinger G, Pringle SD, Rudan JF, Heeren RMA, Takáts Z, Balog J, Porta Siegel T. Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats. Metabolites 2022; 12:1130. [PMID: 36422272 PMCID: PMC9699633 DOI: 10.3390/metabo12111130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
Abstract
Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.
Collapse
Affiliation(s)
- Martin Kaufmann
- Department of Surgery, Queen’s University, Kingston, ON K7L 2V7, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Surgery, Maastricht University Medical Center + (MUMC+), 6229 HX Maastricht, The Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, MUMC+, 6229 HX Maastricht, The Netherlands
| | - Adele Savage
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
| | - Ala Amgheib
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
| | | | - Eftychios Manoli
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
| | - Gabor Fichtinger
- School of Computing, Queen’s University, Kingston, ON K7L 2N8, Canada
| | | | - John F. Rudan
- Department of Surgery, Queen’s University, Kingston, ON K7L 2V7, Canada
| | - Ron M. A. Heeren
- Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Zoltán Takáts
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
| | - Júlia Balog
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
- Waters Research Center, 1031 Budapest, Hungary
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| |
Collapse
|
19
|
Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology. Int J Mol Sci 2022; 23:ijms231810562. [PMID: 36142485 PMCID: PMC9502565 DOI: 10.3390/ijms231810562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/02/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
The present work proposes the use of a fast analytical platform for the mass spectrometric (MS) profiling of canine mammary tissues in their native form for the building of a predictive statistical model. The latter could be used as a novel diagnostic tool for the real-time identification of different cellular alterations in order to improve tissue resection during veterinary surgery, as previously validated in human oncology. Specifically, Rapid Evaporative Ionization Mass Spectrometry (REIMS) coupled with surgical electrocautery (intelligent knife—iKnife) was used to collect MS data from histologically processed mammary samples, classified into healthy, hyperplastic/dysplastic, mastitis and tumors. Differences in the lipid composition enabled tissue discrimination with an accuracy greater than 90%. The recognition capability of REIMS was tested on unknown mammary samples, and all of them were correctly identified with a correctness score of 98–100%. Triglyceride identification was increased in healthy mammary tissues, while the abundance of phospholipids was observed in altered tissues, reflecting morpho-functional changes in cell membranes, and oxidized species were also tentatively identified as discriminant features. The obtained lipidomic profiles represented unique fingerprints of the samples, suggesting that the iKnife technique is capable of differentiating mammary tissues following chemical changes in cellular metabolism.
Collapse
|
20
|
Yau A, Fear MW, Gray N, Ryan M, Holmes E, Nicholson JK, Whiley L, Wood FM. Enhancing the accuracy of surgical wound excision following burns trauma via application of Rapid Evaporative IonisationMass Spectrometry (REIMS). Burns 2022; 48:1574-1583. [PMID: 36116996 DOI: 10.1016/j.burns.2022.08.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 08/13/2022] [Accepted: 08/30/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Surgical wound excision is a necessary procedure for burn patients that require the removal of eschar. The extent of excision is currently guided by clinical judgement, with excessinto healthy tissue potentially leading to excessive scar, or inadequate debridement increasing risk of infection. Thus, an objective real-time measure to facilitate accurate excision could support clinical judgement and improve this surgical procedure. This study was designed to investigate the potential use of Rapid evaporative ionisation mass spectrometry (REIMS) as a tool to support data-driven objective tissue excision. METHODS Data were acquired using a multi-platform approach that consisted of both Rapid Evaporative Ionisation Mass Spectrometry (REIMS) performed on intact skin, and comprehensive liquid chromatography-mass spectrometry (LC-MS/MS) lipidomics performed on homogenised skin tissue extracts. Data were analysed using principal components analysis (PCA) and multivariate orthogonal projections to latent squares discriminant analysis (OPLS-DA) and logistic regression to determine the predictability of the models. RESULTS PCA and OPLS-DA models of the REIMS and LC-MS/MS lipidomics data reported separation of excised and healthy tissue. Molecular fingerprints generated from REIMS analysis of healthy skin tissue revealed a high degree of heterogeneity, however, intra-individual variance was smaller than inter-individual variance. Both platforms indicated high levels of skin classification accuracy. In addition, OPLS-DA of the LC-MS/MS lipidomic data revealed significant differences in specific lipid classes between healthy control and excised skin samples; including lower free fatty acids (FFA), monoacylglycerols (MAG), lysophosphatidylglycerol (LPG) and lysophosphatidylethanolamines (LPE) in excised tissue and higher lactosylceramides (LCER) and cholesterol esters (CE) compared to healthy control tissue. CONCLUSIONS Having established the heterogeneity in the biochemical composition of healthy skin using REIMS and LC-MS/MS, our data show that REIMS has the potential to distinguish between excied and healthy skin tissue samples. This pilot study suggests that REIMS may be an effective tool to support accurate tissue excision during burn surgery.
Collapse
Affiliation(s)
- Andrew Yau
- Burn Injury Research Unit, School of Biomedical sciences, University of Western Australia, Perth, WA, Australia
| | - Mark W Fear
- Burn Injury Research Unit, School of Biomedical sciences, University of Western Australia, Perth, WA, Australia; Fiona Wood Foundation, Perth, WA, Australia
| | - Nicola Gray
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia
| | - Monique Ryan
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia
| | - Elaine Holmes
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK
| | - Luke Whiley
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia; Perron Institute for Neurological and Translational Science, Nedlands, WA 6009, Australia.
| | - Fiona M Wood
- Burn Injury Research Unit, School of Biomedical sciences, University of Western Australia, Perth, WA, Australia; Burns Service WA, WA Department of Health, Perth, WA, Australia; Fiona Wood Foundation, Perth, WA, Australia.
