Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry

Metabolomics is the systematic survey of the small molecules (< 1 k Dalton in size) that are the products of metabolism in biological systems [1, 2]. A metabolic phenotype represents the collection of metabolites within the body which reflects influences from both genetic and lifestyle/environmental factors. Because metabolites include the intermediate- and end-products of the cellular processes, metabolomics provides a functional readout of the physiological state of health and disease. Changes in energy metabolism within cells are one of the hallmarks of carcinogenesis. Under aerobic conditions, normal cells metabolize energy by first converting glucose into pyruvate and then to carbon dioxide, and under anaerobic conditions, cells metabolize by glycolysis. However, the converse is true for cancer cells, where under aerobic conditions, energy metabolism occurs largely by glycolysis, i.e., “aerobic glycolysis” [3]. Thus, a characterization of metabolic processes may provide new insights into carcinogenesis. In recent years, metabolomics has emerged as an important tool for the identification of biomarkers in a growing number of applications, including early disease detection, monitoring of disease progression, and investigation of metabolic pathways. The application of metabolomics has yielded novel signatures predicting the occurrence and progression of complex diseases, including cancers of the breast [4], prostate, colon, and kidney [5‐8].

Most metabolomics studies of breast cancer to date have been conducted in tumor tissues or cell lines and with the goals of distinguishing cancer from normal tissue and cancers with metastasis from those without, as well as identifying therapeutic targets [9‐11]. Data from these studies have suggested that metabolomic profiles may differ by pathological and molecular subtype of breast cancer. In large scale epidemiologic studies, blood and urine are more readily available than tissue. Because blood and urine serve as transporters of nutrients and wastes to and from cells for excretion, and maintain homeostasis of essential molecules and fluid levels, they are sensitive indicators of health and perturbations from diseases. Several studies have measured urinary metabolic profiles and found promising candidate markers for early detection and monitoring of breast cancer progression [12‐14]. However, studies using pre-diagnostic blood are limited. To our knowledge, there has been only one previously published study on metabolomics and breast cancer risk using pre-diagnostic blood [4], warranting additional studies to replicate the findings in other populations. We conducted a pilot study to generate preliminary data to assess whether circulating metabolomic profiles could be detected in pre-diagnostic plasma samples of women enrolled in the Breast Cancer Family Registry (BCFR) cohort, and to evaluate the reproducibility of metabolomic assays.

Of the 45 cases selected, 31 had two or more affected first-degree female relatives, while 14 cases had one affected first-degree female relative. Six cases were BRCA1 mutation carriers, while four were BRCA2 mutation carriers. Twenty-one cases were premenopausal at blood collection, and the remainder were postmenopausal. The average age at breast cancer diagnosis was 58.8 years. The average age at blood draw was 52.4 years for cases, compared to 53.1 years for controls. Approximately 51% of cases were ER positive, and 22% were ER negative. About 20% of cases had localized tumors, and 5% had regional involvement limited to the nodes (Table 1).

We detected a total of 661 known named metabolites in our samples. Of these, 338 (51%) were detected in all the samples, and 490 (74%) were detected in greater than 80% of the 90 study samples. These metabolites include amino acids and lipids, and some related to microbiome influences and xenobiotics metabolism (Additional file 1: Table S1). The average CV across all named metabolites was 0.16 (25th – 75th percentile: 0.06–0.20) (Table 2). The median ICC between duplicates was 0.96 (25th – 75th percentile: 0.82–0.99). The average variance was 60.7% among individuals, and 6.0% for duplicate samples within individuals.

Principal component analysis identified the top 3 components of all samples. The scores of the components identified were very similar for duplicate samples. (Fig. 1).

We observed a greater than 20% case-control difference in 24 metabolites that were statistically significant (p < 0.05). Metabolites including 3-(cystein-S-yl)acetaminophen (xenobiotics pathway), 4-acetylphenol sulfate (xenobiotics pathway), and cysteine s-sulfate (amino acid pathway) were significantly higher in cases, whereas indoleacetylglutamine, (amino acid pathway), 2-ethylphenylsulfate (xenobiotics pathway), and sphingosine (lipid pathway) were significantly higher in controls (Fig. 2).

