Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology

The critical role of the bone marrow stromal microenvironment for the development of drug screening platforms in leukemia

Extensive research over the past 50 years has resulted in significant improvements in survival for patients diagnosed with leukemia. Despite this, a subgroup of patients harboring high-risk genetic alterations still suffer from poor outcomes. There is a desperate need for new treatments to improve survival, yet consistent failure exists in the translation of in vitro drug development to clinical application. Preclinical screening conventionally utilizes tumor cell monocultures to assess drug activity; however, emerging research has acknowledged the vital role of the tumor microenvironment in treatment resistance and disease relapse. Current co-culture drug screening methods frequently employ fibroblasts as the designated stromal cell component. Alternative stromal cell types that are known to contribute to chemoresistance are often absent in preclinical evaluations of drug efficacy. This review highlights mechanisms of chemoresistance by a range of different stromal constituents present in the bone marrow microenvironment. Utilizing an array of stromal cell types at the early stages of drug screening may enhance the translation of in vitro drug development to clinical use. Ultimately, we highlight the need to consider the bone marrow microenvironment in drug screening platforms for leukemia to develop superior therapies for the treatment of high-risk patients with poor prognostic outcomes.

Mesenchymal Stem/Stromal Cells May Decrease Success of Cancer Treatment by Inducing Resistance to Chemotherapy in Cancer Cells

Simple Summary

Tumours consist of different cell types and an extracellular matrix, all of which together form a complex microenvironment. The tumour microenvironment plays a critical role in various aspects of tumour development and progression. Mesenchymal stem/stromal cells (MSCs) are multipotent stem cells that have a tri-lineage differentiation capacity and are one of the key stromal cells in the tumour microenvironment. Following the interaction with cancer cells, they are transformed from naïve MSCs to tumour-associated MSCs, which substantially affect tumour growth and progression as well as the development of chemoresistance in cancer cells. The aim of this review article is to provide an overview of studies that have investigated how MSCs affect the susceptibility of cancer cells to chemotherapeutics. Their results show that MSCs protect cancer cells from chemotherapeutics by influencing several signalling pathways. This knowledge is crucial for the development of new treatment approaches that will lead to improved treatment outcomes.

Abstract

The tumour microenvironment, which is comprised of various cell types and the extracellular matrix, substantially impacts tumour initiation, progression, and metastasis. Mesenchymal stem/stromal cells (MSCs) are one of the key stromal cells in the tumour microenvironment, and their interaction with cancer cells results in the transformation of naïve MSCs to tumour-associated MSCs. The latter has an important impact on tumour growth and progression. Recently, it has been shown that they can also contribute to the development of chemoresistance in cancer cells. This review provides an overview of 42 studies published between 1 January 2001 and 1 January 2022 that examined the effect of MSCs on the susceptibility of cancer cells to chemotherapeutics. The studies showed that MSCs affect various signalling pathways in cancer cells, leading to protection against chemotherapy-induced damage. Promising results emerged from the use of inhibitors of various signalling pathways that are affected in cancer cells due to interactions with MSCs in the tumour microenvironment. These studies present a good starting point for the investigation of novel treatment approaches and demonstrate the importance of targeting the stroma in the tumour microenvironment to improve treatment outcomes.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

Involvement of immune system and Epithelial-Mesenchymal-Transition in increased invasiveness of clustered circulatory tumor cells in breast cancer

Background

Circulating tumor cells (CTCs) are the critical initiators of distant metastasis formation. In which, the reciprocal interplay among different metastatic pathways and their metastasis driver genes which promote survival of CTCs is not well introduced using network approaches.

Methods

Here, to investigate the unknown pathways of single/cluster CTCs, the co-expression network was reconstructed, using WGCNA (Weighted Correlation Network Analysis) method. Having used the hierarchical clustering, we detected the Immune-response and EMT subnetworks. The metastatic potential of genes was assessed and validated through the support vector machine (SVM), neural network, and decision tree methods on two external datasets. To identify the active signaling pathways in CTCs, we reconstructed a casual network. The Log-Rank test and Kaplan–Meier curve were applied to detect prognostic gene signatures for distant metastasis-free survival (DMFS). Finally, a predictive model was developed for metastasis risk of patients using VIF-stepwise feature selection.

Results

Our results showed the crosstalk among EMT, the immune system, menstrual cycles, and the stemness pathway in CTCs. In which, fluctuation of menstrual cycles is a new detected pathway in breast cancer CTCs. The reciprocal association between immune responses and EMT was identified in CTCs. The SVM model indicated a high metastatic potential of EMT subnetwork (accuracy, sensitivity, and specificity scores were 87%). The DMFS model was identified to predict patients’ metastasis risks. (c-index = 0.7). Finally, novel metastatic biomarkers of KRT18 and KRT19 were detected in breast cancer CTCs.

