= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Balog

MSACL 2023 Abstract

Self-Classified Topic Area(s): Metabolomics > Precision Medicine > Emerging Technologies

Podium Presentation in Steinbeck 1 on Wednesday at 15:30 (Chair: Eftychios Manoli / Hannah Brown)

Multi-Site Development of a Real-Time Breast Cancer Recognition and Tumor Metabolic Phenotyping Platform Using Rapid Evaporative Ionization Mass Spectrometry

Martin Kaufmann (1,2), Pierre-Maxence Vaysse (3,4,5), Adele Savage (6), Loes F.S. Kooreman (7,8), Natasja Janssen (9), Sonal Varma (10), Kevin Yi Mi Ren (11), Shaila Merchant (1), Cecil Jay Engel (1), Steven W.M. Olde Damink (4,11,12), Marjolein L. Smidt (4,8), Sami Shousha (13), Hemali Chauhan (6), Gabor Fichtinger (9), Steven D. Pringle (14), John F. Rudan (1), Tiffany Porta Siegel (3), Ron M.A. Heeren (3), Zoltan Takats (6), Julia Balog (6,15)
(1) Department of Surgery, Queen’s University, Kingston, ON, CA (2) Department of Biomedical and Molecular Sciences, Queen’s University, Kingston ON, CA (3) M4I Institute, Maastricht University, NL (4) Department of Surgery, Maastricht University Medical Center + (MUMC+), NL (5) Department of Otorhinolaryngology, Head & Neck Surgery, MUMC+, NL (6) Department of Surgery and Cancer, Imperial College London, London, UK (7) Department of Pathology, MUMC+, NL (8) GROW School for Oncology and Developmental Biology, MUMC+, NL (9) School of Computing, Queen’s University, Kingston ON, CA (10) Department of Pathology, Queen’s University, Kingston ON, CA (11) Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen, Aachen, Germany (12) NUTRIM School of Health, Maastricht University, NL (13) Imperial NHS Trust, London, UK (14) Waters Corporation, Wilmslow, UK (15) Waters Research Center, Budapest, Hungary

Julia Balog, PhD (Presenter)
Waters Research Center


Oncological surgery is aimed at the complete removal of all cancer cells within a margin of non-cancerous tissue, while minimizing destruction of non-cancerous tissue. Rapid evaporative ionization mass spectrometry (REIMSTM) has been shown to be capable of both detecting tumor cells in real-time, in addition to providing a snapshot of lipid metabolism. If real-time identification of genomic drivers – such as PIK3CA mutation – was possible during surgery, then REIMS could inform clinical decision-making during the course of surgery to avoid positive margins, but also to help inform the selection of adjuvant therapy.

Our goal was to (1) harmonize REIMS methods across 3 sites and evaluate cross-site performance of breast cancer recognition models to study the feasibility of real-time margin detection and (2) to investigate the correlation between fatty acid profiles detected in REIMS spectra in normal, wild-type and PIK3CA mutant breast cancer tissues.

Human breast tissue was collected at 3 sites (Imperial College London, UK; Maastricht M4I Institute, NL; and Queens University Kingston, CA) affiliated with clinical centers, from patients who underwent surgery for treatment of invasive breast cancer. After macroscopic examination, a local breast pathologist (four pathologists in total) selected tumor and/or normal tissue samples from 6-8 breast cancer surgery cases at each site. A strict protocol for storage and ex vivo REIMS sampling was developed and used for controlled aerosol generation. The evaporated tissue was analyzed with Waters XevoTM G2-XS Mass Spectrometers equipped with REIMS sources. Spectra were acquired in negative ion mode in the m/z 100-1500 mass range. For lipid identification, accurate mass measurements and MS/MS fragmentation were used. Multivariate analysis and subsequent classification were based on principal component analysis combined with linear discriminant analysis (PCA/LDA), while feature selection was performed using linear support vector classifier (lscv). Univariate analysis was done by first performing an F-test for testing the variance of the dataset, followed by a t-test.

A total of 21 patient samples were collected for the study including 16 invasive ductal carcinoma, 4 invasive lobular carcinoma, 1 ductal carcinoma in-situ (DCIS) and adjacent normal tissue respectively. After histological examination, all sampling points containing solely normal breast tissue or greater than 30% tumor tissue were used to build our database (n=71 cancer and n=139 normal tissue) and was tested at each site separately. Cross validation resulted in 96.4%, 97.11% and 98.57% when building the classifier from single-site or two-site data or using leave-one-patient out cross validation, respectively. The same results could be achieved using only 11 unique species selected by lsvc feature selection. The species selected by the algorithm also contained 3 fatty acids including oleic acid (18:1) enriched in normal tissue and arachidonic acid (20:4) and linoleic acid (18:2) enriched in tumor tissue. Since linoleic acid and arachidonic acid are main components of the omega-6-fatty acid metabolism pathway, we have studied the histological distribution of further 9 relevant fatty acids to further evaluate the upregulation of the omega-6 pathway versus the omega-3 pathway in different cancers. Our findings suggest that while the amount of linoleic acid (18:2) is significantly higher in normal tissue, all other components of the omega-6 fatty acid pathway are significantly (p<0.001) enriched in cancerous tissue including arachidonic acid, dihomo-γ-linoleic acid (DGLA) and FA(22:5). Comparing tumors with PIK3CA mutation to wild type (WT) ones, we also found a significant upregulation of the FA(18:2) - FA(20:2) - FA(20:3) synthetic pathway (p<0.001) leading to a further enhancement of the omega-6 production. In agreement with previous studies, there is also a significant boost in normal tissue towards doxosahexaenoic acid (DHA) showing a better balance of the omega-3 vs omega-6 metabolism when no cancer is present. These observations correlate with previously reported findings showing a significant increase in arachidonic acid levels in tumors linked with an upregulated omega-6 fatty acid metabolism.

Invasive breast cancer was successfully differentiated from normal breast tissue using real-time mass spectrometric profiling across multiple sites. Recognition models created at each clinical site accurately detected breast cancer at the two other sites, demonstrating the robustness of this approach and algorithms for future clinical use. Furthermore, relative fatty acid concentrations discerned from REIMS were associated with clinically-relevant tumor features such as PIK3CA mutation based on the upregulated omega-6 fatty acid pathway, which can lead to less invasive interventions. Our study of fatty acid metabolism in human tissues correlates to previous work that emphasizes the importance of a balanced omega-3 vs omega-6 diet for the suppression of cancer progression.

Novel aspects.
Cross-site validation of ex vivo breast cancer recognition by REIMS demonstrating applicability to the clinic, fatty acid metabolism monitoring with REIMS for identification of oncogenic PIK3CA mutations.

Financial Disclosure

SalaryyesWaters Research Center - part of Waters corporation
Board Memberno
IP Royaltyno

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