Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

Autor:	Kaufmann M; Department of Surgery, Queen's University, Kingston, ON, Canada.; Gastrointestinal Diseases Research Unit, Kingston Health Sciences Centre, Kingston, ON, Canada., Vaysse PM; Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands.; Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands.; Department of Otorhinolaryngology, Head & Neck Surgery, MUMC+, Maastricht, NL, Netherlands., Savage A; Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK., Kooreman LFS; Department of Pathology, MUMC+, Maastricht, NL, Netherlands.; GROW School for Oncology and Reproduction, MUMC+, Maastricht, NL, Netherlands., Janssen N; School of Computing, Queen's University, Kingston, ON, Canada., Varma S; Department of Pathology, Queen's University, Kingston, ON, Canada., Ren KYM; Department of Pathology, Queen's University, Kingston, ON, Canada., Merchant S; Department of Surgery, Queen's University, Kingston, ON, Canada., Engel CJ; Department of Surgery, Queen's University, Kingston, ON, Canada., Olde Damink SWM; Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, 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, NL, Netherlands., Smidt ML; Department of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands.; GROW School for Oncology and Reproduction, MUMC+, Maastricht, NL, Netherlands., Shousha S; Imperial NHS Trust, London, UK., Chauhan H; Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK., Karali E; Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK., Kazanc E; Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK., Poulogiannis G; Signalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK., Fichtinger G; School of Computing, Queen's University, Kingston, ON, Canada., Tauber B; Qamcom Research & Technology Central Europe, Budapest, Hungary., Leff DR; Department of Surgery and Cancer, Biosurgery and Surgical Technology, Imperial College London, London, UK., Pringle SD; Waters Corporation, Wilmslow, UK., Rudan JF; Department of Surgery, Queen's University, Kingston, ON, Canada., Heeren RMA; Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands., Porta Siegel T; Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands., Takáts Z; Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK., Balog J; Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. Julia_balog@waters.com.; Waters Research Center, Budapest, Hungary. Julia_balog@waters.com.
Jazyk:	angličtina
Zdroj:	British journal of cancer [Br J Cancer] 2024 Nov; Vol. 131 (8), pp. 1298-1308. Date of Electronic Publication: 2024 Sep 18.
DOI:	10.1038/s41416-024-02739-y
Abstrakt:	Background: While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. Methods: A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. Results: Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. Conclusions: The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach. (© 2024. Waters Technologies Corporation and The Author(s).)
Databáze:	MEDLINE
Externí odkaz:	Full text from SpringerLink