Crizotinib induced antitumor activity and synergized with chemotherapy and hormonal drugs in breast cancer cells via downregulating MET and estrogen receptor levels

Bioinformatics analysis of the role of lysosome-related genes in breast cancer

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

Synergistic effects of ozone with doxorubicin on the proliferation, apoptosis and metastatic profile of luminal-B type human breast cancer cell line

OBJECTIVE

Luminal-B type human breast cancer cell line (BT-474) to assess the synergistic effects of ozone applied after chemotherapeutic treatment with various dosages of doxorubicin, and compare the results with the effects on L929 fibroblast cell line.

METHODS

Doxorubicin (1-50 M) was added to each cell lines and left to sit for 24 h at 37 °C. Then, as combination groups, half of the groups were incubated with 30 g/mL ozone for 25 min. Tumor necrosis factor alpha (TNF-α), transforming growth factor beta (TGF-β), and matrix metalloproteinase-2 and - 9 (MMP-2 and MMP-9) levels were measured using the MTT test, flow cytometry, and immunocytochemistry, respectively.

RESULTS

When compared to simply doxorubicin-applied cells without ozone treatment, each dose of doxorubicin + ozone treatment considerably boosted L929 viability but significantly decreased BT-474 viability. Additionally, the combination increased the apoptotic impact of doxorubicin on BT-474 but not L929 (P < 0.001). TGF-, MMP-2, and MMP-9 levels of L929 after combination were substantially higher than those of the other groups (P < 0.01). Doxorubicin's effect on BT-474's protein levels, which had significantly decreased in comparison to those of the other groups, was reversed by the combination treatment (P < 0.05).

CONCLUSION

Doxorubicin's anti-proliferative and apoptotic effects were enhanced by ozone treatment in BT-474 cells, but it also repaired and healed healthy fibroblast cells that had been harmed by the cytotoxicity of the chemotherapy drug. If doxorubicin and ozone treatment are coupled, BT-474 cells may develop resistance to it through expressions of TNF-α, TGF-β, MMP-2, and MMP-9.

Drug screening at single-organoid resolution via bioprinting and interferometry

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.

A novel twelve-gene signature to predict neoadjuvant chemotherapy response and prognosis in breast cancer

Background

Accurate evaluation of the response to neoadjuvant chemotherapy (NAC) provides important information about systemic therapies for breast cancer, which implies pharmacological response, prognosis, and guide further therapy. Gene profiles overcome the shortcomings of the relatively limited detection indicators of the classical pathological evaluation criteria and the subjectivity of observation, but are complicated and expensive. Therefore, it is essential to develop a more accurate, repeatable, and economical evaluation approach for neoadjuvant chemotherapy responses.

Methods

We analyzed the transcriptional profiles of chemo-resistant breast cancer cell lines and tumors of chemo-resistant breast cancer patients in the GSE25066 dataset. We preliminarily screened out common significantly differentially expressed genes and constructed a NAC response risk model using LASSO regression and univariate and multivariate analyses. The differences in bioinformatic features of tumor cells, immune characteristics, and prognosis were compared between high and low-risk group. The potential drugs that could reverse chemotherapy resistance in breast cancer were screened by the CMap database.

Results

Thirty-six genes were commonly up/down-regulated in both NAC chemo-resistant tumors and cells compared to the sensitive tumors and wild-type cells. Through LASSO regression, we obtained a risk model composed of 12 genes. The risk model divided patients into high and low-risk groups. Univariate and multivariate Cox regression analyses suggested that the risk score is an independent prognostic factor for evaluating NAC response in breast cancer. Tumors in risk groups exhibited significant differences in molecular biological characteristics, tumor-infiltrating lymphocytes, and immunosuppressive molecule expression. Our results suggested that the risk score was also a good prognostic factor for breast cancer. Finally, we screened potential drugs that could reverse chemotherapy resistance in breast cancer.

Conclusion

A novel 12 gene-signature could be used to predict NAC response and predict prognosis in breast cancer.