| |
Collapse
|
21
|
Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology. JOURNAL OF MOLECULAR PATHOLOGY 2022. [DOI: 10.3390/jmp3030014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
Collapse
|
22
|
Morgan J, Salcedo-Sora JE, Wagner I, Beynon RJ, Triana-Chavez O, Strode C. Rapid Evaporative Ionization Mass Spectrometry (REIMS): a Potential and Rapid Tool for the Identification of Insecticide Resistance in Mosquito Larvae. JOURNAL OF INSECT SCIENCE (ONLINE) 2022; 22:5. [PMID: 36082679 PMCID: PMC9459442 DOI: 10.1093/jisesa/ieac052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Indexed: 06/15/2023]
Abstract
Insecticide resistance is a significant challenge facing the successful control of mosquito vectors globally. Bioassays are currently the only method for phenotyping resistance. They require large numbers of mosquitoes for testing, the availability of a susceptible comparator strain, and often insectary facilities. This study aimed to trial the novel use of rapid evaporative ionization mass spectrometry (REIMS) for the identification of insecticide resistance in mosquitoes. No sample preparation is required for REIMS and analysis can be rapidly conducted within hours. Temephos resistant Aedes aegypti (Linnaeus) larvae from Cúcuta, Colombia and temephos susceptible larvae from two origins (Bello, Colombia, and the lab reference strain New Orleans) were analyzed using REIMS. We tested the ability of REIMS to differentiate three relevant variants: population source, lab versus field origin, and response to insecticide. The classification of these data was undertaken using linear discriminant analysis (LDA) and random forest. Classification models built using REIMS data were able to differentiate between Ae. aegypti larvae from different populations with 82% (±0.01) accuracy, between mosquitoes of field and lab origin with 89% (±0.01) accuracy and between susceptible and resistant larvae with 85% (±0.01) accuracy. LDA classifiers had higher efficiency than random forest with this data set. The high accuracy observed here identifies REIMS as a potential new tool for rapid identification of resistance in mosquitoes. We argue that REIMS and similar modern phenotyping alternatives should complement existing insecticide resistance management tools.
Collapse
Affiliation(s)
- Jasmine Morgan
- Department of Biology, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK
| | | | - Iris Wagner
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Robert J Beynon
- Centre for Proteome Research, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Omar Triana-Chavez
- Instituto de Biología, Facultad de Ciencias Exactas y Naturales (FCEN), University of Antioquia, Medellín, Colombia
| | | |
Collapse
|
23
|
Kontsek E, Pesti A, Slezsák J, Gordon P, Tornóczki T, Smuk G, Gergely S, Kiss A. Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes. Pathol Oncol Res 2022; 28:1610439. [PMID: 36061143 PMCID: PMC9428038 DOI: 10.3389/pore.2022.1610439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022]
Abstract
Introduction: Lung cancer is the most common malignancy worldwide. Squamous cell carcinoma (SQ) and adenocarcinoma (LUAD) are the two most frequent histological subtypes. Small cell carcinoma (SCLC) subtype has the worst prognosis. Differential diagnosis is essential for proper oncological treatment. Life science associated mid- and near-infrared based microscopic techniques have been developed exponentially, especially in the past decade. Vibrational spectroscopy is a potential non-destructive approach to investigate malignancies. Aims: Our goal was to differentiate lung cancer subtypes by their label-free mid-infrared spectra using supervised multivariate analyses. Material and Methods: Formalin-fixed paraffin-embedded (FFPE) samples were selected from the archives. Three subtypes were selected for each group: 10-10 cases SQ, LUAD and SCLC. 2 μm thick sections were cut and laid on aluminium coated glass slides. Transflection optical setup was applied on Perkin-Elmer infrared microscope. 250 × 600 μm areas were imaged and the so-called mid-infrared fingerprint region (1800-648cm−1) was further analysed with linear discriminant analysis (LDA) and support vector machine (SVM) methods. Results: Both “patient-based” and “pixel-based” approaches were examined. Patient-based analysis by using 3 LDA models and 2 SVM models resulted in different separations. The higher the cut-off value the lower is the accuracy. The linear C-support vector classification (C-SVC) SVM resulted in the best (100%) accuracy for the three subtypes using a 50% cut-off value. The pixel-based analysis gave, similarly, the linear C-SVC SVM model to be the most efficient in the statistical indicators (SQ sensitivity 81.65%, LUAD sensitivity 82.89% and SCLC sensitivity 88.89%). The spectra cut-off, the kernel function and the algorithm function influence the accuracy. Conclusion: Mid-Infrared imaging could be used to differentiate FFPE lung cancer subtypes. Supervised multivariate tools are promising to accurately separate lung tumor subtypes. The long-term perspective is to develop a spectroscopy-based diagnostic tool, revolutionizing medical differential diagnostics, especially cancer identification.
Collapse
Affiliation(s)
- E. Kontsek
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
- *Correspondence: E. Kontsek, ; A. Kiss,
| | - A. Pesti
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
| | - J. Slezsák
- Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary
| | - P. Gordon
- Department of Electronics Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - T. Tornóczki
- Department of Pathology, Medical School and Clinical Center, University of Pécs, Pécs, Hungary
| | - G. Smuk
- Department of Pathology, Medical School and Clinical Center, University of Pécs, Pécs, Hungary
| | - S. Gergely
- Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary
| | - A. Kiss
- 2nd Department of Pathology, Semmelweis University, Budapest, Hungary
- *Correspondence: E. Kontsek, ; A. Kiss,
| |
Collapse
|
24
|
Vaysse PM, Demers I, van den Hout MFCM, van de Worp W, Anthony IGM, Baijens LWJ, Tan BI, Lacko M, Vaassen LAA, van Mierlo A, Langen RCJ, Speel EJM, Heeren RMA, Porta Siegel T, Kremer B. Evaluation of the Sensitivity of Metabolic Profiling by Rapid Evaporative Ionization Mass Spectrometry: Toward More Radical Oral Cavity Cancer Resections. Anal Chem 2022; 94:6939-6947. [PMID: 35503862 PMCID: PMC9118195 DOI: 10.1021/acs.analchem.1c03583] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Radical resection
for patients with oral cavity cancer remains
challenging. Rapid evaporative ionization mass spectrometry (REIMS)
of electrosurgical vapors has been reported for real-time classification
of normal and tumor tissues for numerous surgical applications. However,
the infiltrative pattern of invasion of oral squamous cell carcinomas
(OSCC) challenges the ability of REIMS to detect low amounts of tumor
cells. We evaluate REIMS sensitivity to determine the minimal amount
of detected tumors cells during oral cavity cancer surgery. A total
of 11 OSCC patients were included in this study. The tissue classification
based on 185 REIMS ex vivo metabolic profiles from
five patients was compared to histopathology classification using
multivariate analysis and leave-one-patient-out cross-validation.
