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 to trastuzumab treatment were investigated across the subtypes by 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 subtype 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.

Recent genetic examinations and multisteroid profiles have provided the basis for subclassification of aldosterone-producing adenomas (APAs). The objective of the current study was to produce a comprehensive, high-resolution mass spectrometry imaging (MSI) map of APAs in relation to morphometry, immunohistochemical profiles, mutational status, and clinical outcome. The study cohort comprised 136 patients with unilateral primary aldosteronism. Matrix-assisted laser desorption/ionization–Fourier transform–ion cyclotron resonance MSI was conducted, and metabolite profiles were analyzed with genotype/phenotype information, including digital image analysis from morphometry and IHC of steroidogenic enzymes. Distinct molecular signatures between KCNJ5 - and CACNA1D -mutated APAs with significant differences of 137 metabolites, including metabolites of purine metabolism and steroidogenesis, were observed. Intratumor concentration of 18-oxocortisol and 18-hydroxycortisol were inversely correlated with the staining intensity of CYP11B1. Lower staining intensity of CYP11B1 and higher levels of 18-oxocortisol were associated with a higher probability of complete clinical success after surgery. The present study demonstrates distinct metabolomic profiles of APAs in relation to tumor genotype. In addition, we reveal an inverse correlation between cortisol derivatives and CYP11B1 and the impact of 18-oxocortisol and CYP11B1 on clinical outcome, which provides unprecedented insights into the pathophysiology, clinical features, and steroidogenesis of APAs.

High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan–Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.

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.

The adrenal gland integrates catecholamine-producing neuroendocrine cells and steroid-producing cells with mesenchymal origin in a structured manner under one capsule and is a key regulator for vital bioactivity. In addition to adrenal-specific disease, dysregulation of adrenal hormones is associated with systemic effects, leading to undesirable metabolic and cardiovascular consequences. Mass spectrometry imaging (MSI) technique can simultaneously measure a broad range of biomolecules, including metabolites and hormones, which has enabled the study of tissue metabolic and hormone alterations in adrenal and adrenal-related diseases. Furthermore, this technique coupled with labeled immunohistochemistry staining has enabled the study of the pathophysiological adaptation of the adrenal gland under normal and abnormal conditions at different molecular levels. This review discusses the recent applications of in situ MSI in the adrenal gland. For example, the combination of formalin-fixed paraffin-embedded tissue microarray and MSI to tissues from patient cohorts has facilitated the discovery of clinically relevant prognostic biomolecules and generated promising hypotheses for new sights into physiology and pathophysiology of adrenal gland. MSI also has enabled the discovery of clinically significant tissue molecular (i. e., biomarker) and pathway changes in adrenal disease, particularly in adrenal tumors. In addition, MSI has advanced the ability to optimally identify and detect adrenal gland specific molecules. Thus, as a novel analytical methodology, MSI has provided unprecedented capabilities for in situ tissue study.