Patients with Advanced Ovarian Cancer Administered Oral Etoposide following Taxane as Maintenance Chemotherapy

Elevated Levels of Lamin A Promote HR and NHEJ-Mediated Repair Mechanisms in High-Grade Ovarian Serous Carcinoma Cell Line

Extensive research for the last two decades has significantly contributed to understanding the roles of lamins in the maintenance of nuclear architecture and genome organization which is drastically modified in neoplasia. It must be emphasized that alteration in lamin A/C expression and distribution is a consistent event during tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (high-grade ovarian serous carcinoma cell line) in comparison to IOSE (immortalised ovarian surface epithelial cells) and, consequently, altered damage repair machinery in OVCAR3. We have analysed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We hereby establish the role of elevated lamin A in neoplastic transformation in the context of high-grade ovarian serous cancer through a combination of HR and NHEJ mechanisms.

DTF: Deep Tensor Factorization for predicting anticancer drug synergy

Motivation

Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs.

Results

We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug–cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations.

Availability and implementation

Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy.

Supplementary information

Supplementary data are available at Bioinformatics online.

Different transcriptome profiles between human retinoblastoma Y79 cells and an etoposide-resistant subline reveal a chemoresistance mechanism

Background

Retinoblastoma (RB) is the most frequent pediatric retinal tumor. In the present study, to elucidate chemoresistance mechanisms and identify potential biomarkers in RB, we utilized RNA sequencing (RNAseq) technological platforms to reveal transcriptome profiles and identify any differentially expressed genes (DEGs) between an etoposide drug-resistant subline (Y79/EDR) and parental Y79 cells.

Methods

To test whether Y79/EDR cells showed resistance to antineoplastic agents for RB, we treated the cells with etoposide, carboplatin and vincristine and analyzed them with a Cell Counting Kit-8 (CCK-8). Y79/EDR and parental Y79 cells were used for RNAseq and bioinformatics analysis to enable a genome-wide review of DEGs between the two lines using the DESeq R package (1.10.1). Then, DEG enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was analyzed with KOBAS software. Next, real-time quantitative reverse transcription polymerase chain reaction (real time QRT-PCR) and cytotoxicity assays were performed to experimentally and functionally validate the identified candidate biomarkers.

Results

Y79/EDR cells showed resistance to etoposide, carboplatin and vincristine at different concentrations. In total, 524 transcripts were differentially expressed in Y79/EDR cells based on analysis of fragments per kilobase of transcript per million fragments mapped (FPKM); among these, 57 genes were downregulated and 467 genes were upregulated in Y79/EDR cells compared to parental Y79 cells. We selected candidate DEGs, including ARHGAP9, HIST1H4H, RELN, DDIT4, HK2, STC1 and PFKFB4, for mRNA expression validation with real time QRT-PCR assays and found that the expression levels determined by real time QRT-PCR were consistent with the RNAseq data. Further studies involving downregulation of ARHGAP9 with a specific siRNA showed that ARHGAP9 altered the cellular sensitivity of Y79 cells to etoposide and carboplatin.

Conclusion

Our initial findings provided a genomic view of the transcription profiles of etoposide-induced acquired resistance in RB. Follow-up studies indicated that ARHGAP9 might be a chemoresistance biomarker in RB, providing insight into potential therapeutic targets for overcoming acquired chemoresistance in RB. These findings can aid in understanding and overcoming chemoresistance during treatment of RB in the clinic.

Chemotherapy induces ovarian cancer cell repopulation through the caspase 3-mediated arachidonic acid metabolic pathway

Recurrence is one of the major causes of high mortality in ovarian cancer. However, the mechanism of ovarian cancer recurrence after chemotherapy has not been fully understood. In the present study, we investigated the effect of chemotherapy-induced tumor microenvironment on the proliferation of SKOV3 cells. We have shown that SKOV3 cells repopulated faster in the culture medium from apoptotic SKOV3 ovarian cancer cells after 24 h of etoposide phosphate (VP-16) treatment. We found that during apoptosis, cleaved caspase 3 could activate cytosolic calcium-independent phospholipase A₂, which stimulated the release of arachidonic acid (AA) and triggered the production of prostaglandin E₂ (PGE₂). An increased level of phosphorylated focal adhesion kinase (FAK) subsequently facilitated the reproliferation of SKOV3 cells, and VP-16-induced repopulation effects were partially reversed by the FAK inhibitor PF562271. Furthermore, the plasma AA-to-PGE₂ ratio and tumoral FAK expression of ovarian cancer patients after chemotherapy were significantly lower than those before chemotherapy. Taken together, our results indicate that chemotherapy-induced apoptotic cancer cells can produce PGE₂-enriched microenvironment through caspase 3-mediated AA metabolic pathway, which could lead to the abnormal activation of FAK and eventually accelerate the repopulation of SKOV3 cells. Our study provides novel insight into a mechanism that may be utilized to prevent ovarian cancer recurrence in response to chemotherapy.