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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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2
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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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.
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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
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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5
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Van Hese L, Vaysse PM, Siegel TP, Heeren R, Rex S, Cuypers E. Real-time drug detection using a diathermic knife combined to rapid evaporative ionisation mass spectrometry. Talanta 2021; 221:121391. [PMID: 33076053 DOI: 10.1016/j.talanta.2020.121391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 11/27/2022]
Abstract
Fast, accurate and sensitive detection of drugs in human tissue is of crucial importance in an investigation of a suspicious death. Here, we aimed to screen cocaine, diazepam, methadone and morphine in post-mortem muscle samples without sample preparation and in quasi-real time using rapid evaporative ionisation mass spectrometry (REIMS). REIMS enables the online MS analysis of vapours generated from tissue dissection by a diathermic knife. Human muscle samples were soaked in solutions of 4 drugs at different concentrations and multiple incubation times to check the feasibility of REIMS for this innovative application. Muscle samples soaked in blank saline were used as a control. The classification model was able to distinguish between 30 μg g-1 cocaine (m/z 304.2), 200 μg g-1 morphine (m/z 286.2), 10 μg g-1 methadone (m/z 310.2) and 10 μg g-1 muscle of diazepam (m/z 285.1). REIMS tandem MS confirmed that the mass peaks that contributed to the class separation, originated from the drugs of interest. As a proof-of-concept, a forensic case muscle sample from a methadone overdose was investigated using REIMS. Here, using our classification model, the recognition software was able to detect methadone, demonstrating that the REIMS method opens new possibilities in forensic toxicology and during autopsy, leading to faster crime solving and decreased costs.
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Affiliation(s)
- Laura Van Hese
- Department of Anaesthesiology, University Hospitals Leuven, Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium; Toxicology and Pharmacology, KU Leuven, 3000, Leuven, Belgium; Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229, ER, Maastricht, the Netherlands
| | - 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+, Maastricht, 6229, HX, the Netherlands; Department of Otorhinolaryngology, Head & Neck Surgery, Maastricht University Medical Center+, Maastricht, 6229, HX, the Netherlands
| | - Tiffany Porta Siegel
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229, ER, Maastricht, the Netherlands
| | - Ron Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229, ER, Maastricht, the Netherlands
| | - Steffen Rex
- Department of Anaesthesiology, University Hospitals Leuven, Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium
| | - Eva Cuypers
- Toxicology and Pharmacology, KU Leuven, 3000, Leuven, Belgium; Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229, ER, Maastricht, the Netherlands.
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Vaysse PM, Kooreman LFS, Engelen SME, Kremer B, Olde Damink SWM, Heeren RMA, Smidt ML, Porta Siegel T. Stromal vapors for real-time molecular guidance of breast-conserving surgery. Sci Rep 2020; 10:20109. [PMID: 33208813 PMCID: PMC7674429 DOI: 10.1038/s41598-020-77102-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/05/2020] [Indexed: 11/17/2022] Open
Abstract
Achieving radical tumor resection while preserving disease-free tissue during breast-conserving surgery (BCS) remains a challenge. Here, mass spectrometry technologies were used to discriminate stromal tissues reported to be altered surrounding breast tumors, and build tissue classifiers ex vivo. Additionally, we employed the approach for in vivo and real-time classification of breast pathology based on electrosurgical vapors. Breast-resected samples were obtained from patients undergoing surgery at MUMC+. The specimens were subsequently sampled ex vivo to generate electrosurgical vapors analyzed by rapid evaporative ionization mass spectrometry (REIMS). Tissues were processed for histopathology to assign tissue components to the mass spectral profiles. We collected a total of 689 ex vivo REIMS profiles from 72 patients which were analyzed using multivariate statistical analysis (principal component analysis-linear discriminant analysis). These profiles were classified as adipose, stromal and tumor tissues with 92.3% accuracy with a leave-one patient-out cross-validation. Tissue recognition using this ex vivo-built REIMS classification model was subsequently tested in vivo on electrosurgical vapors. Stromal and adipose tissues were classified during one BCS. Complementary ex vivo analyses were performed by REIMS and by desorption electrospray ionization mass spectrometry (DESI-MS) to study the potential of breast stroma to guide BCS. Tumor border stroma (TBS) and remote tumor stroma (RTS) were classified by REIMS and DESI-MS with 86.4% and 87.8% accuracy, respectively. We demonstrate the potential of stromal molecular alterations surrounding breast tumors to guide BCS in real-time using REIMS analysis of electrosurgical vapors.
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Affiliation(s)
- Pierre-Maxence Vaysse
- Division of Imaging Mass Spectrometry, 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 and Neck Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Loes F S Kooreman
- 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
| | - Bernd Kremer
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, 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
| | - Ron M A Heeren
- Division of Imaging Mass Spectrometry, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Marjolein L Smidt
- Department of Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Tiffany Porta Siegel
- Division of Imaging Mass Spectrometry, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands.
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Vaysse PM, Heeren RMA, Porta T, Balluff B. Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations. Analyst 2018. [PMID: 28642940 DOI: 10.1039/c7an00565b] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.
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Affiliation(s)
- Pierre-Maxence Vaysse
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Tiffany Porta
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging (M4I) institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
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