Evidence map›Paper›PMID 31160688›Full record

ArticleLaboratory investigation; a journal of technical methods and pathology2019

Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies.

Nicole Morse, Tamara Jamaspishvili, David Simon, Palak G Patel, Kevin Yi Mi Ren, Jenny Wang, Richard Oleschuk, Martin Kaufmann, Robert J Gooding, David M Berman

Open access · bronzeAbstract readValidation Study
PubMed Publisher
In one paragraph

Article in Laboratory investigation; a journal of technical methods and pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.3field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 54 citations in OpenAlex.

  1. Review
  2. Advances in spatial omics for the analysis of prostate cancer.Analytical and bioanalytical chemistry · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Nicole MorseCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada.
Tamara JamaspishviliCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada.
David SimonDepartment of Chemistry, Queen's University, Kingston, ON, K7L 3N6, Canada.
Palak G PatelCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada.
Kevin Yi Mi RenDepartment of Pathology & Molecular Medicine, Queen's University, Kingston, ON, K7L 3N6, Canada.
Jenny WangCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada.
Richard OleschukDepartment of Chemistry, Queen's University, Kingston, ON, K7L 3N6, Canada.
Martin KaufmannDepartment of Surgery, Queen's University, Kingston, ON, K7L 3N6, Canada.
Robert J GoodingCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada.
David M BermanCancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada. bermand@queensu.ca.ORCID http://orcid.org/0000-0001-5985-3698
Queen's University · CA

Funding

CIHR
6 · The paper itself

Abstract

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 phosphatidylcholines (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.

Indexed as

MetabolomeSpectrometry, Mass, Electrospray IonizationBiopsy, NeedleHumansMaleMetabolomicsProstateProstatic Neoplasms

Identifiers

PMID31160688
OpenAlexW2948504187

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.