Crizotinib prodrug micelles co-delivered doxorubicin for synergistic immunogenic cell death induction on breast cancer chemo-immunotherapy

Chemotherapeutic agents can trigger the immune response via inducing immunogenic cell death (ICD), but the weak ICD effect induced by chemotherapy alone limits its lasting antitumor immunotherapy effect. A Cro polymerized prodrug carriers (POEG-b-PCro) with immunostimulatory by ICD induction was developed and co-delivered DOX to generate synergistic ICD induction for chemo-immunotherapy on breast cancer. DOX/POEG-b-PCro micelles displayed prolonged circulation in blood, efficient accumulation in tumors, internalization and then co-released DOX&Cro in tumor cells. Moreover, the DOX/POEG-b-PCro micelles synergistically triggered ICD induction by releasing the nuclear high mobility group box 1 (HMGB1) and down-regulation of c-Met level for generating chemo-immune anti-tumor actions. Importantly, the DOX/POEG-b-PCro micelles synergistically enhanced the tumor cytotoxic T lymphocytes infiltration, concomitant decreasing the immunosuppressive regulatory T (Treg) cells, accompanied with the increased cytokines secretion of IFN-γ and TNF-α, consequently displaying an improved anti-tumor activity in 4T1 breast cancer mice. Overall, POEG-b-PCro prodrug micelles co-delivered DOX could be served as a promising nano drug delivery system for synergistic ICD induction on breast cancer chemo-immunotherapy.

Breast Cancer Prognosis Prediction and Immune Pathway Molecular Analysis Based on Mitochondria-Related Genes

Background

Mitochondria play an important role in breast cancer (BRCA). We aimed to build a prognostic model based on mitochondria-related genes.

Method

Univariate Cox regression analysis, random forest, and the LASSO method were performed in sequence on pretreated TCGA BRCA datasets to screen out genes from a Gene Set Enrichment Analysis, Gene Ontology: biological process gene set to build a prognosis risk score model. Survival analyses and ROC curves were performed to verify the model by using the GSE103091 dataset. The BRCA datasets were equally divided into high- and low-risk score groups. Comparisons between clinical features and immune infiltration related to different risk scores and gene mutation analysis and drug sensitivity prediction were performed for different groups.

Result

Four genes, MRPL36, FEZ1, BMF, and AFG1L, were screened to construct our risk score model in which the higher the risk score, the poorer the prognosis. Univariate and multivariate analyses showed that the risk score was significantly associated with age, M stage, and N stage. The gene mutation probability in the high-risk score group was significantly higher than that in the low-risk score group. Patients with higher risk scores were more likely to die. Drug sensitivity prediction in different groups indicated that PF-562271 and AS601245 might be new inhibitors of BRCA.

Conclusion

We developed a new workable risk score model based on mitochondria-related genes for BRCA prognosis and identified new targets and drugs for BRCA research.

The therapeutic efficacy and safety improvements of crizotinib prodrug micelles on breast cancer treatment

Nowadays, breast cancer has become a major killer threatening women health. MET is a receptor tyrosine kinase that upon binding of its ligand, hepatocyte growth factor, activates downstream pathways with diverse cellular functions which are important in occurrence and development of breast cancer. Crizotinib (Cro) is multi-target tyrosine kinase inhibitor targeting ALK gene recombination, MET gene amplification and ROS gene. Although, Cro has ideal treatment of breast cancer, Cro has stronger hepatotoxicity and lacks targeting capacity to tumor cell, which limited Cro to effectively therapy breast cancer. In this study, we develop a novel prodrug micelle through polymerization reaction polymerizing Cro onto the chain to form POEG-b-PCro prodrug micelles, which the drug loading capacity of Cro was significantly increased to improve cumulant of tumor. Pharmacokinetic and biodistribution studies illustrated POEG-b-PCro prodrug micelles had significant effect through improving Cro content in tumor. Meanwhile, antitumor mechanism of POEG-b-PCro prodrug micelles proved POEG-b-PCro prodrug micelles had stronger effect through reducing negative regulatory proteins. POEG-b-PCro prodrug micelles had splendid safety through safety study in vivo to account for POEG-b-PCro prodrug micelles. Therefore, POEG-b-PCro prodrug micelles are a promising drug delivery strategy for reducing toxicity and enhancing efficacy of Cro.

