Tumor Glucose and Fatty Acid Metabolism in the Context of Anthracycline and Taxane-Based (Neo)Adjuvant Chemotherapy in Breast Carcinomas

NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate enhances the antitumor effect of quercetin liposomes in triple-negative breast cancer

In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (1H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.28 times and 2.43 times that of the QUE Solution and QUE-L groups, respectively. The release amount of NPC-QUE-L in an acidic environment was significantly higher than in physiological pH value. The order of the tumor cell inhibition rate in different pH environments was NPC-QUE-L > PC-QUE-L > QUE-L. In addition, the cellular uptake of NPC-modified liposomes was higher than that of PC-modified and unmodified liposomes, indicating that NPC had good pH-sensitivity and targeting. In the triple-negative breast cancer (TNBC) model, the relative tumor proliferation rate of NPC-QUE-L is about 73%, which is better than that of the QUE solution group. Western blot results show that NPC-QUE-L can effectively reduce the expression of α-smooth actin and transforming growth factor-β1 in tumor tissues, and improve the degree of tumor fibrosis. In this study, NPC could endow QUE liposomes with good stability, pH-sensitivity, and targeting, which provides a reference for improving the solubility and targeting of poorly soluble natural drug components.

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer

Background

This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

Method

We retrospectively analyzed ultrasound images and clinical information from 231 participants with BC who received NAC. These patients were randomly assigned to training and validation cohorts. Tumor regions of interest (ROI) were delineated, and radiomics features were extracted. Z-score normalization, Pearson correlation analysis, and the least absolute shrinkage selection operator (LASSO) were utilized for further screening ultrasound radiomics and clinical features. Univariate and multivariate logistic regression analysis were performed to identify the CFs that were independently associated with pCR. We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). Diagnostic performance was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC), accuracy, sensitivity, and specificity, and the Rad score was calculated. Subsequently, construction of clinical predictive models and Rad score joint clinical predictive models using ML algorithms for optimal diagnostic performance. The diagnostic process of the ML model was visualized and analyzed using SHapley Additive exPlanation (SHAP).

Results

Out of 231 participants with BC, 98 (42.42%) achieved pCR, and 133 (57.58%) did not. Twelve radiomics features were identified, with the GBM model demonstrating the best predictive performance (AUC of 0.851, accuracy of 0.75, sensitivity of 0.821, and specificity of 0.698). The clinical feature prediction model using the GBM algorithm had an AUC of 0.819 and an accuracy of 0.739. Combining the Rad score with clinical features in the GBM model resulted in superior predictive performance (AUC of 0.939 and an accuracy of 0.87). SHAP analysis indicated that participants with a high Rad score, PR-negative, ER-negative and human epidermal growth factor receptor-2 (HER-2) positive were more possibly to reach pCR. Based on the decision curve analysis, it was shown that the combined model of GBM provided higher clinical benefits.

Conclusion

The GBM model based on ultrasound radiomics features and routine clinical date of BC patients had high performance in predicting pCR. SHAP analysis provided a clear explanation for the prediction results of the GBM model, revealing that patients with a high Rad score, PR-negative status, ER-negative status and HER-2-positive status are more likely to achieve pCR.

Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature

Introduction

Lipid metabolic reprogramming is gaining attention as a hallmark of cancers. Recent mounting evidence indicates that the malignant behavior of breast cancer (BC) is closely related to lipid metabolism. Here, we focus on the estrogen receptor-positive (ER+) subtype, the most common subgroup of BC, to explore immunometabolism landscapes and prognostic significance according to lipid metabolism-related genes (LMRGs).

Methods

Samples from The Cancer Genome Atlas (TCGA) database were used as training cohort, and samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Gene Expression Omnibus (GEO) datasets and our cohort were applied for external validation. The survival-related LMRG molecular pattern and signature were constructed by unsupervised consensus clustering and least absolute shrinkage and selection operator (LASSO) analysis. A lipid metabolism-related clinicopathologic nomogram was established. Gene enrichment and pathway analysis were performed to explore the underlying mechanism. Immune landscapes, immunotherapy and chemotherapy response were further explored. Moreover, the relationship between gene expression and clinicopathological features was assessed by immunohistochemistry.

Results

Two LMRG molecular patterns were identified and associated with distinct prognoses and immune cell infiltration. Next, a prognostic signature based on nine survival-related LMRGs was established and validated. The signature was confirmed to be an independent prognostic factor and an optimal nomogram incorporating age and T stage (AUC of 5-year overall survival: 0.778). Pathway enrichment analysis revealed differences in immune activities, lipid biosynthesis and drug metabolism by comparing groups with low- and high-risk scores. Further exploration verified different immune microenvironment profiles, immune checkpoint expression, and sensitivity to immunotherapy and chemotherapy between the two groups. Finally, arachidonate 15-lipoxygenase (ALOX15) was selected as the most prominent differentially expressed gene between the two groups. Its expression was positively related to larger tumor size, more advanced tumor stage and vascular invasion in our cohort (n = 149).