Vapors were analyzed in vivo by REIMS during four
glossectomies. Complementary desorption electrospray ionization–mass
spectrometry imaging (DESI-MSI) was employed to map tissue heterogeneity
on six oral cavity sections to support REIMS findings. REIMS sensitivity
was assessed with a new cell-based assay consisting of mixtures of
cell lines (tumor, myoblasts, keratinocytes). Our results depict REIMS
classified tumor and soft tissues with 96.8% accuracy. In
vivo REIMS generated intense mass spectrometric signals.
REIMS detected 10% of tumor cells mixed with 90% myoblasts with 83%
sensitivity and 82% specificity. DESI-MSI underlined distinct metabolic
profiles of nerve features and a metabolic shift phosphatidylethanolamine
PE(O-16:1/18:2))/cholesterol sulfate common to both mucosal maturation
and OSCC differentiation. In conclusion, the assessment of tissue
heterogeneity with DESI-MSI and REIMS sensitivity with cell mixtures
characterized sensitive metabolic profiles toward in vivo tissue recognition during oral cavity cancer surgeries.
Collapse
Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging Institute (M4i), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.,Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,Department of Surgery, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
| | - Imke Demers
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,Department of Pathology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Mari F C M van den Hout
- Department of Pathology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Wouter van de Worp
- Department of Respiratory Medicine, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Ian G M Anthony
- Maastricht MultiModal Molecular Imaging Institute (M4i), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Laura W J Baijens
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Bing I Tan
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Martin Lacko
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Lauretta A A Vaassen
- Department of Cranio-Maxillofacial Surgery, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Auke van Mierlo
- Department of Cranio-Maxillofacial Surgery, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Ramon C J Langen
- Department of Respiratory Medicine, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Ernst-Jan M Speel
- Department of Pathology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging Institute (M4i), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging Institute (M4i), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Bernd Kremer
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| |
Collapse
|
25
|
Phelps DL, Borley JV, Brown R, Takáts Z, Ghaem-Maghami S. The use of biomarkers to stratify surgical care in women with ovarian cancer: Scientific Impact Paper No. 69 March 2022: Scientific Impact Paper No. 69 May 2022. BJOG 2022; 129:e66-e74. [PMID: 35437905 DOI: 10.1111/1471-0528.17142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Biomarkers may offer unforeseen insights into clinical diagnosis, as well as the likely course and outcome of a condition. In this paper, the focus is on the use of biological molecules found in body fluids or tissues for diagnosis and prediction of outcome in ovarian cancer patients. In cancer care, biomarkers are being used to develop personalised treatment plans for patients based on the unique characteristics of their tumour. This tailoring of care can be used to pursue specific targets identified by biomarkers, or treat the patient according to specific tumour characteristics. Surgery is one of the core treatments for ovarian cancer, whether it is offered in primary surgery or following chemotherapy in delayed surgery. Biomarkers already exist to guide the treatment of tumours with chemotherapy, but very little research has determined the value of biomarkers in tailoring surgical care for ovarian cancer. Such research is required to identify new biomarkers and assess their effectiveness in a clinical setting as well as to help identify specific tumour types to guide surgery. Biomarkers could help to determine the success of removing the disease surgically, or help to identify tumour deposits that persist after chemotherapy. All of these aspects would improve current practice. This Scientific Impact Paper highlights research that may pave the way towards bespoke surgery according to the biological characteristics of a tumour and aid gynaecological oncologists to provide surgical treatment according to individual need, rather than a blanket approach for all.
Collapse
Affiliation(s)
- D L Phelps
- Royal College of Obstetricians and Gynaecologists, London, UK
| | - J V Borley
- Royal College of Obstetricians and Gynaecologists, London, UK
| | - R Brown
- Royal College of Obstetricians and Gynaecologists, London, UK
| | - Z Takáts
- Royal College of Obstetricians and Gynaecologists, London, UK
| | - S Ghaem-Maghami
- Royal College of Obstetricians and Gynaecologists, London, UK
| | | |
Collapse
|
26
|
Pekov SI, Zhvansky ES, Eliferov VA, Sorokin AA, Ivanov DG, Nikolaev EN, Popov IA. Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27082587. [PMID: 35458785 PMCID: PMC9029908 DOI: 10.3390/molecules27082587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022]
Abstract
Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.
Collapse
Affiliation(s)
- Stanislav I. Pekov
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- Siberian State Medical University, 634050 Tomsk, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| | - Evgeny S. Zhvansky
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Vasily A. Eliferov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Anatoly A. Sorokin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- Department of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK
| | - Daniil G. Ivanov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
| | - Eugene N. Nikolaev
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| | - Igor A. Popov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (E.S.Z.); (V.A.E.); (A.A.S.); (D.G.I.)