Among the metabolites that showed a greater than 20% case-control difference, we also examined differences among hypothesized predictors of breast cancer risk, including differences by age at blood draw, the number of affected first-degree relatives (Table 3), ER status, and PR status (Table 4). Statistically significant (p < 0.05) but modest associations were observed between some metabolites and age at blood draw; for example, age explained 13% of the variation in 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1) (adjusted r² = 0.13; p < 0.01).

In some metabolites, we found differences by ER status. For example, cases with ER+ breast cancer had higher mean laurylcarnitine level than those with ER- breast cancer (1.16 vs. 0.83, p = 0.04). Conversely, the mean indoleacetyleglutamine level was lower for ER+ breast cancer cases than ER- cases (0.46 vs. 0.54, p = 0.04).

These data, despite small numbers, suggest that a large number of metabolites have detectable levels, with good reproducibility, as suggested by high ICCs and reasonable CVs. We also showed that for most metabolites, the within-person variance is small, while the between-person variance is much larger. We found that some metabolites have a greater than 20% case-control difference. Finally, we showed that some metabolites (including N-(2-furoyl)glycine in the xenobiotics pathway) differed by key breast cancer risk factors such as the number of affected family members, although these associations were not significant after adjusting for multiple comparisons, likely due to the small sample size. Taken together, these results suggest that a large-scale study (~ 1000) would be well-powered to detect meaningful biological and statistically significant differences between cases and controls to provide a better understanding of breast cancer etiology across a wide spectrum of risks, and among high-risk women in particular.

Metabolomics profiles are becoming increasingly utilized in epidemiological studies to predict the risk of chronic diseases, including breast cancer; however, data from prospective studies are limited. In the first prospective study of metabolomics and breast cancer risk, Kuhn et al. [4] found that phosphatidylcholines were associated with breast cancer risk. That study included 362 sporadic breast cancer cases, and measured only 120 metabolites. To date, there are no metabolomics data on women at increased risk of breast cancer due to their family history of breast cancer. Clearly additional data from prospective studies are needed to further examine the role of metabolomics in breast carcinogenesis.

The assay performance on our samples, measured by ICCs and CVs, is consistent with earlier studies that have examined the utility of metabolomics in epidemiological research among participants of the Shanghai Physical Activity Study [21]. In that study, the variability in a large subset of metabolites was assessed and the intraclass correlation was high (median 0.8). Similar assay performance was also observed in a nested case-control study of metabolomics and colorectal cancer risk [22] that included 254 cases and 254 matched controls from the Prostate, Lung, Colorectal and Ovarian Cancer study. In that study, which used a metabolomics platform similar to the one used in our pilot study, the median intraclass correlation was 0.86 (25th–75th percentile: 0.64–0.92).

Consistent with our observation that age at blood collection was associated with metabolite levels, Saito et al. [23] also reported that certain metabolites were associated with age at blood draw in a Japanese population. Because the populations in the two studies are quite different, a direct comparison is not possible. Similarly, Tang et al. [24] reported that metabolites in tumor tissues were associated with ER status, and also with BRCA1-associated tumors. However, studies utilizing human plasma are limited.

One notable limitation of our study is that we did not match on the duration of storage time between cases and controls. Post-hoc analysis revealed that among case-control pairs, 35 pairs (78%) had a difference of less than 3 years of storage duration, while 1 pair had a difference of more than 10 years. Further analyses showed that while there were no appreciable differences in the analyses by age, there were differences in 5 metabolites when evaluating levels by the number of affected relatives. Future studies should match on calendar year of blood draw (hence storage duration) within case-control matched sets.

Our study is among the first to examine the association between metabolomics and breast cancer risk using pre-diagnostic plasma samples. Despite the limited sample size, we were able to find a larger than 20% case-control difference in several metabolites, although we cannot rule out the possibility that the presence of asymptomatic preclinical breast cancer may have affected metabolite levels in cases. Such bias is possible but should be minimal as we excluded cases diagnosed with breast cancer within 12 months after the blood draw in order to limit the potential for preclinical disease to influence metabolite levels. Finally, our study is also among the first to examine reliability across more than 600 metabolites.

In conclusion, findings from this study suggest that metabolomics can be used reliably in large-scale epidemiologic studies of breast cancer to detect meaningful differences in risk.