Evidence map›Paper›PMID 27841360›Full record

ArticleScientific reports2016

Assessment of pathological response to therapy using lipid mass spectrometry imaging.

Nathan Heath Patterson, Balqis Alabdulkarim, Anthoula Lazaris, Aurélien Thomas, Mieczyslaw M Marcinkiewicz, Zu-Hua Gao, Peter B Vermeulen, Pierre Chaurand, Peter Metrakos

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Lipidomic profiling of endometrial cancer using desorption electrospray ionization mass spectrometry imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Review
  5. Article
  6. Mass Spectrometry Imaging.Analytical chemistry · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Quantitative mass spectrometry imaging: therapeutics & biomolecules.Chemical communications (Cambridge, England) · 2024
    Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Review
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

9 authors.

Nathan Heath PattersonDepartment of Chemistry, University of Montreal, Montreal, Quebec, Canada.
Balqis AlabdulkarimDepartment of Surgery, McGill University Health Center, Quebec, Canada.
Anthoula LazarisDepartment of Surgery, McGill University Health Center, Quebec, Canada.
Aurélien ThomasUnit of Toxicology, University Centre of Legal Medicine, Geneva-Lausanne, Switzerland.
Mieczyslaw M MarcinkiewiczCytochem Inc., 6465 Durocher Avenue, Montreal, Quebec, Canada.
Zu-Hua GaoCancer Research Program, McGill University Health Center-Research Institute, Quebec, Canada.
Peter B VermeulenTranslational Cancer Research Unit, GZA Hospitals St.-Augustinus, 2610, Wilrijk, Belgium.
Pierre ChaurandDepartment of Chemistry, University of Montreal, Montreal, Quebec, Canada.
Peter MetrakosDepartment of Surgery, McGill University Health Center, Quebec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In many cancers, the establishment of a patient's future treatment regime often relies on histopathological assessment of tumor tissue specimens in order to determine the extent of the 'pathological response' to a given therapy. However, histopathological assessment of pathological response remains subjective. Here we use MALDI mass spectrometry imaging to generate lipid signatures from colorectal cancer liver metastasis specimens resected from patients preoperatively treated with chemotherapy. Using these signatures we obtained a unique pathological response score that correlates with prognosis. In addition, we identify single lipid moieties that are overexpressed in different histopathological features of the tumor, which have potential as new biomarkers for assessing response to therapy. These data show that computational methods, focusing on the lipidome, can be used to determine prognostic markers for response to chemotherapy and may potentially improve risk assessment and patient care.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorChemotherapy, AdjuvantColorectal NeoplasmsFemaleHumansLipidsLiver NeoplasmsMaleMiddle AgedPrognosisSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationTreatment OutcomeBiomarkers, TumorLipids

Identifiers

PMID27841360
PMCPMC5107952

What OpenQuestion holds

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LicenceCC BY
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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.