Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer.

Background: Luminal A tumours generally have a favourable prognosis but possess the highest 10‐year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post‐diagnosis. Identifying such patients is crucial as long‐term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.Methods: We conducted a study to explore non‐structural chromosome maintenance condensin I complex subunit H’s (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.Results: We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)‐NCAPH^ErbB2double‐transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10‐gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.Conclusions: The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.

Breast cancer that arises in the postpartum period often carries a poor prognosis and urgently requires chemoprevention strategies, particularly for women at elevated risk. Here, we report that a single post-lactation dose of cabergoline, a dopaminergic agonist that blocks prolactin secretion, delays the onset and reduces the incidence of postpartum mammary cancer in K14-Cre; Brca1F/F, Trp53F/F mice. In these mice, cabergoline treatment remodels the mammary gland by potentiating involution, reducing ductal area and proliferation, and reversing T cell exhaustion, potentially through modulation of ion channel expression. Notably, independent retrospective studies in two large cohorts of European women showed that cabergoline treatment was associated with a lower incidence of postpartum breast cancer, with a meta-analysis indicating an approximate 69% reduction in risk. Our work underscores the importance of targeting postlactational involution as a strategy for the prevention of postpartum breast cancer and positions cabergoline as a novel, low-risk chemoprevention strategy.

It is imperative to identify patients with prostate cancer (PCa) who will not benefit from androgen receptor signaling inhibitors and to improve their clinical outcomes. Using artificial intelligence (AI), in this multicenter cohort study of 623 PCa patients, we identified 13 cellular morphometric biomarkers (CMBs), as a New Approach Methodology (NAM), from whole slide images of needle biopsies in clinical trial specimens (NCT02430480, n = 37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) plus enzalutamide (AUC: 0.981, 95% CI [0.979, 0.983]). Importantly, the 13-CMB model stratified PCa patients into responders and non-responders after NADT across two independent hospital cohorts. In one cohort (n = 122), the model identified groups with significantly different pathologic complete response (pCR) (p = 0.0005) and biochemical recurrence-free survival (BCRFS) (p = 0.024). In the second cohort (n = 60), the model similarly distinguished patients with significantly different BCRFS (p = 0.031). The 13-CMB model also stratified PCa patients in the TCGA-PRAD cohort (n = 396) with distinct progression-free survival (p = 0.0017). Importantly, across hospital cohorts and the TCGA-PRAD cohort, the 13-CMB model demonstrated significant and independent clinical value after adjustment for established clinical factors and commonly used genomic biomarkers, including Decipher and Oncotype DX. Furthermore, CMBs accurately predicted the molecular differences between stratified patient groups and the potential benefit from mTOR inhibitors in non-responders, which were validated through IHC staining and patient-derived organoids (n = 8), respectively. Overall, our AI-powered CMB model, relying only on routine needle biopsy specimens, could potentially serve as a robust solution for precision management of PCa patients.