Discussion

This is the first lipid metabolism-based signature with value for prognosis prediction and immunotherapy or chemotherapy guidance for ER+ BC.

Influence of cyclin D1 splicing variants expression on breast cancer chemoresistance via CDK4/CyclinD1-pRB-E2F1 pathway

Cyclin D1 (CCND1), a mediator of cell cycle control, has a G870A polymorphism which results in the formation of two splicing variants: full-length CCND1 (CCND1a) and C-terminally truncated CCND1 species (CCND1b). However, the role of CCND1a and CCND1b variants in cancer chemoresistance remains unknown. Therefore, this study aimed to explore the molecular mechanism of alternative splicing of CCND1 in breast cancer (BC) chemoresistance. To address the contribution of G870A polymorphism to the production of CCND1 variants in BC chemoresistance, we sequenced the G870A polymorphism and analysed the expressions of CCND1a and CCND1b in MCF-7 and MCF-7/ADM cells. In comparison with MCF-7 cells, MCF-7/ADM cells with the A allele could enhance alternative splicing with the increase of SC-35, upregulate the ratio of CCND1b/a at both mRNA and protein levels, and activate the CDK4/CyclinD1-pRB-E2F1 pathway. Furthermore, CCND1b expression and the downstream signalling pathway were analysed through Western blotting and cell cycle in MCF-7/ADM cells with knockdown of CCND1b. Knockdown of CCND1b downregulated the ratio of CCND1b/a, demoted cell proliferation, decelerated cell cycle progression, inhibited the CDK4/CyclinD1-pRB-E2F1 pathway and thereby decreased the chemoresistance of MCF-7/ADM cells. Finally, CCND1 G870A polymorphism, the alternative splicing of CCDN1 was detected through Sequenom Mass ARRAY platform, Sanger sequencing, semi-quantitative RT-PCR, Western blotting and immunohistochemistry in clinical BC specimens. The increase of the ratio of CCND1b/a caused by G870A polymorphism was involved in BC chemoresistance. Thus, these findings revealed that CCND1b/a ratio caused by the polymorphism is involved in BC chemoresistance via CDK4/CyclinD1-pRB-E2F1 pathway.

Lipid Metabolism Heterogeneity and Crosstalk with Mitochondria Functions Drive Breast Cancer Progression and Drug Resistance

Breast cancer (BC) is a heterogeneous disease that can be triggered by genetic alterations in mammary epithelial cells, leading to diverse disease outcomes in individual patients. The metabolic heterogeneity of BC enhances its ability to adapt to changes in the tumor microenvironment and metabolic stress, but unfavorably affects the patient's therapy response, prognosis and clinical effect. Extrinsic factors from the tumor microenvironment and the intrinsic parameters of cancer cells influence their mitochondrial functions, which consequently alter their lipid metabolism and their ability to proliferate, migrate and survive in a harsh environment. The balanced interplay between mitochondria and fatty acid synthesis or fatty acid oxidation has been attributed to a combination of environmental factors and to the genetic makeup, oncogenic signaling and activities of different transcription factors. Hence, understanding the mechanisms underlying lipid metabolic heterogeneity and alterations in BC is gaining interest as a major target for drug resistance. Here we review the major recent reports on lipid metabolism heterogeneity and bring to light knowledge on the functional contribution of diverse lipid metabolic pathways to breast tumorigenesis and therapy resistance.

Characterisation of 3D Bioprinted Human Breast Cancer Model for In Vitro Drug and Metabolic Targeting

Monolayer cultures, the less standard three-dimensional (3D) culturing systems, and xenografts are the main tools used in current basic and drug development studies of cancer research. The aim of biofabrication is to design and construct a more representative in vivo 3D environment, replacing two-dimensional (2D) cell cultures. Here, we aim to provide a complex comparative analysis of 2D and 3D spheroid culturing, and 3D bioprinted and xenografted breast cancer models. We established a protocol to produce alginate-based hydrogel bioink for 3D bioprinting and the long-term culturing of tumour cells in vitro. Cell proliferation and tumourigenicity were assessed with various tests. Additionally, the results of rapamycin, doxycycline and doxorubicin monotreatments and combinations were also compared. The sensitivity and protein expression profile of 3D bioprinted tissue-mimetic scaffolds showed the highest similarity to the less drug-sensitive xenograft models. Several metabolic protein expressions were examined, and the in situ tissue heterogeneity representing the characteristics of human breast cancers was also verified in 3D bioprinted and cultured tissue-mimetic structures. Our results provide additional steps in the direction of representing in vivo 3D situations in in vitro studies. Future use of these models could help to reduce the number of animal experiments and increase the success rate of clinical phase trials.