A Prospective Targeted Serum Metabolomics Study of Pancreatic Cancer in Postmenopausal Women

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

A blood-based metabolomic signature predictive of risk for pancreatic cancer

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Plasma Metabolomics Predicts Chemotherapy Response in Advanced Pancreatic Cancer

Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.

The Association between Serum Serine and Glycine and Related-Metabolites with Pancreatic Cancer in a Prospective Cohort Study

Background. Serine and glycine play an important role in the folate-dependent one-carbon metabolism. The metabolism of serine and glycine has been shown to be associated with cancer cell proliferation. No prior epidemiologic study has investigated the associations for serum levels of serine and glycine with pancreatic cancer risk. Methods. We conducted a nested case-control study involved 129 incident pancreatic cancer cases and 258 individually matched controls within a prospective cohort study of 18,244 male residents in Shanghai, China. Glycine and serine and related metabolites in pre-diagnostic serum were quantified using gas chromatography-tandem mass spectrometry. A conditional logistic regression method was used to evaluate the associations for serine, glycine, and related metabolites with pancreatic cancer risk with adjustment for potential confounders. Results: Odds ratios (95% confidence intervals) of pancreatic cancer for the highest quartile of serine and glycine were 0.33 (0.14−0.75) and 0.25 (0.11−0.58), respectively, compared with their respective lowest quartiles (both p’s < 0.01). No significant association with risk of pancreatic cancer was observed for other serine- or glycine related metabolites including cystathionine, cysteine, and sarcosine. Conclusion. The risk of pancreatic cancer was reduced by more than 70% in individuals with elevated levels of glycine and serine in serum collected, on average, more than 10 years prior to cancer diagnosis in a prospectively designed case-control study. These novel findings support a protective role of serine and glycine against the development of pancreatic cancer in humans that might have an implication for cancer prevention.

DNA damage, serum metabolomic alteration and carcinogenic risk associated with low-level air pollution

Outdoor air pollution has been classified as carcinogenic to humans (Group 1) for lung cancer, but the underlying mechanism and key toxic components remain incompletely understood. Since DNA damage and metabolite alterations are associated with cancer progression, exploring potential mechanisms linking air pollution and cancer might be meaningful. In this study, a real-time ambient air exposure system was established to simulate the real-world environment of adult male SD rats in Beijing from June 13th, 2018, to October 8th, 2018. 8-OHdG in the urine, γ-H2AX in the lungs and mtDNA copy number in the peripheral blood were analyzed to explore DNA damage at different levels. Serum non-targeted metabolomics analysis was performed. Pair-wise spearman was used to explore the correlation between DNA damage biomarkers and serum differential metabolites. Carcinogenic risks of heavy metals and PAHs via inhalation were assessed according to US EPA guidelines. Results showed that PM_2.5 and O₃ were the major air pollutants in the exposure group and not detected in the control group. Compared with control group, higher levels of 8-OHdG, mtDNA copy number, γ-H2AX and PCNA-positive nuclei cells were observed in the exposure group. Histopathological evaluation suggested ambient air induced alveolar wall thickening and inflammatory cell infiltration in lungs. Perturbed metabolic pathways identified included glycolysis/gluconeogenesis metabolism, purine and pyrimidine metabolism, etc. γ-H2AX was positively correlated with serum ADP, 3-phospho-D-glyceroyl phosphate and N-acetyl-D-glucosamine. The BaPeq was 0.120 ng/m³. Risks of Cr(VI), As, V, BaP, BaA and BbF were above 1 × 10^-6. We concluded that low-level air pollution was associated with DNA damage and serum metabolomic alterations in rats. Cr(VI) and BaP were identified as key carcinogenic components in PM_2.5. Our results provided experimental evidence for hazard identification and risk assessment of low-level air pollution.

Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy

Congenital heart defects (CHDs) are the most prevalent and serious of all birth defects in the United States. However, little is known about the impact of CHD-affected pregnancies on subsequent maternal health. Thus, there is a need to characterize the metabolic alterations associated with CHD-affected pregnancies. Fifty-six plasma samples were identified from post-partum women who participated in the National Birth Defects Prevention Study between 1997 and 2011 and had (1) unaffected control offspring (n = 18), (2) offspring with tetralogy of Fallot (ToF, n = 22), or (3) hypoplastic left heart syndrome (HLHS, n = 16) in this pilot study. Absolute concentrations of 408 metabolites using the AbsoluteIDQ^® p400 HR Kit (Biocrates) were evaluated among case and control mothers. Twenty-six samples were randomly selected from above as technical repeats. Analysis of covariance (ANCOVA) and logistic regression models were used to identify significant metabolites after controlling for the maternal age at delivery and body mass index. The receiver operating characteristic (ROC) curve and area-under-the-curve (AUC) are reported to evaluate the performance of significant metabolites. Overall, there were nine significant metabolites (p < 0.05) identified in HLHS case mothers and 30 significant metabolites in ToF case mothers. Statistically significant metabolites were further evaluated using ROC curve analyses with PC (34:1), two sphingolipids SM (31:1), SM (42:2), and PC-O (40:4) elevated in HLHS cases; while LPC (18:2), two triglycerides: TG (44:1), TG (46:2), and LPC (20:3) decreased in ToF; and cholesterol esters CE (22:6) were elevated among ToF case mothers. The metabolites identified in the study may have profound structural and functional implications involved in cellular signaling and suggest the need for postpartum dietary supplementation among women who gave birth to CHD offspring.

Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women

Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.

IDO1 Expression in Ovarian Cancer Induces PD-1 in T Cells via Aryl Hydrocarbon Receptor Activation

The immunoregulatory enzyme, indoleamine 2,3-dioxygenase (IDO1) and the PD-1/PD-L1 axis are potent mechanisms that impede effective anti-tumor immunity in ovarian cancer. However, whether the IDO pathway regulates PD-1 expression in T cells is currently unknown. Here we show that tumoral IDO1 expression led to profound changes in tryptophan, nicotinate/nicotinamide, and purine metabolic pathways in the ovarian tumor microenvironment, and to an increased frequency of PD-1+CD8+ tumor infiltrating T cells. We determined that activation of the aryl hydrocarbon receptor (AHR) by kynurenine induced PD-1 expression, and this effect was significantly abrogated by the AHR antagonist CH223191. Mechanistically, kynurenine alters chromatin accessibility in regulatory regions of T cell inhibitory receptors, allowing AHR to bind to consensus XRE motifs in the promoter region of PD-1. These results enable the design of strategies to target the IDO1 and AHR pathways for enhancing anti-tumor immunity in ovarian cancer.

Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis

METHODS

Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models.

RESULTS

We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models.

CONCLUSION

We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.

Metabolomic Detection Between Pancreatic Cancer and Liver Metastasis Nude Mouse Models Constructed by Using the PANC1-KAI1/CD82 Cell Line

Background: Pancreatic cancer (PC) has a poor prognosis and is prone to liver metastasis. The KAI1/CD₈₂ gene inhibits PC metastasis. This study aimed to explore differential metabolites and enrich the pathways in serum samples between PC and liver metastasis nude mouse models stably expressing KAI1/CD₈₂. Methods: KAI1/CD₈₂-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD₈₂ were constructed for the first time. This cell line was used to construct 3 PC nude mouse models and 3 liver metastasis nude mouse models. The different metabolites and Kyoto encyclopedia of genes and genomes (KEGG) and human metabolome database (HMDB) enrichment pathways were analyzed using the serum samples of the 2 groups of nude mouse models on the basis of untargeted ultra-performance liquid chromatography-tandem mass spectrometry platform. Results: KAI1/CD₈₂-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD₈₂ were constructed successfully, and all nude mouse models survived and developed cancers. Among the 1233 metabolites detected, 18 metabolites (9 upregulated and 9 downregulated) showed differences. In agreement with the literature data, the most significant differences between both groups were found in the levels of bile acids (taurocholic acid, chenodeoxycholic acid), glycine, prostaglandin E2, vitamin D, guanosine monophosphate, and inosine. Bile recreation, primary bile acid biosynthesis, and purine metabolism KEGG pathways and a series of HMDB pathways (P < .05) contained differential metabolites that may be associated with liver metastasis from PC. However, the importance of these metabolites on PC liver metastases remains to be elucidated. Conclusions: Our findings suggested that the metabolomic approach may be a useful method to detect potential biomarkers in PC.

Associations between metabolites and pancreatic cancer risk in a large prospective epidemiological study

OBJECTIVE

To assess whether prediagnostic metabolites were associated with incident pancreatic ductal adenocarcinoma (PDAC) in a prospective cohort study.