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov, 117997 Moscow, Russia
- Correspondence: (S.I.P.); (E.N.N); (I.A.P.)
| |
Collapse
|
27
|
Kelis Cardoso VG, Sabin GP, Hantao LW. Rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics for real-time beer analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:1540-1546. [PMID: 35302124 DOI: 10.1039/d2ay00063f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The beer industry plays an important role in the economy since this is the third most consumed beverage worldwide. Efficient analytical methods must be developed to ensure the quality of the product. Rapid evaporative ionization mass spectrometry (REIMS) can provide molecular-level information, while enabling fast analysis. REIMS requires minimal sample preparation and it is ideal for the analysis of homogeneous liquid samples, such as beers, within only five seconds. In this article, 32 different beers were analyzed by REIMS in positive and negative ionization modes using a hybrid quadrupole time-of-flight mass spectrometer. The positive and negative MS spectrum blocks were augmented for data fusion. A predictive model by partial least squares discriminant analysis (PLS-DA) was used to discriminate the samples (i) by their brands and (ii) by the beer type (Premium and Standard American lagers). The results showed that REIMS provided a rich fingerprint of beers, which was successfully used to discriminate the brands and types with 96.9% and 97.9% accuracy, respectively. We believe that this proof-of-concept has great potential to be applied on a larger scale for industrial purposes due to its high-throughput.
Collapse
Affiliation(s)
| | - Guilherme Post Sabin
- Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, São Paulo, 13083-862, Brazil.
- OpenScience, Office 916, 233 Conceição Street, Campinas, São Paulo, 13010-050, Brazil
| | - Leandro Wang Hantao
- Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, São Paulo, 13083-862, Brazil.
| |
Collapse
|
28
|
Van Hese L, De Vleeschouwer S, Theys T, Larivière E, Solie L, Sciot R, Siegel TP, Rex S, Heeren RM, Cuypers E. Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery. J Mass Spectrom Adv Clin Lab 2022; 24:80-89. [PMID: 35572786 PMCID: PMC9095887 DOI: 10.1016/j.jmsacl.2022.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 11/17/2022] Open
Abstract
REIMS can differentiate glioblastoma from normal brain with 99.2% sensitivity. Starting from 5% glioblastoma, REIMS showed a 100% correct classification rate. Low-grade gliomas can be identified with a 97.5% sensitivity.
Introduction Objectives Methods Results Conclusion
Collapse
Affiliation(s)
- Laura Van Hese
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Anaesthesiology, UZ Leuven; Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Steven De Vleeschouwer
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Tom Theys
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Emma Larivière
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Lien Solie
- Department of Neurosurgery, Laboratory for Experimental Neurosurgery and Neuroanatomy, UZ Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Raf Sciot
- Department of Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Steffen Rex
- Department of Anaesthesiology, UZ Leuven; Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Ron M.A. Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Eva Cuypers
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
- Corresponding author at: M4I Institute, Division of Imaging Mass Spectrometry, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.
| |
Collapse
|
29
|
Fabregat-Safont D, Ibáñez M, Hernández F, Sancho JV. Development of a simple and low-cost prototype probe fully-compatible with atmospheric solids analysis probe for the analysis of human breath in real-time. Microchem J 2022. [DOI: 10.1016/j.microc.2021.107086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
30
|
Metabolomic Phenotyping of Gliomas: What Can We Get with Simplified Protocol for Intact Tissue Analysis? Cancers (Basel) 2022; 14:cancers14020312. [PMID: 35053475 PMCID: PMC8773998 DOI: 10.3390/cancers14020312] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 12/17/2022] Open
Abstract
Glioblastoma multiforme is one of the most malignant neoplasms among humans in their third and fourth decades of life, which is evidenced by short patient survival times and rapid tumor-cell proliferation after radiation and chemotherapy. At present, the diagnosis of gliomas and decisions related to therapeutic strategies are based on genetic testing and histological analysis of the tumor, with molecular biomarkers still being sought to complement the diagnostic panel. This work aims to enable the metabolomic characterization of cancer tissue and the discovery of potential biomarkers via high-resolution mass spectrometry coupled to liquid chromatography and a solvent-free sampling protocol that uses a microprobe to extract metabolites directly from intact tumors. The metabolomic analyses were performed independently from genetic and histological testing and at a later time. Despite the small cohort analyzed in this study, the results indicated that the proposed method is able to identify metabolites associated with different malignancy grades of glioma, as well as IDH and 1p19q codeletion mutations. A comparison of the constellation of identified metabolites and the results of standard tests indicated the validity of using the characterization of one comprehensive tumor phenotype as a reflection of all diagnostically meaningful information. Due to its simplicity, the proposed analytical approach was verified as being compatible with a surgical environment and applicable for large-scale studies.
Collapse
|
31
|
Santilli AML, Ren K, Oleschuk R, Kaufmann M, Rudan J, Fichtinger G, Mousavi P. Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review. IEEE Trans Biomed Eng 2022; 69:2220-2232. [PMID: 34982670 DOI: 10.1109/tbme.2021.3139992] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines. METHODS We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. RESULTS Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. CONCLUSION Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. SIGNIFICANCE By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.
Collapse
|
32
|
Yaari Z, Horoszko CP, Antman-Passig M, Kim M, Nguyen FT, Heller DA. Emerging technologies in cancer detection. Cancer Biomark 2022. [DOI: 10.1016/b978-0-12-824302-2.00011-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
33
|
Murphy F, Gathercole J, Lee E, Homewood I, Ross AB, Clerens S, Maes E. Discrimination of milk fermented with different starter cultures by MALDI-TOF MS and REIMS fingerprinting. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
34
|
Zhang J, Sans M, Garza KY, Eberlin LS. MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE. MASS SPECTROMETRY REVIEWS 2021; 40:692-720. [PMID: 33094861 DOI: 10.1002/mas.21664] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 09/09/2020] [Accepted: 09/09/2020] [Indexed: 06/11/2023]
Abstract
Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.
