Wang, Q. ; Sun, N. ; Meixner, R. ; Le Gleut, R. ; Kunzke, T. ; Feuchtinger, A. ; Wang, J. ; Shen, J. ; Kircher, S.* ; Dischinger, U.* ; Weigand, I.* ; Beuschlein, F.* ; Fassnacht, M.* ; Kroiss, M.* ; Walch, A.K.
Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes.
JCI insight 8:18 (2023)
Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Bioinformatics ; Cancer ; Metabolism ; Oncology; Imaging Mass-spectrometry; Intratumor Heterogeneity; Genomic Characterization; Cancer; Management; Chemotherapy; Multicenter; Mechanisms; Algorithm; Proposal
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2023
Prepublished im Jahr
0
HGF-Berichtsjahr
2023
ISSN (print) / ISBN
2379-3708
e-ISSN
2379-3708
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 8,
Heft: 16,
Seiten: ,
Artikelnummer: 18
Supplement: ,
Reihe
Verlag
Clarivate
Verlagsort
Ann Arbor, Michigan
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Peer reviewed
POF Topic(s)
30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-500390-001
A-632200-001
Förderungen
University Hospital of Wuerzburg
Deutsche Krebshilfe
Deutsche Forschungsgemeinschaft
China Scholarship Council
Copyright
Erfassungsdatum
2023-10-06