Analysis of metabolites and metabolic pathways in breast cancer in a Korean prospective cohort: the Korean Cancer Prevention Study-II

Metabolomics-enabled biomarker discovery in breast cancer research

Breast cancer (BC) remains the most prevalent malignancy among women worldwide. While genetic predisposition and reproductive history are key contributors to its development, modifiable risk factors are also important, particularly those linked to lifestyle behaviors, often influencing the endogenous metabolome. Over the past decade, mass spectrometry-based metabolomics has enabled agnostic investigations into correlations between the metabolome and BC risk. Here we review recent results from prospective nested case-control studies, which have led to the identification of significantly different metabolites between women who subsequently developed BC and those who did not. As replication of these findings remains limited, we emphasize the need for robust quantitative validation studies, cancer subtype-specific analyses in diverse populations, and expanded chemical space coverage of analytical assays.

Application of Metabolic Biomarkers in Breast Cancer: A Literature Review

Breast cancer is the most common cancer and the second leading cause of cancer death in women worldwide. Novel biomarkers for early diagnosis, treatment, and prognosis in breast cancer are needed and extensively studied. Metabolites, which are small molecules produced during metabolic processes, provide links between genetics, environment, and phenotype, making them useful biomarkers for diagnosis, prognosis, and disease classification. With recent advancements in metabolomics techniques, metabolomics research has expanded, which has led to significant progress in biomarker research. In breast cancer, alterations in metabolic pathways result in distinct metabolomic profiles that can be harnessed for biomarker discovery. Studies using mass spectrometry and nuclear magnetic resonance spectroscopy have helped identify significant changes in metabolites, such as amino acids, lipids, and organic acids, in the tissues, blood, and urine of patients with breast cancer, highlighting their potential as biomarkers. Integrative analysis of these metabolite biomarkers with existing clinical parameters is expected to improve the accuracy of breast cancer diagnosis and to be helpful in predicting prognosis and treatment responses. However, to apply these findings in clinical practice, larger cohorts for validation and standardized analytical methods for QC are necessary. In this review, we provide information on the current state of metabolite biomarker research in breast cancer, highlighting key findings and their clinical implications.

Plasma metabolomics profiles and breast cancer risk

BACKGROUND

Breast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors.

METHODS

We conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features.

RESULTS

We found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%.

CONCLUSIONS

If replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.

Untargeted serum metabolomic profiles and breast density in young women

PURPOSE OF THE STUDY

Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women.

METHODS

In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q).

RESULTS

The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV.

CONCLUSION

Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention

PURPOSE

To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology.

METHODS

We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context.

RESULTS

Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods.

CONCLUSION

Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.

Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study

Background

The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.

Methods

Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids (acylcarnitines, glycerides, phosphatidylcholines, sphingolipids, and cholesteryl esters).

Results

Age was a major confounding factor responsible for a large heterogeneity in the dataset, hence age-defined subgroups were analyzed separately. The largest number of metabolites whose serum levels differentiated BC cases and controls (82 compounds) were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women.

Conclusions

Changes in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner.

Childhood adiposity, serum metabolites and breast density in young women

BACKGROUND

Childhood adiposity is inversely associated with young adult percent dense breast volume (%DBV) and absolute dense breast volume (ADBV), which could contribute to its protective effect for breast cancer later in life. The objective of this study was to identify metabolites in childhood serum that may mediate the inverse association between childhood adiposity and young adult breast density.

METHODS

Longitudinal data from 182 female participants in the Dietary Intervention Study in Children (DISC) and the DISC 2006 (DISC06) Follow-Up Study were analyzed. Childhood adiposity was assessed by anthropometry at the DISC visit with serum available that occurred closest to menarche and expressed as a body mass index (BMI) z-score. Serum metabolites were measured by untargeted metabolomics using ultra-high-performance liquid chromatography-tandem mass spectrometry. %DBV and ADBV were measured by magnetic resonance imaging at the DISC06 visit when participants were 25-29 years old. Robust mixed effects linear regression was used to identify serum metabolites associated with childhood BMI z-scores and breast density, and the R package mediation was used to quantify mediation.

RESULTS

Of the 115 metabolites associated with BMI z-scores (FDR < 0.20), 4 were significantly associated with %DBV and 6 with ADBV before, though not after, adjustment for multiple comparisons. Mediation analysis identified 2 unnamed metabolites, X-16576 and X-24588, as potential mediators of the inverse association between childhood adiposity and dense breast volume. X-16576 mediated 14% (95% confidence interval (CI) = 0.002, 0.46; P = 0.04) of the association of childhood adiposity with %DBV and 11% (95% CI = 0.01, 0.26; P = 0.02) of its association with ADBV. X-24588 also mediated 7% (95% CI = 0.001, 0.18; P = 0.05) of the association of childhood adiposity with ADBV. None of the other metabolites examined contributed to mediation of the childhood adiposity-%DBV association, though there was some support for contributions of lysine, valine and 7-methylguanine to mediation of the inverse association of childhood adiposity with ADBV.

CONCLUSIONS

Additional large longitudinal studies are needed to identify metabolites and other biomarkers that mediate the inverse association of childhood adiposity with breast density and possibly breast cancer risk.

High serum levels of L-carnitine and citric acid negatively correlated with alkaline phosphatase are detectable in Koreans before gastric cancer onset

INTRODUCTION

Monitoring metabolic biomarkers could be utilized as an effective tool for the early detection of gastric cancer (GC) risk.

OBJECTIVE

We aimed to discover predictive serum biomarkers for GC and investigate biomarker-related metabolism.

