Although the classification of breast carcinomas into molecular or immunohistochemical subtypes has contributed to a better categorization of women into different therapeutic regimens, breast cancer nevertheless still progresses or recurs in a remarkable number of patients. Identifying women who would benefit from chemotherapy could potentially increase treatment effectiveness, which has important implications for long-term survival. Metabolomic analyses of fluids and tissues from cancer patients improve our knowledge of the reprogramming of metabolic pathways involved in resistance to chemotherapy. This review evaluates how recent metabolomic approaches have contributed to understanding the relationship between breast cancer and the acquisition of resistance. We focus on the advantages and challenges of cancer treatment and the use of new strategies in clinical care, which helps us comprehend drug resistance and predict responses to treatment.

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

High superoxide dismutase 2 (SOD2) expression is associated with a poor prognosis at many cancer sites, the presence of metastases, and more advanced cervical cancer. This study aims to determine whether SOD2 protein expression is associated with the prognosis of stage IIIB cervical carcinoma. Methods: sixty-three patients with stage IIIB squamous cell cervical carcinoma were included. The evaluation of SOD2 expression by immunohistochemistry was based on a positive cell ratio score and the staining intensity score. Taking disease recurrence and death as endpoints, receiver operating characteristic curves were used to discriminate between high and low SOD2 expression. Results: high SOD2 expression was associated with recurrence (p = 0.001), distant recurrence (p = 0.002), and death (p = 0.005). A multivariate analysis showed that patients with high SOD2 expression had a threefold increased risk for recurrence (HR = 3.16; 1.33–7.51) and death (HR = 2.98; 1.20–7.40) compared with patients who had low SOD2 expression. Patients with high SOD2 expression had shorter disease-free survival (p = 0.001) and overall survival (p = 0.003) than patients with low SOD2 expression. Conclusion: high SOD2 expression is a strong prognostic factor for stage IIIB squamous cell carcinoma of the cervix and could be used as a prognostic marker in women with cervical carcinoma.

Background: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. Methods: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. Results: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. Conclusion: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Breast cancer (BC) remains a significant global health concern, with neoadjuvant chemotherapy (NACT) offering preoperative benefits like tumor downstaging and treatment response assessment. However, identifying factors influencing post-NACT treatment response and survival outcomes is challenging. Metabolomic approaches offer promising insights into understanding these outcomes. This study analyzed the serum of 80 BC patients before and after NACT, followed for up to five years, correlating with disease-free survival (DFS) and overall survival (OS). Using untargeted nuclear magnetic resonance (NMR) spectroscopy and a novel statistical model that avoids collinearity issues, we identified metabolic changes associated with survival outcomes. Four metabolites (histidine, lactate, serine, and taurine) were significantly associated with DFS. We developed a metabolite-related survival score (MRSS) from these metabolites, stratifying patients into low- and high-risk relapse groups, independent of classical prognostic factors. High-risk patients had a hazard ratio (HR) for DFS of 3.42 (95% CI 1.51–7.74; p = 0.003) after adjustment for disease stage and age. A similar trend was observed for OS (HR of 3.34, 95% CI 1.64–6.80; p < 0.001). Multivariate Cox proportional hazards analysis confirmed the independent prognostic value of the MRSS. Our findings suggest the potential of metabolomic data, alongside traditional markers, in guiding personalized treatment decisions and risk stratification in BC patients undergoing NACT. This study provides a methodological framework for leveraging metabolomics in survival analyses.

Screening of fetomaternal hemorrhage (FMH) is essential in management of fetomaternal antigen incompatibilities of blood. The objective in this study was to evaluate the ability of automatic blood analyzer (ABA) to screen FMH, also comparing this method with flow cytometry (FCM). The contents of fetal red blood cells and fetal hemoglobin were evaluated by FCM and ABA, respectively, using both blood samples of male adults laced with umbilical cord blood diluted at 1/10, 1/100, 1/1,000, and 1/10,000, or blood from puerperal women collected within 48 hours following delivery. FCM had better performance (area under curve, AUC = 0.8723) than ABA (AUC = 0.6569) in detecting fetal blood laced with blood from male adults. At a critical level of 0.5%, ABA indicated that 27.5% of puerperal women would have FMH while FCM did not detect FMH. Our results showed that ABA overestimates FMH and disagrees with FCM on indicating puerperal women with FMH. ABA is inadequate for being used to screen for or to measure FMH.