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

Stereospecific inhibition of AMPK by (R)-crizotinib induced changes to the morphology and properties of cancer and cancer stem cell-like cells

Crizotinib is used in the clinic for treating patients with ALK- or ROS1-positive non-small-cell lung carcinoma. The objective of the present study was to determine if crizotinib enantiomers could induce changes to the properties of cancer and cancer stem cell (CSC)-like cells at a high concentration (∼ 3 μM). While (R)-crizotinib induced changes in morphologies or sizes of cells, (S)-crizotinib did not. Pretreatment with (R)-crizotinib suppressed the proliferation of cancer or CSC-like cells in vitro and tumor growth in vivo. In vivo administration of (R)-crizotinib inhibited the growth of tumors formed from CSC-like cells by 72%. %. Along with the morphological changes induced by (R)-crizotinib, the expression levels of CD44 (NCI-H23 and HCT-15), ALDH1 (NCI-H460), nanog (PC-3), and Oct-4A (CSC-like cells), which appear to be specific marker proteins, were greatly changed, suggesting that changes in cellular properties accompanied the morphological changes in the cells. The expression levels of Snail, Slug, and E-cadherin were also greatly altered by (R)-crizotinib. Among several signal transduction molecules examined, AMPK phosphorylation appeared to be selectively inhibited by (R)-crizotinib. BML-275 (an AMPK inhibitor) and AMPKα2 siRNA efficiently induced morphological changes to all types of cells examined, suggesting that (R)-crizotinib might cause losses of characteristics of cancer or CSCs via inhibition of AMPK. These results indicate that (R)-crizotinib might be an effective anticancer agent that can cause alteration in cancer cell properties.

Overcoming resistance to targeted therapy using MET inhibitors in solid cancers: evidence from preclinical and clinical studies

Targeted therapy is a hallmark of cancer treatment that has changed the landscape of cancer management and enabled a personalized treatment approach. Nevertheless, the development of cancer resistance is a major challenge that is currently threatening the effective utilization of targeted therapies. The hepatocyte growth factor receptor, MET, is a receptor tyrosine kinase known for its oncogenic activity and tumorigenic potential. MET is a well-known driver of cancer resistance. A growing body of evidence revealed a major role of MET in mediating acquired resistance to several classes of targeted therapies. Deregulations of MET commonly associated with the development of cancer resistance include gene amplification, overexpression, autocrine activation, and crosstalk with other signaling pathways. Small-molecule tyrosine kinase inhibitors of MET are currently approved for the treatment of different solid cancers. This review summarizes the current evidence regarding MET-mediated cancer resistance toward targeted therapies. The molecular mechanisms associated with resistance are described along with findings from preclinical and clinical studies on using MET inhibitors to restore the anticancer activity of targeted therapies for the treatment of solid tumors.

Crizotinib-induced anti-cancer activity in human cervical carcinoma cells via ROS-dependent mitochondrial depolarization and induction of apoptotic pathway

INTRODUCTION

Cervical cancer is one of the leading causes of mortality among women population worldwide. In spite of recurrent screening, vaccination, and chemotherapeutic interventions, combating cervical cancer still remains a challenge. Crizotinib is a small molecule inhibitor that targets mesenchymal epithelial transition factor (c-MET) and has been successfully studied for its anti-cancer effects in non-small cell lung cancer, pancreatic, gastric, renal, prostate, and breast carcinomas. Although c-MET is a well-known prognostic, diagnostic, and therapeutic target in cervical cancer, anti-cancer properties of its inhibitor crizotinib against cervical carcinoma, has not been explored yet.

METHODS

In the present study, the anti-cancer effects of crizotinib on cervical cancer cells were evaluated using various in vitro cell-based assays, such as labelling drug-treated cells with MTT, H₂ DCFDA, Annexin V5-fluorescein isothiocyanate (FITC) antibody, JC-1, PI, and analysis using fluorescence-activated cell sorting (FACS).

RESULTS

The molecule was found to effectively inhibit proliferation of cervical cancer cells HeLa and SiHa with an IC₅₀ of 0.641 ± 0.0724 and 0.871 ± 0.104 μM, respectively, and induce apoptosis in a dose-dependent manner. Further investigations showed that crizotinib-induced production of reactive oxygen species (ROS) with increasing concentrations further resulted in mitochondrial membrane depolarization. However, the drug had no effect on cell cycle progression of HeLa and SiHa cells.

CONCLUSION

Thus, the study elucidates the cytotoxic effects of crizotinib in cervical cancer cells by activation of ROS-dependent apoptotic pathway via mitochondrial depolarization. These findings will further aid the evaluation of other molecular mechanisms of crizotinib and would pave the way for its implication as a chemotherapeutic option in cervical cancer.