Metabolomics patterns of breast cancer tumors using mass spectrometry imaging



Intraoperative assessment of surgical margins is important for reducing the rate of revisions in breast conserving surgery for palpable malignant tumors. The hypothesis was that metabolomics methods, based on mass spectrometry, could find patterns of relative abundances of molecules that distinguish clusters of benign tissue and cancer in surgical resections.


Excisions from 8 patients were used to acquire 112,317 mass spectrometry signals by desorption electrospray ionization. A process of nonnegative matrix factorization and graph decomposition produced clusters that were approximated as affine spaces. Each signal’s distance to the affine space of a cluster was used to visualize the clustering.


The distance maps were superior to binary clustering in identifying cancer regions. They were particularly effective at finding cancer regions that were discontinuously distributed within benign tissue.


Desorption electrospray ionization mass spectrometry, which has been shown to be useful intraoperatively, can acquire signals that distinguish malignant from benign breast tissue in surgically excised tumors. The method may be suitable for real-time surgical decisions based on cancer margins.

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Nicole Morse contributed to the DESI-MS data acquisition when she was a graduate student. We are grateful to Emma Ritcey and Tyler Mainguy for their computational assistance in preprocessing the data. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant RGPIN-2018-04430, and in part by a Vector Scholarship in Artificial Intelligence, provided through the Vector Institute.

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Correspondence to Randy E. Ellis.

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This study was conducted in accordance with the principles outlined in the Declaration of Helsinki for the use of human tissue.

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Theriault, R.L., Kaufmann, M., Ren, K.Y.M. et al. Metabolomics patterns of breast cancer tumors using mass spectrometry imaging. Int J CARS 16, 1089–1099 (2021).

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  • Metabolomics
  • Breast cancer
  • Mass spectrometry imaging