METHODS

Subjects were randomly selected from the Korean Cancer Prevention Study-II cohort and matched by age and sex. We analyzed baseline serum samples of 160 subjects (discovery set; control and GC occurrence group, 80 each) via nontargeted screening. Identified putative biomarkers were validated in baseline serum samples of 140 subjects (validation set; control and GC occurrence group, 70 each) using targeted metabolites analysis.

RESULTS

The final analysis was conducted on the discovery set (control, n = 52 vs. GC occurrence, n = 50) and the validation set (control, n = 43 vs. GC occurrence, n = 44) applying exclusion conditions. Eighteen putative metabolite sets differed between two groups found on nontargeted metabolic screening. We focused on fatty acid-related energy metabolism. In targeted analysis, levels of decanoyl-L-carnitine (p = 0.019), L-carnitine (p = 0.033), and citric acid (p = 0.025) were significantly lower in the GC occurrence group, even after adjusting for age, sex, and smoking status. Additionally, L-carnitine and citric acid were confirmed to have an independently significant relationship to GC development. Notably, alkaline phosphatase showed a significant correlation with these two biomarkers.

CONCLUSION

Changes in serum L-carnitine and citric acid levels that may result from alterations of fatty-acid-related energy metabolism are expected to be valuable biomarkers for the early diagnosis of GC risk.

Circulating Carnitine Levels and Breast Cancer: A Matched Retrospective Case-Control Study

Introduction

Epidemiological studies investigating the association between carnitine and breast cancer are scarce.

Materials and Methods

This 1:1 age-matched retrospective case-control study identified 991 female breast cancer cases and 991 female controls without breast cancer using pathological testing. We used targeted metabolomics technology to measure 16 types of whole blood carnitine compounds, such as free carnitine (C0) and octadecanoylcarnitine (C18).

Results

The average age for cases and controls was approximately 50 ± 8.7 years. After adjusting for covariates, each standard deviation (SD) increase in malonylcarnitine (C3DC; OR 0.91; 95% CI 0.83-1.00), decenoylcarnitine (C10:1; OR 0.87; 95% CI 0.79-0.96), and decadienoylcarnitine (C10:2; OR 0.90; 95% CI 0.82-0.99) level was associated with decreased odds of breast cancer. However, higher butyrylcarnitine (C4) levels were associated with increased odds of breast cancer (OR 1.12; 95% CI 1.02-1.23). No statistically significant relationship was noted between other carnitine compounds and breast cancer. The false discovery rates for C3DC, C4, C10:1 and C10:2 were 0.172, 0.120, 0.064 and 0.139, respectively.

Conclusions

Higher levels of C3DC, C10:1, and C10:2 were protective factors for breast cancer, whereas increased C4 levels were a risk factor for the disease.

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.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Liver Cirrhosis Patients Who Had Normal Liver Function Before Liver Cirrhosis Development Have the Altered Metabolic Profiles Before the Disease Occurrence Compared to Healthy Controls

Liver cirrhosis (LC) is the final usual outcome of liver damage induced by various chronic liver diseases. Because of asymptomatic nature of LC, it is usually diagnosed at late and advanced stages, and patients are easy to miss the best timing for treatment. Thus, the early detection of LC is needed. In the prospective Korean Cancer Prevention Study-II (K-II), we aimed to identify valuable biomarkers for LC using metabolomics to distinguish subjects with incident LC (LC group) from subjects free from LC (control group) during a mean 7-year follow-up period. Metabolic alterations were investigated using baseline serum specimens acquired from 94 subjects with incident LC and 180 age- and sex-matched LC-free subjects via ultra-performance liquid chromatography (UPLC)-linear-trap quardrupole (LTQ)-Orbitrap mass spectrometry (MS). As a result of the metabolic analysis, 46 metabolites were identified. Among them, 11 and 18 metabolite level showed a significant increase and decrease, respectively, in the LC group compared to the control group. Nine metabolic pathways, including glyoxylate and dicarboxylate metabolism, amino acid metabolism, fatty acid metabolism, linoleic acid metabolism, α-linolenic acid metabolism, and arachidonic acid metabolism, were significantly different between the two groups. Logistic regression demonstrated that the LC emergence was independently affected by serum levels of myristic acid, palmitic acid, linoleic acid, eicosapentaenoic acid (EPA), lysophosphatidic acid (LPA) (18:1), glycolic acid, lysophosphatidylcholine (lysoPC) (22:6), and succinylacetone (R 2 = 0.837, P < 0.001). This prospective study revealed that dysregulation of various metabolism had the clinical relevance on the LC development. Moreover, myristic acid, palmitic acid, linoleic acid, EPA, LPA (18:1), glycolic acid, lysoPC (22:6), and succinylacetone were emerged as independent variables influencing the incidence of LC. The results support that the early biomarkers found in this study may useful for predicting and remedying the risk of LC.

Mass Spectrometry-Based Non-targeted Metabolic Profiling for Disease Detection: Recent Developments

Mass spectrometry (MS) plays an important role in seeking biomarkers for disease detection. High-quality quantitative data is needed for accurate analysis of metabolic perturbations in patients. This article describes recent developments in MS-based non-targeted metabolomics research with applications to the detection of several major common human diseases, focusing on study cohorts, MS platforms utilized, statistical analyses and discriminant metabolite identification. Potential disease biomarkers recently discovered for type 2 diabetes, cardiovascular disease, hepatocellular carcinoma, breast cancer and prostate cancer through metabolomics are summarized, and limitations are discussed.