One therapeutic approach for triple-negative breast cancer: Checkpoint kinase 1 inhibitor AZD7762 combination with neoadjuvant carboplatin

Single-cell sequencing unveils mitophagy-related prognostic model for triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer lacking hormone receptors and HER2 expression, leading to limited treatment options and poor prognosis. Mitophagy, a selective autophagy process targeting damaged mitochondria, plays a complex role in cancer progression, yet its prognostic significance in TNBC is not well understood.

Methods

This study utilized single-cell RNA sequencing data from the TCGA and GEO databases to identify mitophagy-related genes (MRGs) associated with TNBC. A prognostic model was developed using univariate Cox analysis and LASSO regression. The model was validated across multiple independent cohorts, and correlations between MRG expression, immune infiltration, and drug sensitivity were explored.

Results

Nine key MRGs were identified and used to stratify TNBC patients into high-risk and low-risk groups, with the high-risk group showing significantly worse survival outcomes. The model demonstrated strong predictive accuracy across various datasets. Additionally, the study revealed a correlation between higher MRG expression levels and increased immune cell infiltration, as well as potential responsiveness to specific chemotherapeutic agents.

Conclusion

The mitophagy-related prognostic model offers a novel method for predicting outcomes in TNBC patients and highlights the role of mitophagy in influencing the tumor microenvironment, with potential applications in personalized treatment strategies.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Cellular signaling in the hypoxic cancer microenvironment: Implications for drug resistance and therapeutic targeting

The rewiring of cellular metabolism is a defining characteristic of cancer, as tumor cells adapt to acquire essential nutrients from a nutrient-poor environment to sustain their viability and biomass. While hypoxia has been identified as a major factor depriving cancer cells of nutrients, recent studies have revealed that cancer cells distant from supporting blood vessels also face nutrient limitations. To overcome this challenge, hypoxic cancer cells, which heavily rely on glucose as an energy source, employ alternative pathways such as glycogen metabolism and reductive carboxylation of glutamine to meet their energy requirements for survival. Our preliminary studies, alongside others in the field, have shown that under glucose-deficient conditions, hypoxic cells can utilize mannose and maltose as alternative energy sources. This review aims to comprehensively examine the hypoxic cancer microenvironment, its association with drug resistance, and potential therapeutic strategies for targeting this unique niche. Furthermore, we will critically evaluate the current literature on hypoxic cancer microenvironments and explore state-of-the-art techniques used to analyze alternate carbohydrates, specifically mannose and maltose, in complex biological fluids. We will also propose the most effective analytical methods for quantifying mannose and maltose in such biological samples. By gaining a deeper understanding of the hypoxic cancer cell microenvironment and its role in drug resistance, novel therapeutic approaches can be developed to exploit this knowledge.

XMR: an explainable multimodal neural network for drug response prediction

Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

Carboplatin Niosomal Nanoplatform for Potentiated Chemotherapy

This study aimed to characterize a stable nano-niosome formulation, which could reduce the adverse effects of carboplatin (CB) and improve its therapeutic efficacy in the treatment of breast cancer. For this purpose, CB-loaded polyethylene glycol (PEG)ylated niosome nanoparticles (PEG-NS-CB) were synthesized using the reverse-phase evaporation method. PEG-NS-CB (226.0 ± 10.6 nm) could release CB in a controlled manner and, compared to CB and CB-loaded non-PEGylated niosome (NS-CB), caused higher cytotoxicity effects against mouse breast cancer 4T1 cells (IC₅₀: 83.4, 26.6, and 22.5 µM for CB, NS-CB, and PEG-NS-CB, respectively). Also, PEG-NS-CB demonstrated higher stability, in which its profile of drug release, cytotoxicity, and LE% did not change significantly three months after preparation compared to those at the production time. In addition, the in vivo results demonstrated that PEG-NS-CB caused higher therapeutic (the number of alive mice: 12, 15, and 17 out of 20 in CB, NS-CB, and PEG-NS-CB receiver groups, respectively) and less toxicity effects (weight loss of 17, 12.5, and 10% in CB, NS-CB, and PEG-NS-CB receiver groups, respectively), compared to NS-CB and CB in breast cancer-bearing mice. Overall, the results of this study suggest that PEG-NS-CB could be a promising formulation for the treatment of breast cancer.

Targeting regulated cell death (RCD) with small-molecule compounds in triple-negative breast cancer: a revisited perspective from molecular mechanisms to targeted therapies

Triple-negative breast cancer (TNBC) is a subtype of human breast cancer with one of the worst prognoses, with no targeted therapeutic strategies currently available. Regulated cell death (RCD), also known as programmed cell death (PCD), has been widely reported to have numerous links to the progression and therapy of many types of human cancer. Of note, RCD can be divided into numerous different subroutines, including autophagy-dependent cell death, apoptosis, mitotic catastrophe, necroptosis, ferroptosis, pyroptosis and anoikis. More recently, targeting the subroutines of RCD with small-molecule compounds has been emerging as a promising therapeutic strategy, which has rapidly progressed in the treatment of TNBC. Therefore, in this review, we focus on summarizing the molecular mechanisms of the above-mentioned seven major RCD subroutines related to TNBC and the latest progress of small-molecule compounds targeting different RCD subroutines. Moreover, we further discuss the combined strategies of one drug (e.g., narciclasine) or more drugs (e.g., torin-1 combined with chloroquine) to achieve the therapeutic potential on TNBC by regulating RCD subroutines. More importantly, we demonstrate several small-molecule compounds (e.g., ONC201 and NCT03733119) by targeting the subroutines of RCD in TNBC clinical trials. Taken together, these findings will provide a clue on illuminating more actionable low-hanging-fruit druggable targets and candidate small-molecule drugs for potential RCD-related TNBC therapies.