Collapse
Affiliation(s)
- Jialing Zhang
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Marta Sans
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Kyana Y Garza
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Livia S Eberlin
- Department of Chemistry, University of Texas at Austin, Austin, TX
| |
Collapse
|
35
|
Brown HM, Alfaro CM, Pirro V, Dey M, Hattab EM, Cohen-Gadol AA, Cooks RG. Intraoperative Mass Spectrometry Platform for IDH Mutation Status Prediction, Glioma Diagnosis, and Estimation of Tumor Cell Infiltration. J Appl Lab Med 2021; 6:902-916. [PMID: 33523209 PMCID: PMC8266740 DOI: 10.1093/jalm/jfaa233] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/23/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Surgical tumor resection is the primary treatment option for diffuse glioma, the most common malignant brain cancer. The intraoperative diagnosis of gliomas from tumor core samples can be improved by use of molecular diagnostics. Further, residual tumor at surgical margins is a primary cause of tumor recurrence and malignant progression. This study evaluates a desorption electrospray ionization mass spectrometry (DESI-MS) system for intraoperative isocitrate dehydrogenase (IDH) mutation assessment, estimation of tumor cell infiltration as tumor cell percentage (TCP), and disease status. This information could be used to enhance the extent of safe resection and so potentially improve patient outcomes. METHODS A mobile DESI-MS instrument was modified and used in neurosurgical operating rooms (ORs) on a cohort of 49 human subjects undergoing craniotomy with tumor resection for suspected diffuse glioma. Small tissue biopsies (ntotal = 203) from the tumor core and surgical margins were analyzed by DESI-MS in the OR and classified using univariate and multivariate statistical methods. RESULTS Assessment of IDH mutation status using DESI-MS/MS to measure 2-hydroxyglutarate (2-HG) ion intensities from tumor cores yielded a sensitivity, specificity, and overall diagnostic accuracy of 89, 100, and 94%, respectively (ncore = 71). Assessment of TCP (categorized as low or high) in tumor margin and core biopsies using N-acetyl-aspartic acid (NAA) intensity provided a sensitivity, specificity, and accuracy of 91, 76, and 83%, respectively (ntotal = 203). TCP assessment using lipid profile deconvolution provided sensitivity, specificity, and accuracy of 76, 85, and 81%, respectively (ntotal = 203). Combining the experimental data and using PCA-LDA predictions of disease status, the sensitivity, specificity, and accuracy in predicting disease status are 63%, 83%, and 74%, respectively (ntotal = 203). CONCLUSIONS The DESI-MS system allowed for identification of IDH mutation status, glioma diagnosis, and estimation of tumor cell infiltration intraoperatively in a large human glioma cohort. This methodology should be further refined for clinical diagnostic applications.
Collapse
Affiliation(s)
| | - Clint M. Alfaro
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Valentina Pirro
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Mahua Dey
- Department of Neurological Surgery, Indiana University School of Medicine, Goodman Campbell Brain and Spine, Indianapolis, IN, USA
| | - Eyas M. Hattab
- Department of Pathology and Laboratory Medicine, University of Louisville, KY, USA
| | - Aaron A. Cohen-Gadol
- Department of Neurological Surgery, Indiana University School of Medicine, Goodman Campbell Brain and Spine, Indianapolis, IN, USA
| | - R. Graham Cooks
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| |
Collapse
|
36
|
Nauta SP, Poeze M, Heeren RMA, Porta Siegel T. Clinical use of mass spectrometry (imaging) for hard tissue analysis in abnormal fracture healing. Clin Chem Lab Med 2021; 58:897-913. [PMID: 32049645 DOI: 10.1515/cclm-2019-0857] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 12/16/2019] [Indexed: 12/23/2022]
Abstract
Common traumas to the skeletal system are bone fractures and injury-related articular cartilage damage. The healing process can be impaired resulting in non-unions in 5-10% of the bone fractures and in post-traumatic osteoarthritis (PTOA) in up to 75% of the cases of cartilage damage. Despite the amount of research performed in the areas of fracture healing and cartilage repair as well as non-unions and PTOA, still, the outcome of a bone fracture or articular cartilage damage cannot be predicted. Here, we discuss known risk factors and key molecules involved in the repair process, together with the main challenges associated with the prediction of outcome of these injuries. Furthermore, we review and discuss the opportunities for mass spectrometry (MS) - an analytical tool capable of detecting a wide variety of molecules in tissues - to contribute to extending molecular understanding of impaired healing and the discovery of predictive biomarkers. Therefore, the current knowledge and challenges concerning MS imaging of bone and cartilage tissue as well as in vivo MS are discussed. Finally, we explore the possibilities of in situ, real-time MS for the prediction of outcome during surgery of bone fractures and injury-related articular cartilage damage.
Collapse
Affiliation(s)
- Sylvia P Nauta
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, The Netherlands.,Department of Orthopedic Surgery and Traumasurgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Martijn Poeze
- Department of Surgery, Division of Traumasurgery, Maastricht University Medical Center, Maastricht, The Netherlands.,NUTRIM, School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ron M A Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229ER Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
37
|
Santilli AML, Jamzad A, Sedghi A, Kaufmann M, Logan K, Wallis J, Ren KYM, Janssen N, Merchant S, Engel J, McKay D, Varma S, Wang A, Fichtinger G, Rudan JF, Mousavi P. Domain adaptation and self-supervised learning for surgical margin detection. Int J Comput Assist Radiol Surg 2021; 16:861-869. [PMID: 33956307 DOI: 10.1007/s11548-021-02381-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/13/2021] [Indexed: 01/02/2023]
Abstract
PURPOSE One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets. METHODS We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer classification task on breast data. This domain adaptation allows for the transfer of learnt weights from models of one tissue type to another. RESULTS Our datasets contained 320 skin burns (129 tumor burns, 191 normal burns) from 51 patients and 144 breast tissue burns (41 tumor and 103 normal) from 11 patients. We investigate the effect of different hyper-parameters on the performance of the final classifier. The proposed two-step method performed statistically significantly better than a baseline model (p-value < 0.0001), by achieving an accuracy, sensitivity and specificity of 92%, 88% and 92%, respectively. CONCLUSION This is the first application of domain transfer for iKnife REIMS data. We showed that having a limited number of breast data samples for training a classifier can be compensated by self-supervised learning and domain adaption on a set of unlabeled skin data. We plan to confirm this performance by collecting new breast samples and extending it to incorporate other cancer tissues.
