TY - JOUR
T1 - Spatial Metabolomics Identifies Distinct Tumor-Specific Subtypes in Gastric Cancer Patients
AU - Wang, J.
AU - Kunzke, T.
AU - Prade, V.M.
AU - Shen, J.
AU - Buck, A.
AU - Feuchtinger, A.
AU - Haffner, I.
AU - Luber, B.
AU - Liu, D.H.W.
AU - Langer, R.
AU - Lordick, F.
AU - Sun, N.
AU - Walch, A.
N1 - Funding Information:
The authors thank Ulrike Buchholz, Claudia-Mareike Pflu€ger, Cristina Hu€bner Freitas, Andreas Voss, and Elenore Samson for excellent technical assistance. The study was supported by the Ministry of Education and Research of the Federal Republic of Germany (BMBF; 01ZX1610B and 01KT1615; to A. Walch); the Deutsche Forschungsgmeinschaft SFB 824 C4, CRC/Transregio 205/1; to A. Walch); and the China Scholarship Council (CSC; No. 201906210076; to J. Wang).
Funding Information:
F. Lordick reports grants, personal fees, and other support from BMS, as well as personal fees from AstraZeneca, Astellas, Eli Lilly, MSD, Merck Serono, Roche, Amgen, BioNTech, MedUpdate, StreamedUp!, and Novartis outside the submitted work. No disclosures were reported by the other authors.
Publisher Copyright:
©2022 American Association for Cancer Research
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Purpose: Current systems of gastric cancer molecular classification include genomic, molecular, and morphological features. Gastric cancer classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct gastric cancer subtypes and identify their clinicopathological and molecular characteristics. Experimental Design: Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 patients with gastric cancer. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses trastuzumab treatment were investigated across the subtypes introducing an independent patient cohort with HER2-positive gastric cancer from a multicenter observational study. Results: Three tumor-and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2(+)MIB(+)CD3(+)) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2(-)MIB(-)CD3(-)) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR(+)) positively correlated with pEGFR. Patients with tumor sub-type T1(HER2(+)MIB(+)CD3(+)) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2 (HER2(-)MIB(-)CD3(-)) and T3(pEGFR(+)). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however, linked to distinct metabolic pathways and molecular features. Conclusions: Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing gastric cancer molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.
AB - Purpose: Current systems of gastric cancer molecular classification include genomic, molecular, and morphological features. Gastric cancer classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct gastric cancer subtypes and identify their clinicopathological and molecular characteristics. Experimental Design: Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 patients with gastric cancer. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses trastuzumab treatment were investigated across the subtypes introducing an independent patient cohort with HER2-positive gastric cancer from a multicenter observational study. Results: Three tumor-and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2(+)MIB(+)CD3(+)) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2(-)MIB(-)CD3(-)) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR(+)) positively correlated with pEGFR. Patients with tumor sub-type T1(HER2(+)MIB(+)CD3(+)) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2 (HER2(-)MIB(-)CD3(-)) and T3(pEGFR(+)). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however, linked to distinct metabolic pathways and molecular features. Conclusions: Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing gastric cancer molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.
KW - MOLECULAR CLASSIFICATION
KW - MASS-SPECTROMETRY
KW - METABOLITES
KW - RESPONSES
KW - PD-1
U2 - 10.1158/1078-0432.ccr-21-4383
DO - 10.1158/1078-0432.ccr-21-4383
M3 - Article
SN - 1078-0432
VL - 28
SP - 2865
EP - 2877
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 13
ER -