Conclusions

In conclusion, the reciprocal interplay among critical unknown pathways in CTCs manifests both their survival in blood and metastatic potentials. Such findings may help to develop more precise predictive metastatic-risk models or detect pivotal metastatic biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01112-9.

The efficacy of etanercept as anti-breast cancer treatment is attenuated by residing macrophages

BACKGROUND

Interaction between microenvironment and breast cancer cells often is not considered at the early stages of drug development leading to failure of many drugs at later clinical stages. Etanercept is a TNF-alpha inhibitor that has been investigated for potential antitumor effect in breast cancer with conflicting results.

METHODS

Secretome data on MDA-MB-231 cancer cell-line were from public repositories and subjected to gene enrichment analyses. Since MDA-MB-231 cells secrete high levels of Granulocyte-Monocyte Colony Stimulating Factor, which activates macrophages to promote tumor growth, the effect of macrophage co-culturing on anticancer efficacy of Etanercept in breast cancer was evaluated using the Boolean network modeling and in vitro experiments including invasion, cell cycle, Annexin PI, and tetrazolium based viability assays and NFKB activity.

RESULTS

The secretome profile of MDA-MB-231 cells was similar to the expression of genes following treatment of breast cancer cells with TNF-α. Accordingly, inhibition of TNF-α by Etanercept decreased MDA-MB-231 cell survival, induced apoptosis and cell cycle arrest in vitro and inhibited NFKB activation. The inhibitory effect of Etanercept on cell viability, cell cycle progression, invasion and induction of apoptosis decreased following co-culturing of the cancer cells with macrophages. The Boolean network modeling of the changes in the dynamics of intracellular signaling pathways revealed NFKB activation by secretome of macrophages, leading to a decreased efficacy of Etanercept, suggesting NFKB inhibition as an alternative approach to inhibit cancer cell growth in the presence of macrophage crosstalk.

CONCLUSION

This study indicates that the effect of Etanercept may be influenced by residing macrophages in tumor microenvironment, and suggests a method to predict the effect of drugs in the presence of stromal cells to guide experimental designs in drug development.

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.

Epigenetic Regulation of Multidrug Resistance Protein 1 and Breast Cancer Resistance Protein Transporters by Histone Deacetylase Inhibition

Multidrug resistance protein 1 (MDR1, ABCB1, P-glycoprotein) and breast cancer resistance protein (BCRP, ABCG2) are key efflux transporters that mediate the extrusion of drugs and toxicants in cancer cells and healthy tissues, including the liver, kidneys, and the brain. Altering the expression and activity of MDR1 and BCRP influences the disposition, pharmacodynamics, and toxicity of chemicals, including a number of commonly prescribed medications. Histone acetylation is an epigenetic modification that can regulate gene expression by changing the accessibility of the genome to transcriptional regulators and transcriptional machinery. Recently, studies have suggested that pharmacological inhibition of histone deacetylases (HDACs) modulates the expression and function of MDR1 and BCRP transporters as a result of enhanced histone acetylation. This review addresses the ability of HDAC inhibitors to modulate the expression and the function of MDR1 and BCRP transporters and explores the molecular mechanisms by which HDAC inhibition regulates these transporters. While the majority of studies have focused on histone regulation of MDR1 and BCRP in drug-resistant and drug-sensitive cancer cells, emerging data point to similar responses in nonmalignant cells and tissues. Elucidating epigenetic mechanisms regulating MDR1 and BCRP is important to expand our understanding of the basic biology of these two key transporters and subsequent consequences on chemoresistance as well as tissue exposure and responses to drugs and toxicants. SIGNIFICANCE STATEMENT: Histone deacetylase inhibitors alter the expression of key efflux transporters multidrug resistance protein 1 and breast cancer resistance protein in healthy and malignant cells.

Pharmacological modulation of cell functional activity with valproic acid and erythropoietin

Introduction: Valproic acid (VA) is carboxylic acid with a branched chain, which is used as an antiepileptic drug.

Valproic acid influence on cells in vivo: VA, which is an antiepileptic drug, is also a teratogen, which causes defects of a neural tube and an axial skeleton, although the mechanisms are not yet fully clear.

Valproic acid influence on mesenchymal stem cells (MSC) in vitro: It is shown that valproic acid reduces the intracellular level of oxygen active forms.

Valproic acid effect on tumor cells: VA inhibits tumor growth through several mechanisms, including the cell cycle stop, differentiation induction and inhibition of growth of tumor vessels.

Valproic acid influence on enzymes: It affects mainly GSK-3.

Valproic acid influence on animals’ cells: It is shown that VA can significantly improve an ability to develop in vitro and improve nuclear reprogramming of embryos.

Erythropoietin (EPO): Is an hypoxia-induced hormone and a cytokine, which is necessary for normal erythropoiesis. EPO is widely used in in vitro experiments.

Conclusion: Thus, the influence of VA and EPO on cells can be used in cell technologies.