Metabolic adaptation towards glycolysis supports resistance to neoadjuvant chemotherapy in early triple negative breast cancers

Jacaric Acid Empowers RSL3-Induced Ferroptotic Cell Death in Two- and Three-Dimensional Breast Cancer Cell Models

Ferroptosis has recently emerged as a promising strategy to combat therapy-resistant cancers. As lipid peroxidation is a key trigger of ferroptotic cell death, enhancing cancer cell susceptibility through the supply of highly peroxidisable fatty acids represents a novel therapeutic approach. Conjugated linolenic acids (CLnAs) fulfill this requirement, exhibiting a peroxidation propagation rate eight times higher than their non-conjugated counterpart, α-linolenic acid. This study evaluates jacaric acid (JA), a plant-derived CLnA, as a ferroptotic inducer, both as a monotherapy and in combination with RAS-selective lethal 3 (RSL3), a canonical ferroptosis inducer, in 2D and 3D breast cancer cell models. JA treatment significantly reduced cell viability across all models, primarily through lipid peroxidation driven by JA incorporation into cellular lipids rather than alterations in anti-ferroptotic gene expression. Moreover, JA synergistically enhanced RSL3 cytotoxicity under 2D and several 3D conditions. Similar effects were observed with punicic acid, another plant-derived CLnA isomer. Our study exploits a common feature of cancer metabolism, increased fatty acid uptake, to turn it into a vulnerability. The incorporation of JA into breast cancer cells creates a highly peroxidisable environment that increases cancer cell sensitivity to RSL3, potentially reducing required doses and minimising side effects.

Predictive Value of STC2 Gene Expression in Chemotherapy Response in Breast Cancer

Background: Breast cancer is the most commonly diagnosed cancer among women, and resistance to chemotherapy presents a significant challenge in its treatment. Stanniocalcin-2 (STC2), a glycoprotein involved in calcium homeostasis and cellular stress responses, is frequently overexpressed in various human cancers. Despite its critical role in cellular adaptation to stress, the potential of STC2 as a biomarker for predicting chemotherapy response has not been evaluated. This study aimed to assess the potential of STC2 as a predictive biomarker of response to chemotherapy in breast cancer. Methods: We utilized publicly available databases to characterize STC2 expression in breast cancer patients and its role in predicting relapse-free survival (RFS). Moreover, we evaluated the treatment responses of patients subjected to chemotherapy, correlating their outcomes with STC2 expression levels to determine its potential as a predictive biomarker. Finally, we evaluated the STC2 expression levels in breast cancer cell lines following exposure to doxorubicin (Dox), the primary anthracycline used in chemotherapy, and they were contrasted with the publicly available dataset. Results: The analysis showed that STC2 is significantly overexpressed in luminal A breast cancer, where it is linked to genetic amplifications. High STC2 expression was associated with improved RFS in ER-positive patients but correlated with worse outcomes in ER-negative cases. Furthermore, in grade II ER-positive patients, higher STC2 expression is linked to better chemotherapy response, while in grade II ER-negative patients, it was associated with poorer response. Finally, STC2 downregulation was observed in response to Dox treatment. Conclusions: These findings suggest that STC2 expression serves as a predictive biomarker for chemotherapy response in grade II breast cancer patients.

TBC1 domain-containing proteins are frequently involved in triple-negative breast cancers in connection with the induction of a glycolytic phenotype

Metabolic plasticity is a hallmark of cancer, and metabolic alterations represent a promising therapeutic target. Since cellular metabolism is controlled by membrane traffic at multiple levels, we investigated the involvement of TBC1 domain-containing proteins (TBC1Ds) in the regulation of cancer metabolism. These proteins are characterized by the presence of a RAB-GAP domain, the TBC1 domain, and typically function as attenuators of RABs, the master switches of membrane traffic. However, a number of TBC1Ds harbor mutations in their catalytic residues, predicting biological functions different from direct regulation of RAB activities. Herein, we report that several genes encoding for TBC1Ds are expressed at higher levels in triple-negative breast cancers (TNBC) vs. other subtypes of breast cancers (BC), and predict prognosis. Orthogonal transcriptomics/metabolomics analysis revealed that the expression of prognostic TBC1Ds correlates with elevated glycolytic metabolism in BC cell lines. In-depth investigations of the three top hits from the previous analyses (TBC1D31, TBC1D22B and TBC1D7) revealed that their elevated expression is causal in determining a glycolytic phenotype in TNBC cell lines. We further showed that the impact of TBC1D7 on glycolytic metabolism of BC cells is independent of its known participation in the TSC1/TSC2 complex and consequent downregulation of mTORC1 activity. Since TBC1D7 behaves as an independent prognostic biomarker in TNBC, it could be used to distinguish good prognosis patients who could be spared aggressive therapy from those with a poor prognosis who might benefit from anti-glycolytic targeted therapies. Together, our results highlight how TBC1Ds connect disease aggressiveness with metabolic alterations in TNBC. Given the high level of heterogeneity among this BC subtype, TBC1Ds could represent important tools in predicting prognosis and guiding therapy decision-making.

The Transformative Role of 3D Culture Models in Triple-Negative Breast Cancer Research

Advancements in cell culturing techniques have allowed the development of three-dimensional (3D) cell culture models sourced directly from patients' tissues and tumors, faithfully replicating the native tissue environment. These models provide a more clinically relevant platform for studying disease progression and treatment responses compared to traditional two-dimensional (2D) models. Patient-derived organoids (PDOs) and patient-derived xenograft organoids (PDXOs) emerge as innovative 3D cancer models capable of accurately mimicking the tumor's unique features, enhancing our understanding of tumor complexities, and predicting clinical outcomes. Triple-negative breast cancer (TNBC) poses significant clinical challenges due to its aggressive nature, propensity for early metastasis, and limited treatment options. TNBC PDOs and PDXOs have significantly contributed to the comprehension of TNBC, providing novel insights into its underlying mechanism and identifying potential therapeutic targets. This review explores the transformative role of various 3D cancer models in elucidating TNBC pathogenesis and guiding novel therapeutic strategies. It also provides an overview of diverse 3D cell culture models, derived from cell lines and tumors, highlighting their advantages and culturing challenges. Finally, it delves into live-cell imaging techniques, endpoint assays, and alternative cell culture media and methodologies, such as scaffold-free and scaffold-based systems, essential for advancing 3D cancer model research and development.