Mass spectrometry imaging identifies metabolic patterns associated with malignant potential in pheochromocytoma and paraganglioma

Laboratory Testing for Endocrine Hypertension: Current and Future Perspectives

BACKGROUND

Secondary hypertension (SH) is a form of high blood pressure caused by an identifiable underlying condition. Although, it accounts for a small fraction of the overall hypertensive population, detection and management of SH is of utmost importance, because SH phenotypes carry a high cardiovascular risk and can possibly be cured by timely treatment.

CONTENT

This review focuses on the endocrine causes of SH, such as primary aldosteronism, Cushing syndrome, thyroid disease, pheochromocytoma and paraganglioma, acromegaly, and rare monogenic forms. It discusses current biomarkers, analytical methods, and diagnostic strategies, highlighting advantages and limitations of each approach. It also explores the emerging -omics technologies that can provide a comprehensive and multidimensional assessment of SH and its underlying mechanisms.

SUMMARY

Endocrine SH is a heterogeneous and complex condition that requires proper screening and confirmatory tests to avoid diagnostic delays and improve patient outcomes. Careful biomarker interpretation is essential due to potential interferences, variability, and method-dependent differences. Liquid chromatography-tandem mass spectrometry is a superior method for measuring low-concentration hormones and metabolites involved in SH, but it requires expertise. Omics approaches have great potential to identify novel biomarkers, pathways, and targets for SH diagnosis and treatment, especially considering its multifactorial nature.

Metabolism of pancreatic neuroendocrine tumors: what can omics tell us?

Introduction

Reprogramming of cellular metabolism is now a hallmark of tumorigenesis. In recent years, research on pancreatic neuroendocrine tumors (pNETs) has focused on genetic and epigenetic modifications and related signaling pathways, but few studies have been devoted to characterizing the metabolic profile of these tumors. In this review, we thoroughly investigate the metabolic pathways in pNETs by analyzing the transcriptomic and metabolomic data available in the literature.

Methodology

We retrieved and downloaded gene expression profiles from all publicly available gene set enrichments (GSE43797, GSE73338, and GSE117851) to compare the differences in expressed genes based on both the stage and MEN1 mutational status. In addition, we conducted a systematic review of metabolomic data in NETs.

Results

By combining transcriptomic and metabolomic approaches, we have identified a distinctive metabolism in pNETs compared with controls without pNETs. Our analysis showed dysregulations in the one-carbon, glutathione, and polyamine metabolisms, fatty acid biosynthesis, and branched-chain amino acid catabolism, which supply the tricarboxylic acid cycle. These targets are implicated in pNET cell proliferation and metastasis and could also have a prognostic impact. When analyzing the profiles of patients with or without metastasis, or with or without MEN1 mutation, we observed only a few differences due to the scarcity of published clinical data in the existing research. Consequently, further studies are now necessary to validate our data and investigate these potential targets as biomarkers or therapeutic solutions, with a specific focus on pNETs.

Metastatic Pheochromocytoma and Paraganglioma: Somatostatin Receptor 2 Expression, Genetics, and Therapeutic Responses

CONTEXT

Pheochromocytomas and paragangliomas (PPGLs) with pathogenic mutations in the succinate dehydrogenase subunit B (SDHB) are associated with a high metastatic risk. Somatostatin receptor 2 (SSTR2)-dependent imaging is the most sensitive imaging modality for SDHB-related PPGLs, suggesting that SSTR2 expression is a significant cell surface therapeutic biomarker of such tumors.

OBJECTIVE

Exploration of the relationship between SSTR2 immunoreactivity and SDHB immunoreactivity, mutational status, and clinical behavior of PPGLs. Evaluation of SSTR-based therapies in metastatic PPGLs.

METHODS

Retrospective analysis of a multicenter cohort of PPGLs at 6 specialized Endocrine Tumor Centers in Germany, The Netherlands, and Switzerland. Patients with PPGLs participating in the ENSAT registry were included. Clinical data were extracted from medical records, and immunohistochemistry (IHC) for SDHB and SSTR2 was performed in patients with available tumor tissue. Immunoreactivity of SSTR2 was investigated using Volante scores. The main outcome measure was the association of SSTR2 IHC positivity with genetic and clinical-pathological features of PPGLs.

RESULTS

Of 202 patients with PPGLs, 50% were SSTR2 positive. SSTR2 positivity was significantly associated with SDHB- and SDHx-related PPGLs, with the strongest SSTR2 staining intensity in SDHB-related PPGLs (P = .01). Moreover, SSTR2 expression was significantly associated with metastatic disease independent of SDHB/SDHx mutation status (P < .001). In metastatic PPGLs, the disease control rate with first-line SSTR-based radionuclide therapy was 67% (n = 22, n = 11 SDHx), and with first-line "cold" somatostatin analogs 100% (n = 6, n = 3 SDHx).

CONCLUSION

SSTR2 expression was independently associated with SDHB/SDHx mutations and metastatic disease. We confirm a high disease control rate of somatostatin receptor-based therapies in metastatic PPGLs.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

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.

In Situ Metabolomics of Cortisol-Producing Adenomas

BACKGROUND

Recent advances in omics techniques have allowed detailed genetic characterization of cortisol-producing adrenal adenoma (CPA). In contrast, the pathophysiology of CPAs has not been elucidated in detail on the level of tumor metabolic alterations.

METHODS

The current study conducted a comprehensive mass spectrometry imaging (MSI) map of CPAs in relation to clinical phenotypes and immunohistochemical profiles of steroidogenic enzymes. The study cohort comprised 46 patients with adrenal tumors including CPAs (n 35) and nonfunctional adenomas (n 11).

RESULTS

Severity of cortisol hypersecretion was significantly correlated with 29 metabolites (adjusted P 0.05). Adrenal androgens derived from the classic androgen pathway were inversely correlated with both cortisol secretion (rs 0.41, adjusted P 0.035) and CYP11B1 expression (rs 0.77, adjusted P 2.00E-08). The extent of cortisol excess and tumor CYP11B1 expression further correlated with serotonin (rs 0.48 and 0.62, adjusted P 0.008 and 2.41E-05). Tumor size was found to be correlated with abundance of 13 fatty acids (adjusted P 0.05) and negatively associated with 9 polyunsaturated fatty acids including phosphatidic acid 38:8 (rs 0.56, adjusted P 0.009).

CONCLUSIONS

MSI reveals novel metabolic links between endocrine function and tumorigenesis, which will further support the understanding of CPA pathophysiology.

Mass Spectrometry Imaging technology in metabolomics: a systematic review

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.

OBJECTIVE

Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.

DESIGN

Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.

PATIENTS

Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.

RESULTS

Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.

CONCLUSIONS

The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.