David Berman" class="rev-slidebg" data-no-retina> INNOVATION FUND Technology and AI in Healthcare Technological Innovations SHOWCASE 2019 David Berman" class="rev-slidebg" data-no-retina> INNOVATION FUND Technology and AI in Healthcare Technological Innovations SHOWCASE 2019

Towards a metabolomic classification of prostate cancer using mass spectral imaging

Technological Innovations


David Berman

bermand@queensu.ca

613-329-1410

SEAMO, Queen’s University

Highlights

SEA-16-001 Mass spectrometry imaging (MSI) is a potential adjunct to histopathology. However, studies have yet to adequately test its performance and reliability. Using over 900 MSI spectra, we established and validated an accurate, high-resolution metabolic profile of prostate cancer, supporting the development of mass spectrometry applications in surgery and surgical pathology.

Abstract

SEA-16-001 Metabolomic profiling can aid in understanding crucial biological processes in cancer development and progression and can also yield diagnostic biomarkers. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) has been proposed as a potential adjunct to diagnostic surgical pathology, particularly for prostate cancer. However, due to low resolution sampling, small numbers of mass spectra, and little validation, published studies have yet to test whether this method is sufficiently robust to merit clinical translation. We used over 900 spatially resolved DESI-MSI spectra to establish an accurate, high-resolution metabolic profile of prostate cancer. We identified 25 differentially abundant metabolites, with cancer tissue showing increased fatty acids (FAs) and phospholipids, along with utilization of the Krebs cycle, and benign tissue showing increased levels of lyso-phosphatidylethanolamine (PE). Additionally, we identified, for the first time, two lyso-PEs with abundance that decreased with cancer grade and two PChs with increased abundance with increasing cancer grade. Importantly, we developed and internally validated a multivariate metabolomic classifier for prostate cancer using 534 spatial regions of interest (ROIs) in the training cohort and 430 ROIs in the test cohort. With excellent statistical power, the training cohort achieved a balanced accuracy of 97% and validation on testing data set demonstrated 85% balanced accuracy. Given the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established patterns of prostate cancer cell metabolism, we conclude that DESI-MSI is an effective tool for characterizing prostate cancer metabolism with the potential for clinical translation.

Themes & Abstracts

Search

Generic selectors
Exact matches only
Search in title
Search in content
Search in posts
Search in pages

©2020 IFPOC - The Innovation Fund Provincial Oversight Committee - created by Techna

CONTACT US

We're not around right now. But you can send us an email and we'll get back to you, asap.

Sending

Log in with your credentials

Forgot your details?