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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)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Bioinformatics ; Cancer ; Metabolism ; Oncology; Imaging Mass-spectrometry; Intratumor Heterogeneity; Genomic Characterization; Cancer; Management; Chemotherapy; Multicenter; Mechanisms; Algorithm; Proposal
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2379-3708
e-ISSN 2379-3708
Zeitschrift JCI insight
Quellenangaben Band: 8, Heft: 16, Seiten: , Artikelnummer: 18 Supplement: ,
Verlag Clarivate
Verlagsort Ann Arbor, Michigan
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Analytical Pathology (AAP)
CF Statistical Consulting (CF-STATCON)
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
DOI 10.1172/jci.insight.167007
WOS ID 001054473800001
Scopus ID 85168528935
PubMed ID 37606037
Erfassungsdatum 2023-10-06
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