Molecular subtyping of breast cancer may provide additional prognostic information regarding patient outcome. However, its clinical significance remains to be established. In this study, the main aims were to discover whether reclassification of breast cancer into molecular subtypes provides more precise information regarding outcome compared to conventional histopathological grading and to study breast cancer-specific survival in the different molecular subtypes. Cases of breast cancer occurring in a cohort of women born between 1886 and 1928 with long-term follow-up were included in the study. Tissue microarrays were constructed from archival formalin-fixed, paraffin-embedded tissue from 909 cases. Using immunohistochemistry and in situ hybridisation as surrogates for gene expression analyses, all cases were reclassified into the following molecular subtypes: Luminal A; Luminal B (HER2−); Luminal B (HER2+); HER2 subtype; Basal phenotype; and five negative phenotype. Kaplan–Meier survival curves and Cox proportional hazards models were used in the analyses. During the first 5 years after diagnosis, there were significant differences in prognosis according to molecular subtypes with the best survival for the Luminal A subtype and the worst for HER2 and five negative phenotype. In this historic cohort of women with breast cancer, differences in breast cancer-specific survival according to subtype occur almost exclusively amongst the histopathological grade 2 tumours. From 5 years after time of diagnosis until the end of follow-up, there appears to be no difference in survival according to molecular subtype or histopathological grade.

BackgroundThe 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 ERpos/PgRpos/HER-2pos status which for the sake of simplicity is called triple positive breast cancer (TPBC).MethodsThe 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.ResultsCholine 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-2pos tumors, which support Glycine as potential marker for tumor aggressiveness.ConclusionsMetabolic 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.

We examine the properties of several tests for goodness-of-fit for multinomial logistic regression. One test is based on a strategy of sorting the observations according to the complement of the estimated probability for the reference outcome category and then grouping the subjects into g equal-sized groups. A g x c contingency table, where c is the number of values of the outcome variable, is constructed. The test statistic, denoted as Cg, is obtained by calculating the Pearson chi2 statistic where the estimated expected frequencies are the sum of the model-based estimated logistic probabilities. Simulations compare the properties of Cg with those of the ungrouped Pearson chi2 test (X2) and its normalized test (z). The null distribution of Cg is well approximated by the chi2 distribution with (g-2) x (c-1) degrees of freedom. The sampling distribution of X2 is compared with a chi2 distribution with n x (c-1) degrees of freedom but shows erratic behavior. With a few exceptions, the sampling distribution of z adheres reasonably well to the standard normal distribution. Power simulations show that Cg has low power for a sample of 100 observations, but satisfactory power for a sample of 400. The tests are illustrated using data from a study of cytological criteria for the diagnosis of breast tumors.

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.

BackgroundToday'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.MethodsPatients (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.ResultsAll 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.ConclusionsThe 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.