Collapse
Affiliation(s)
| | - Amoon Jamzad
- School of Computing, Queen's University, Ontario, Canada
| | - Alireza Sedghi
- School of Computing, Queen's University, Ontario, Canada
| | | | - Kathryn Logan
- Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada
| | - Julie Wallis
- Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada
| | - Kevin Y M Ren
- Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada
| | | | | | - Jay Engel
- Department of Surgery, Queen's University, Ontario, Canada
| | - Doug McKay
- Department of Surgery, Queen's University, Ontario, Canada
| | - Sonal Varma
- Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada
| | - Ami Wang
- Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada
| | | | - John F Rudan
- Department of Surgery, Queen's University, Ontario, Canada
| | - Parvin Mousavi
- School of Computing, Queen's University, Ontario, Canada
| |
Collapse
|
38
|
Ogrinc N, Saudemont P, Takats Z, Salzet M, Fournier I. Cancer Surgery 2.0: Guidance by Real-Time Molecular Technologies. Trends Mol Med 2021; 27:602-615. [PMID: 33965341 DOI: 10.1016/j.molmed.2021.04.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/30/2021] [Accepted: 04/02/2021] [Indexed: 12/14/2022]
Abstract
In vivo cancer margin delineation during surgery remains a major challenge. Despite the availability of several image guidance techniques and intraoperative assessment, clear surgical margins and debulking efficiency remain scarce. For this reason, there is particular interest in developing rapid intraoperative tools with high sensitivity and specificity to help guide cancer surgery in vivo. Recently, several emerging technologies including intraoperative mass spectrometry have paved the way for molecular guidance in a clinical setting. We evaluate these techniques and assess their relevance for intraoperative surgical guidance and how they can transform the future of molecular cancer surgery, diagnostics, patient management and care.
Collapse
Affiliation(s)
- Nina Ogrinc
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Philippe Saudemont
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Zoltan Takats
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France
| | - Michel Salzet
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| | - Isabelle Fournier
- University of Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000 Lille, France; Institut Universitaire de France (IUF), Paris, France.
| |
Collapse
|
39
|
Abstract
Over the past decade, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry has revolutionized the practice of clinical microbiology and infectious disease diagnostics. Rapid advancement has occurred through the development and implementation of mass spectrometric protein profiling technologies that are widely available. Ease of sample preparation, rapid turnaround times, and high throughput accuracy have accelerated acceptance within the clinical laboratory. New mass spectrometric technologies centered on multiple microbial diagnostic markers are in development. Such new applications, reviewed in this article and on the near horizon, stand to greatly enhance the capabilities and utility for improved mass spectrometric microbial identification and patient care.
Collapse
|
40
|
McCullough BJ, Hopley CJ. Results of the first and second British Mass Spectrometry Society interlaboratory studies on ambient mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2021; 35 Suppl 2:e8534. [PMID: 31334890 DOI: 10.1002/rcm.8534] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/16/2019] [Indexed: 06/10/2023]
Abstract
RATIONALE As the popularity of ambient ionisation grows, so too does the importance of understanding its capabilities and limitations. The British Mass Spectrometry Society Special Interest Group on Ambient Ionisation has carried out two studies into the use of ambient ionisation, the results of which are presented here. METHODS The first study (study 1) examined the detection and quantitation capabilities of ambient ionisation while the second examined repeatability and robustness. For study 1 participants were sent a range of samples including two calibration sample sets and asked to analyse them. For study 2, two samples containing the same eight-component mixture were provided (one in solvent, one in matrix); participants were asked to analyse these samples multiple times, over multiple days to allow assessment of repeatability. RESULTS Study 1 showed that small, polar compounds were well detected by the participants while lower polarity compounds were less well detected. For many samples the introduction method appeared to be a significant factor in the observed spectra. The quantitation study gave good results but revealed significant variability. For study 2 the mean repeatabilities were 65% in solvent and 88% in matrix. The inclusion of an internal standard was shown to greatly improve repeatability. CONCLUSIONS Ambient ionisation is capable of ionising a wide range of compounds with good precision and excellent repeatability; however, in order to obtain such data care must be taken with the experimental design. The data can be significantly improved with a well-chosen internal standard.
Collapse
Affiliation(s)
- Bryan J McCullough
- National Measurement Laboratory, LGC, Queens Road, Teddington, TW11 0LY, UK
| | | |
Collapse
|
41
|
Anttalainen A, Mäkelä M, Kumpulainen P, Vehkaoja A, Anttalainen O, Oksala N, Roine A. Predicting lecithin concentration from differential mobility spectrometry measurements with linear regression models and neural networks. Talanta 2021; 225:121926. [PMID: 33592698 DOI: 10.1016/j.talanta.2020.121926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 02/06/2023]
Abstract
Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS.
Collapse
Affiliation(s)
| | | | - Pekka Kumpulainen
- Olfactomics Ltd, Tampere, Finland; Tampere University Hospital, Tampere, Finland
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Niku Oksala
- Olfactomics Ltd, Tampere, Finland; Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Vascular Centre, Tampere University Hospital, Tampere, Finland
| | | |
Collapse
|
42
|
Zhvansky ES, Eliferov VA, Sorokin AA, Shurkhay VA, Pekov SI, Bormotov DS, Ivanov DG, Zavorotnyuk DS, Bocharov KV, Khaliullin IG, Belenikin MS, Potapov AA, Nikolaev EN, Popov IA. Assessment of variation of inline cartridge extraction mass spectra. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4640. [PMID: 32798239 DOI: 10.1002/jms.4640] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/20/2020] [Accepted: 07/31/2020] [Indexed: 06/11/2023]
Abstract
Recently, mass-spectrometry methods show its utility in tumor boundary location. The effect of differences between research and clinical protocols such as low- and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful.
