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An emerging view regarding cancer metabolism is that it is heterogeneous and context-specific, but it remains to be elucidated in breast cancers. In this study, we characterized the energy-related metabolic features of breast cancers through integrative analyses of multiple datasets with genomics, transcriptomics, metabolomics, and single-cell transcriptome profiling. Energy-related metabolic signatures were used to stratify breast tumors into two prognostic clusters: cluster 1 exhibits high glycolytic activity and decreased survival rate, and the signatures of cluster 2 are enriched in fatty acid oxidation and glutaminolysis. The intertumoral metabolic heterogeneity was reflected by the clustering among three independent large cohorts, and the complexity was further verified at the metabolite level. In addition, we found that the metabolic status of malignant cells rather than that of nonmalignant cells is the major contributor at the single-cell resolution, and its interactions with factors derived from the tumor microenvironment are unanticipated. Notably, among various immune cells and their clusters with distinguishable metabolic features, those with immunosuppressive function presented higher metabolic activities. Collectively, we uncovered the heterogeneity in energy metabolism using a classifier with prognostic and therapeutic value. Single-cell transcriptome profiling provided novel metabolic insights that could ultimately tailor therapeutic strategies based on patient- or cell type-specific cancer metabolism.

Metastasis and chemoresistance are two major causes of breast cancer death. We show here that the chemokine receptor CXCR2 was overexpressed in breast cancer cell lines and tissues. CXCR2 promoted anti-apoptosis, anti-senescence, and epithelial-to-mesenchymal transition (EMT) of breast cancer cells, leading to the enhanced metastasis and chemoresistance. Further study suggested that AKT1 and cyclooxygenase-2 (COX2; PTGS2) might mediate the CXCR2 signaling to inversely control the breast cancer metastasis and chemoresistance through the regulation of EMT, apoptosis, and senescence. Analyses of clinical data indicate that the high expression of CXCR2 was correlated with the high expression of COX2 and the low expression of AKT1, P85α, E-cadherin, and β-catenin in cancer tissues. Poor outcomes were associated with the high expression of CXCR2 or COX2 while favorable survivals were associated with the high expression of P85α, AKT1, or E-cadherin in all cancer patients. Cox multivariate analysis demonstrated that CXCR2, COX2, and AKT1 could be independent predictors for disease free survivals. All these data suggest that CXCR2 promotes breast cancer metastasis and chemoresistance via suppressing AKT1 and activating COX2. Thus, antagonists of the CXCR2 signaling molecules may be used to treat breast cancer patients particularly with high metastasis and chemoresistance.

Background An accurate model to stratify breast cancer risk is required to move from a one-size-fits-all screening paradigm to more personalised risk-based screening strategies. Most risk models have limited accuracy and have been calibrated for white women. Considering the rapid increase in breast cancer incidence in China, in this study we aimed to develop a breast cancer model for Chinese women. Methods We recruited women from Fudan University Shanghai Cancer Centre. Information about their height, weight, body-mass index, age at menarche, age at first delivery, age at menopause, menopause status, parity history, number of children, breastfeeding history, personal history of breast cancer, family history of breast cancer, and degree of consanguinity was obtained. Mammograms were acquired from all recruited women. We used AutoDensity software to segment the mammographic dense area. To ensure that the breast density had not been affected by presence of the lesion, we measured the mammographic breast density of patients with cancer from the cancer-free breast contralateral to the biopsy-proven cancer. The breast area, dense area, percentage density, and demographic variables were input into an ensemble of 50 decision trees, the results of which we combined using a hybrid sampling and boosting algorithm (RUSBoost). The variables were also fed into the logistic and stepwise logistic regression models, which are two commonly used linear models. Leave-one-out cross-validation was used to assess the performance of the models and the receiver operating characteristics (ROC) curves were generated for each model. Findings In total, 1079 women (85 women with biopsy-proven breast cancer and 994 women without breast cancer) were recruited. The classifier had an area under ROC curve (AUC) of 0·86 (95% CI 0·82–0·89) for classifying the cases as normal or high-risk. It outperformed the accuracy of traditionally employed logistic and stepwise logistic regression models, which resulted in AUC values of 0·739 (0·67–0·81) and 0·757 (0·69–0·81), respectively. Interpretation When compared with linear models, use of a machine-learning approach, which models data non-linearity, could lead to improvement in the accuracy of a breast cancer risk stratification model. Such a model can be used to recommend intensified surveillance for women at a high risk of future breast cancer. Funding None.