Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

PURPOSE

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

METHODS

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

RESULTS

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

CONCLUSION

The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

Targeting systemic and gut microbial metabolism in ER+ breast cancer

Estrogen receptor-positive (ER+) breast tumors have a better overall prognosis than ER- tumors; however, there is a sustained risk of recurrence. Mounting evidence indicates that genetic and epigenetic changes associated with resistance impact critical signaling pathways governing cell metabolism. This review delves into recent literature concerning the metabolic pathways regulated in ER+ breast tumors by the availability of nutrients and endocrine therapies and summarizes research on how changes in systemic and gut microbial metabolism can affect ER activity and responsiveness to endocrine therapy. As targeting of metabolic pathways using dietary or pharmacological approaches enters the clinic, we provide an overview of the supporting literature and suggest future directions.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15-30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829-0.847 and 0.712-0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92-8.29 and 1.59-3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

Regulation of Tumor Apoptosis of Poriae cutis-Derived Lanostane Triterpenes by AKT/PI3K and MAPK Signaling Pathways In Vitro

Poria cocos is traditionally used as both food and medicine. Triterpenoids in Poria cocos have a wide range of pharmacological activities, such as diuretic, sedative and tonic properties. In this study, the anti-tumor activities of poricoic acid A (PAA) and poricoic acid B (PAB), purified by high-speed counter-current chromatography, as well as their mechanisms and signaling pathways, were investigated using a HepG2 cell model. After treatment with PAA and PAB on HepG2 cells, the apoptosis was obviously increased (p < 0.05), and the cell cycle arrested in the G2/M phase. Studies showed that PAA and PAB can also inhibit the occurrence and development of tumor cells by stimulating the generation of ROS in tumor cells and inhibiting tumor migration and invasion. Combined Polymerase Chain Reaction and computer simulation of molecular docking were employed to explore the mechanism of tumor proliferation inhibition by PAA and PAB. By interfering with phosphatidylinositol-3-kinase/protein kinase B, Mitogen-activated protein kinases and p53 signaling pathways; and further affecting the expression of downstream caspases; matrix metalloproteinase family, cyclin-dependent kinase -cyclin, Intercellular adhesion molecules-1, Vascular Cell Adhesion Molecule-1 and Cyclooxygenase -2, may be responsible for their anti-tumor activity. Overall, the results suggested that PAA and PAB induced apoptosis, halted the cell cycle, and inhibited tumor migration and invasion through multi-pathway interactions, which may serve as a potential therapeutic agent against cancer.

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.