Historical Biobanks in Breast Cancer Metabolomics- Challenges and Opportunities.

Background: Metabolomic characterization of tumours can potentially improve prediction of cancer prognosis and treatment response. Here, we describe efforts to validate previous metabolomic findings using a historical cohort of breast cancer patients and discuss challenges with using older biobanks collected with non-standardized sampling procedures. Methods: In total, 100 primary breast cancer samples were analysed by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and subsequently examined by histology. Metabolomic profiles were related to the presence of cancer tissue, hormone receptor status, T-stage, N-stage, and survival. RNA integrity number (RIN) and metabolomic profiles were compared with an ongoing breast cancer biobank. Results: The 100 samples had a median RIN of 4.3, while the ongoing biobank had a significantly higher median RIN of 6.3 (p = 5.86 × 10-7). A low RIN was associated with changes in choline-containing metabolites and creatine, and the samples in the older biobank showed metabolic differences previously associated with tissue degradation. The association between metabolomic profile and oestrogen receptor status was in accordance with previous findings, however, with a lower classification accuracy. Conclusions: Our findings highlight the importance of standardized biobanking procedures in breast cancer metabolomics studies.

High-resolution magic-angle-spinning NMR spectroscopy of intact tissue.

High-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy is a nondestructive technique that is used to obtain the metabolite profile of a tissue sample. This method requires minimal sample preparation. However, it is important to handle the sample with care and keep it frozen during preparation to minimize degradation. Here, we describe a typical protocol for HR-MAS analysis of intact tissue. We also include examples of typical pulse sequence programs and quantification methods that are used today.

Metabolic characterization of triple negative breast cancer.

BACKGROUND: The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ER(pos)/PgR(pos)/HER-2(pos) status which for the sake of simplicity is called triple positive breast cancer (TPBC). METHODS: The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data. RESULTS: Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or "Glutamine addiction" has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2(pos) tumors, which support Glycine as potential marker for tumor aggressiveness. CONCLUSIONS: Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes.

Phospholipase D1 and choline kinase-α are interactive targets in breast cancer.

A consistent metabolic hallmark observed in multiple cancers is the increase of cellular phosphocholine (PC) and total choline-containing compounds (tCho), which is closely related to malignant transformation, invasion, and metastasis. Enzymes in choline phospholipid metabolism present attractive targets to exploit for treatment, but require a clear understanding of the mechanisms underlying the altered choline phospholipid metabolism observed in cancer. Choline kinase-α (Chk-α) is an enzyme in the Kennedy pathway that phosphorylates free choline (Cho) to PC, and its upregulation in several cancers is a major contributor to increased PC levels. Similarly, increased expression and activity of phospholipase D1 (PLD1), which converts phosphatidylcholine (PtdCho) to phosphatidic acid (PA) and Cho, has been well documented in gastric, ovarian and breast cancer. Here we report a strong correlation between expression of Chk-α and PLD1 with breast cancer malignancy. Data from patient samples established an association between estrogen receptor (ER) status and Chk-α and PLD1 expression. In addition, these two enzymes were found to be interactive. Downregulation of Chk-α with siRNA increased PLD1 expression, and downregulation of PLD1 increased Chk-α expression. Simultaneous silencing of PLD1 and Chk-α in MDA-MB-231 cells increased apoptosis as detected by the TUNEL assay. These data provide new insights into choline phospholipid metabolism of breast cancer, and support multiple targeting of enzymes in choline phospholipid metabolism as a strategy for treatment.

Prognostic value of metabolic response in breast cancer patients receiving neoadjuvant chemotherapy.

BACKGROUND: Today's clinical diagnostic tools are insufficient for giving accurate prognosis to breast cancer patients. The aim of our study was to examine the tumor metabolic changes in patients with locally advanced breast cancer caused by neoadjuvant chemotherapy (NAC), relating these changes to clinical treatment response and long-term survival. METHODS: Patients (n = 89) participating in a randomized open-label multicenter study were allocated to receive either NAC as epirubicin or paclitaxel monotherapy. Biopsies were excised pre- and post-treatment, and analyzed by high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). The metabolite profiles were examined by paired and unpaired multivariate methods and findings of important metabolites were confirmed by spectral integration of the metabolite peaks. RESULTS: All patients had a significant metabolic response to NAC, and pre- and post-treatment spectra could be discriminated with 87.9%/68.9% classification accuracy by paired/unpaired partial least squares discriminant analysis (PLS-DA) (p < 0.001). Similar metabolic responses were observed for the two chemotherapeutic agents. The metabolic responses were related to patient outcome. Non-survivors (< 5 years) had increased tumor levels of lactate (p = 0.004) after treatment, while survivors (≥ 5 years) experienced a decrease in the levels of glycine (p = 0.047) and choline-containing compounds (p ≤ 0.013) and an increase in glucose (p = 0.002) levels. The metabolic responses were not related to clinical treatment response. CONCLUSIONS: The differences in tumor metabolic response to NAC were associated with breast cancer survival, but not to clinical response. Monitoring metabolic responses to NAC by HR MAS MRS may provide information about tumor biology related to individual prognosis.

Glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5) expression correlates with malignant choline phospholipid metabolite profiles in human breast cancer.

Altered choline phospholipid metabolism is a hallmark of cancer, leading to malignant choline metabolite profiles consisting of low glycerophosphocholine (GPC) and high phosphocholine (PC) in human breast cancers. Glycerophosphocholine phosphodiesterase (GPC-PDE) catalyzes the degradation of GPC to free choline and glycerol-3-phosphate. The gene(s) encoding for the GPC-PDE(s) responsible for GPC degradation in breast cancers have not yet been identified. Here, we demonstrate for the first time that the GPC-PDE encoded by glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5) is associated with breast cancer malignancy. Two human breast cancer cell lines (n = 8 and n = 10) and primary human breast tumor samples (n = 19) were studied with combined MRS and quantitative reverse transcription-polymerase chain reaction to investigate several isoforms of GDPD expression with respect to choline phospholipid metabolite levels. Of the five GDPDs tested, GDPD5 was found to be significantly overexpressed in highly malignant estrogen receptor negative (ER(-)) compared with weakly malignant estrogen receptor positive (ER(+)) human breast cancer cells (p = 0.027) and breast tumors from patients (p = 0.015). GDPD5 showed significantly positive correlations with PC (p < 0.001), total choline (tCho) (p = 0.007) and PC/GPC (p < 0.001) levels in human breast tumors. GDPD5 showed a trend towards a negative correlation with GPC levels (p = 0.130). Human breast cancers with malignant choline metabolite profiles consisting of low GPC and high PC levels highly co-expressed GDPD5, choline kinase alpha (CHKA) and phosphatidylcholine-specific phospholipase D1 (PLD1), whereas cancers containing high GPC and relatively low PC levels displayed low co-expression of GDPD5, CHKA and PLD1. GDPD5, CHKA and PLD1 were significantly overexpressed in highly malignant ER(-) tumors in our patient cohort. Our study identified GDPD5 as a GPC-PDE that probably participates in the regulation of choline phospholipid metabolism in breast cancer, which possibly occurs in cooperation with CHKA and PLD1.

Predicting long-term survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling.

PURPOSE: This study aimed to evaluate whether MR metabolic profiling can be used for prediction of long-term survival and monitoring of treatment response in locally advanced breast cancer patients during neoadjuvant chemotherapy (NAC). METHODS: High resolution magic angle spinning (HR MAS) MR spectra of pre- and post-treatment biopsies from 33 patients were acquired. Tissue concentrations of choline-containing metabolites (tCho), glycine and taurine were assessed using electronic reference to access in vivo concentration (ERETIC) of the signal and receiver operating characteristic (ROC) curves was used to define their potential to predict patient survival and treatment response. The metabolite profiles obtained by HR MAS spectroscopy were related to long-term survival and treatment response by genetic algorithm partial least squares discriminant analysis (GA PLS-DA). RESULTS: Different pre-treatment MR metabolic profiles characterized by higher levels of tCho and lower levels of lactate were observed in patients with long-term survival (≥5 years, survivors) compared to patients who died of cancer recurrence (<5 years, non-survivors). A significant decrease in glycerophosphocholine (GPC) post-treatment was associated with long-term survival (p = 0.046) and partial response (p = 0.014) to NAC. Long-term survival was best predicted by GPC using ROC analyses (sens. 66.7%, spec. 62.5%), while taurine had the best predictive value of treatment response (sens. 72.7%, spec. 63.2%). GA PLS-DA multivariate classification models successfully discriminated between survivors and non-survivors, resulting in 82.7% and 90.2% cross-validation (CV) classification accuracy, pre- and post-treatment, respectively. Classification of treatment response using GA PLS-DA was not successful for this patient cohort. CONCLUSIONS: Our results demonstrate that HR MAS MR metabolic profiles consisting of important metabolic characteristics of breast cancer tumors could potentially assist the classification and prediction of long-term survival in locally advanced breast cancer patients, in addition to being used as an adjunct for evaluation of treatment response to NAC.