Collapse
Affiliation(s)
- Evgeny S Zhvansky
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Vasiliy A Eliferov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Anatoly A Sorokin
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Vsevolod A Shurkhay
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
- Outpatient department, Federal State Autonomous Institution «N.N. Burdenko National Scientific and Practical Center for Neurosurgery» of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Stanislav I Pekov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Denis S Bormotov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Daniil G Ivanov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
- Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, Moscow, Russian Federation
| | - Denis S Zavorotnyuk
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Konstantin V Bocharov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Center of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
| | - Iliyas G Khaliullin
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Maksim S Belenikin
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| | - Aleksandr A Potapov
- Outpatient department, Federal State Autonomous Institution «N.N. Burdenko National Scientific and Practical Center for Neurosurgery» of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Evgeny N Nikolaev
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Igor A Popov
- Phystech School of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
| |
Collapse
|
43
|
Manoli E, Mason S, Ford L, Adebesin A, Bodai Z, Darzi A, Kinross J, Takats Z. Validation of Ultrasonic Harmonic Scalpel for Real-Time Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry. Anal Chem 2021; 93:5906-5916. [PMID: 33787247 PMCID: PMC8153397 DOI: 10.1021/acs.analchem.1c00270] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
In this study, we integrate rapid
evaporative ionization mass spectrometry
(REIMS) with the Harmonic scalpel, an advanced laparoscopic surgical
instrument that utilizes ultrasound energy to dissect and coagulate
tissues. It provides unparalleled manipulation capability to surgeons
and has superseded traditional electrosurgical tools particularly
in abdominal surgery, but is yet to be validated with REIMS. The REIMS
platform coupled with the Harmonic device was shown to produce tissue-specific
lipid profiles through the analysis of porcine tissues in both negative
and positive ionization modes. Comparison with other methods of electrosurgical
dissection, such as monopolar electrosurgery and CO2 laser,
showed spectral differences in the profile dependent on the energy
device used. The Harmonic device demonstrated major spectral differences
in the phospholipid region of m/z 600–1000 compared with the monopolar electrosurgical and
CO2 laser-generated spectra. Within the Harmonic REIMS
spectra, high intensities of diglycerides and triglycerides were observed.
In contrast, monopolar electrosurgical and laser spectra demonstrated
high abundances of glycerophospholipids. The Harmonic scalpel was
able to differentiate between the liver, muscle, colon, and small
intestine, demonstrating 100% diagnostic accuracy. The validation
of the Harmonic device–mass spectrometry combination will allow
the platform to be used safely and robustly for real-time in vivo surgical tissue identification in a variety of clinical
applications.
Collapse
Affiliation(s)
- Eftychios Manoli
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - Sam Mason
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - Lauren Ford
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - Afeez Adebesin
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - Zsolt Bodai
- Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - James Kinross
- Department of Surgery and Cancer, Imperial College London, St Marys Hospital, London W2 1NY, United Kingdom
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| |
Collapse
|
44
|
A quantitative LC-MS/MS approach for monitoring 2'-fluoro-2'-deoxy-D-glucose uptake in tumor tissue. Bioanalysis 2021; 13:481-491. [PMID: 33724050 DOI: 10.4155/bio-2020-0326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose: Develop a quantitative LC-MS/MS method for FDG, FDG-monophosphate, glucose and glucose-monophosphate in mouse tumor models to assist in validating the use of [18F]FDG-positron emission tomography (PET) imaging for anticancer therapies in a clinical setting. Methodology/results: Analytes were isolated from tumors by protein precipitation and detected on a Sciex API-5500 mass spectrometer. Improved assay robustness and selectivity were achieved through chromatographic separation of FDG-monophosphate from glucose-monophosphate, selection of a unique ion transition and incorporation of stable isotope labeled internal standards. In a mouse JIMT-1 tumor model, FDG-monophosphate levels measured by LC-MS/MS correlated with [18F]FDG-PET imaging results. Conclusion: LC-MS/MS analysis of FDG-monophosphate accumulation in tumors is a cost-effective tool to gauge the translational potential of [18F]FDG-PET imaging as a noninvasive biomarker in clinical studies.
Collapse
|
45
|
Mangraviti D, Rigano F, Arigò A, Dugo P, Mondello L. Differentiation of Italian extra virgin olive oils by rapid evaporative ionization mass spectrometry. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110715] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
46
|
Vaysse PM, Grabsch HI, van den Hout MFCM, Bemelmans MHA, Heeren RMA, Olde Damink SWM, Porta Siegel T. Real-time lipid patterns to classify viable and necrotic liver tumors. J Transl Med 2021; 101:381-395. [PMID: 33483597 DOI: 10.1038/s41374-020-00526-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 11/30/2020] [Accepted: 12/02/2020] [Indexed: 12/14/2022] Open
Abstract
Real-time tissue classifiers based on molecular patterns are emerging tools for fast tumor diagnosis. Here, we used rapid evaporative ionization mass spectrometry (REIMS) and multivariate statistical analysis (principal component analysis-linear discriminant analysis) to classify tissues with subsequent comparison to gold standard histopathology. We explored whether REIMS lipid patterns can identify human liver tumors and improve the rapid characterization of their underlying metabolic features. REIMS-based classification of liver parenchyma (LP), hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC) reached an accuracy of 98.3%. Lipid patterns of LP were more similar to those of HCC than to those of MAC and allowed clear distinction between primary and metastatic liver tumors. HCC lipid patterns were more heterogeneous than those of MAC, which is consistent with the variation seen in the histopathological phenotype. A common ceramide pattern discriminated necrotic from viable tumor in MAC with 92.9% accuracy and in other human tumors. Targeted analysis of ceramide and related sphingolipid mass features in necrotic tissues may provide a new classification of tumor cell death based on metabolic shifts. Real-time lipid patterns may have a role in future clinical decision-making in cancer precision medicine.
