Molecular subtyping of gastric cancer according to ACRG using immunohistochemistry - Correlation with clinical parameters.
ACRG
EMT
Gastric cancer
Molecular subtyping
Tumor budding
Journal
Pathology, research and practice
ISSN: 1618-0631
Titre abrégé: Pathol Res Pract
Pays: Germany
ID NLM: 7806109
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
14
12
2021
accepted:
04
02
2022
pubmed:
13
2
2022
medline:
29
3
2022
entrez:
12
2
2022
Statut:
ppublish
Résumé
Gastric cancer (GC) is a very heterogenous disease necessitating further stratification for prognostic and therapeutic aspects. Based on the recommendation of The Asisan Cancer Research Group (ACRG) recently established four molecular subtypes (MSI, MSS/EMT, MSS/TP53+, MSS/TP53-) which require molecular expression analysis. The technology required for comprehensive molecular analysis is expensive and not applicable for routine diagnostics. Thus, in this study we established a classification system utilizing immunohistochemistry and morphology-based analyses as surrogate markers in order to reproduce the ACRG molecular subtypes of gastric cancer. To clarify the clinical relevance of the novel classification system, we performed a correlation with established clinical parameters. The study cohort consisted of 189 patients with GC (UICC III and IV). Using immunohistochemistry, the following markers were analysed: MLH1, MSH2, MSH6, PMS2 (as a surrogate for microsatellite status), p53, SOX9. We assessed tumor budding as a surrogate for EMT to distinguish between MSS/EMT and MSS/non-EMT groups. Immunohistochemical and morphologic subtyping classified cases as follows: 10% MSI, 35% MSS/EMT, 16% MSS/TP53 + and 39% MSS/TP53-. Subtypes significantly correlated with the Lauren classification, tumor stage, venous invasion and SOX9 expression (p < .05). There was no significant correlation between molecular subtype and lymph node growth pattern. We propose a simple algorithm for molecular subtyping of GC using universally available immunohistochemistry, which correlates with clinical parameters and is cost-effective and applicable in diagnostic routine. This classification might prospectively help to determine patient prognosis, optimize patient care and homogenize patient cohorts for clinical trials.
Sections du résumé
BACKGROUND
BACKGROUND
Gastric cancer (GC) is a very heterogenous disease necessitating further stratification for prognostic and therapeutic aspects. Based on the recommendation of The Asisan Cancer Research Group (ACRG) recently established four molecular subtypes (MSI, MSS/EMT, MSS/TP53+, MSS/TP53-) which require molecular expression analysis. The technology required for comprehensive molecular analysis is expensive and not applicable for routine diagnostics. Thus, in this study we established a classification system utilizing immunohistochemistry and morphology-based analyses as surrogate markers in order to reproduce the ACRG molecular subtypes of gastric cancer. To clarify the clinical relevance of the novel classification system, we performed a correlation with established clinical parameters.
METHODS
METHODS
The study cohort consisted of 189 patients with GC (UICC III and IV). Using immunohistochemistry, the following markers were analysed: MLH1, MSH2, MSH6, PMS2 (as a surrogate for microsatellite status), p53, SOX9. We assessed tumor budding as a surrogate for EMT to distinguish between MSS/EMT and MSS/non-EMT groups.
RESULTS
RESULTS
Immunohistochemical and morphologic subtyping classified cases as follows: 10% MSI, 35% MSS/EMT, 16% MSS/TP53 + and 39% MSS/TP53-. Subtypes significantly correlated with the Lauren classification, tumor stage, venous invasion and SOX9 expression (p < .05). There was no significant correlation between molecular subtype and lymph node growth pattern.
CONCLUSION
CONCLUSIONS
We propose a simple algorithm for molecular subtyping of GC using universally available immunohistochemistry, which correlates with clinical parameters and is cost-effective and applicable in diagnostic routine. This classification might prospectively help to determine patient prognosis, optimize patient care and homogenize patient cohorts for clinical trials.
Identifiants
pubmed: 35151032
pii: S0344-0338(22)00040-1
doi: 10.1016/j.prp.2022.153797
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
153797Informations de copyright
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