DESIGN

We conducted an untargeted analysis of 554 known metabolites measured in prediagnostic serum (up to 24 years) to determine their association with incident PDAC in a nested case-control study of male smokers (372 matched case-control sets) and an independent nested case-control study that included women and non-smokers (107 matched sets). Metabolites were measured using Orbitrap Elite or Q-Exactive high-resolution/accurate mass spectrometers. Controls were matched to cases by age, sex, race, date of blood draw, and follow-up time. We used conditional logistic regression adjusted for age to calculate ORs and 95% CIs for a 1 SD increase in log-metabolite level separately in each cohort and combined the two ORs using a fixed-effects meta-analysis.

RESULTS

Thirty-one metabolites were significantly associated with PDAC at a false discovery rate <0.05 with 12 metabolites below the Bonferroni-corrected threshold (p<9.04×10^-5). Similar associations were observed in both cohorts. The dipeptides glycylvaline, aspartylphenylalanine, pyroglutamylglycine, phenylalanylphenylalanine, phenylalanylleucine and tryptophylglutamate and amino acids aspartate and glutamate were positively while the dipeptides tyrosylglutamine and α-glutamyltyrosine, fibrinogen cleavage peptide DSGEGDFXAEGGGVR and glutathione-related amino acid cysteine-glutathione disulfide were inversely associated with PDAC after Bonferroni correction. Five top metabolites demonstrated significant time-varying associations (p<0.023) with the strongest associations observed 10-15 years after participants' blood collection and attenuated thereafter.

CONCLUSION

Our results suggest that prediagnostic metabolites related to subclinical disease, γ-glutamyl cycle metabolism and adiposity/insulin resistance are associated with PDAC.

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand.

METHOD

To comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI-) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites.

RESULTS

A total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity.

CONCLUSION

Bile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

The purpose of the present study was to compare metabolites from formalin-fixed and paraffin-embedded (FFPE) pancreatic tissue blocks with those identified in optimal cutting temperature (OCT)-embedded pancreatic tissue blocks. Thus, ultra-performance liquid chromatograph-mass spectrometry/mass spectrometry-based metabolic profiling was performed in paired frozen (n=13) and FFPE (n=13) human pancreatic adenocarcinoma tissue samples, in addition to their benign counterparts. A total of 206 metabolites were identified in both OCT-embedded and FFPE tissue samples. The method feasibility was confirmed through reproducibility and a consistency assessment. Partial least-squares discriminant analysis and heatmap analysis reliably distinguished tumor and normal tissue phenotypes. The expression of 10 compounds, including N-acetylaspartate and creatinine, was significantly different in both OCT-embedded and FFPE tumor samples. These ten compounds may be viable candidate biomarkers of malignant pancreatic tissues. The super-categories to which they belonged exhibited no significant differences between FFPE and OCT-embedded samples. Furthermore, purine, arginine and proline, and pyrimidine metabolism used a shared pathway found in both OCT-embedded and FFPE tissue samples. These results supported the notion that metabolomic data acquired from FFPE pancreatic cancer specimens are reliable for use in retrospective and clinical studies.

Serum PlGF and EGF are independent prognostic markers in non-metastatic colorectal cancer

The aim of this study was to determine the prognostic value of circulating angiogenic cytokines in non-metastatic colorectal cancer (CRC) patients. Preoperative serum samples of a training (TC) (n = 219) and a validation cohort (VC) (n = 168) were analyzed via ELISA to determine PlGF, EGF, VEGF, Ang1, PDGF-A, PDGF-B, IL-8 and bFGF levels. In addition, survival was correlated with PlGF and EGF expression measured by microarray and RNAseq in two publicly available, independent cohorts (n = 550 and n = 463, respectively). Prognostic values for overall (OS) and disease-free survival (DFS) were determined using uni- and multivariate Cox proportional hazard analyses. Elevated PlGF is predictive for impaired OS (TC: HR 1.056; p = 0.046; VC: HR 1.093; p = 0.001) and DFS (TC: HR 1.052; p = 0.029; VC: HR 1.091; p = 0.009). Conversely, elevated EGF is associated with favorable DFS (TC: HR 0.998; p = 0.045; VC: HR 0.998; p = 0.018) but not OS (TC: p = 0.201; VC: p = 0.453). None of the other angiogenic cytokines correlated with prognosis. The prognostic value of PlGF (OS + DFS) and EGF (DFS) was confirmed in both independent retrospective cohorts. Serum PlGF and EGF may serve as prognostic markers in non-metastatic CRC.