Collapse
Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Otorhinolaryngology, Head & Neck Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Mari F C M van den Hout
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Marc H A Bemelmans
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands
| | - Steven W M Olde Damink
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany
- NUTRIM School of Nutrition and Translational Research in Metabolism Faculty of Health, University of Maastricht, Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging Institute (M4i), University of Maastricht, Maastricht, The Netherlands.
| |
Collapse
|
47
|
Zhvansky ES, Sorokin AA, Bormotov DS, Bocharov KV, Zavorotnyuk DS, Ivanov DG, Nikolaev EN, Popov IA. The software for interactive evaluation of mass spectra stability and reproducibility. Bioinformatics 2020; 37:140-142. [PMID: 33367588 DOI: 10.1093/bioinformatics/btaa1072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 11/25/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Mass spectrometry methods are widely used for the analysis of biological and medical samples. Recently developed methods such as DESI, REIMS, NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.) algorithms and statistical analysis, which are sensitive to outliers and intraclass variability. Here we present SSM Display software, a tool for fast visual outlier detection and variance estimation in mass spectrometric profiles. The tool speeds up the process of manual spectra inspection, improves accuracy and explainability of outlier detection, and decreases the requirements to the operator experience. It was shown that the batch effect could be revealed through SSM analysis and that the SSM calculation can also be used for tuning novel ion sources concerning the quality of obtained mass spectra. AVAILABILITY Source code, example datasets, binaries, and other information are available at https://github.com/EvgenyZhvansky/R_matrix. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- E S Zhvansky
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| | - A A Sorokin
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| | - D S Bormotov
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| | - K V Bocharov
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| | - D S Zavorotnyuk
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| | - D G Ivanov
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia.,Emanuel Institute for Biochemical Physics of the Russian Academy of Sciences, Moscow, Russia
| | - E N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - I A Popov
- Dolgoprudny, Moscow Institute of Physics and Technology, Russia
| |
Collapse
|
48
|
Fanciulli G, Ruggeri RM, Grossrubatscher E, Calzo FL, Wood TD, Faggiano A, Isidori A, Colao A. Serotonin pathway in carcinoid syndrome: Clinical, diagnostic, prognostic and therapeutic implications. Rev Endocr Metab Disord 2020; 21:599-612. [PMID: 32152781 DOI: 10.1007/s11154-020-09547-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Carcinoid syndrome represents the most common functional syndrome that affects patients with neuroendocrine neoplasms. Its clinical presentation is really heterogeneous, ranging from mild and often misdiagnosed symptoms to severe manifestations, that significantly worsen the patient's quality of life, such as difficult-to-control diarrhoea and fibrotic complications. Serotonin pathway alteration plays a central role in the pathophysiology of carcinoid syndrome, accounting for most clinical manifestations and providing diagnostic tools. Serotonin pathway is complex, resulting in production of biologically active molecules such as serotonin and melatonin, as well as of different intermediate molecules and final metabolites. These activities require site- and tissue-specific catalytic enzymes. Variable expression and activities of these enzymes result in different clinical pictures, according to primary site of origin of the tumour. At the same time, the biochemical diagnosis of carcinoid syndrome could be difficult even in case of typical symptoms. Therefore, the accuracy of the diagnostic methods of assessment should be improved, also attenuating the impact of confounding factors and maybe considering new serotonin precursors or metabolites as diagnostic markers. Finally, the prognostic role of serotonin markers has been only evaluated for its metabolite 5-hydroxyindole acetic acid but, due to heterogeneous and biased study designs, no definitive conclusions have been achieved. The most recent progress is represented by the new therapeutic agent telotristat, an inhibitor of the enzyme tryptophan hydroxylase, which blocks the conversion of tryptophan in 5-hydroxy-tryptophan. The present review investigates the clinical significance of serotonin pathway in carcinoid syndrome, considering its role in the pathogenesis, diagnosis, prognosis and therapy.
Collapse
Affiliation(s)
- Giuseppe Fanciulli
- Department of Medical, Surgical and Experimental Sciences, University of Sassari - Endocrine Unit, AOU Sassari, Sassari, Italy
| | - Rosaria M Ruggeri
- Department of Clinical and Experimental Medicine, Unit of Endocrinology, University of Messina, Messina, Italy
| | | | - Fabio Lo Calzo
- Department of Clinical Medicine and Surgery, Endocrinology Unit, University Federico II, Naples, Italy
| | - Troy D Wood
- Department of Chemistry, University at Buffalo, Buffalo, NY, USA
| | | | - Andrea Isidori
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Annamaria Colao
- Department of Clinical Medicine and Surgery, Endocrinology Unit, University Federico II, Naples, Italy
| | | |
Collapse
|
49
|
Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification. Nat Commun 2020; 11:5595. [PMID: 33154370 PMCID: PMC7644674 DOI: 10.1038/s41467-020-19354-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available. Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spectrometry datasets.
Collapse
|
50
|
Möginger U, Marcussen N, Jensen ON. Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections. Oncotarget 2020; 11:3998-4015. [PMID: 33216824 PMCID: PMC7646834 DOI: 10.18632/oncotarget.27787] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 10/10/2020] [Indexed: 12/24/2022] Open
Abstract
Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n = 11), clear cell renal cell carcinoma (ccRCC, n = 12) and chromophobe renal cell carcinoma (ChRCC, n = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.
Collapse
Affiliation(s)
- Uwe Möginger
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark.,Present address: Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park, Bagsværd, Denmark
| | - Niels Marcussen
- Institute for Pathology, Odense University Hospital, Odense, Denmark
| | - Ole N Jensen
- Department of Biochemistry & Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
| |
Collapse
|