Group-sparse Modeling Drug-kinase Networks for Predicting Combinatorial Drug Sensitivity in Cancer Cells

Abstract Increasing evidence shows that cancer stem cells are responsible for drug resistance and relapse of tumors. In breast cancer, human epidermal growth factor receptor 2 (HER2) induces Herceptin resistance by inducing cancer stem cells. In the present study, we explored the effect of HER2 on cancer stem cells induction and drug sensitivity of ovarian cancer cell lines. First, we found that HER2 overexpression (HER2 OE) induced, while HER2 knockdown (HER2 KD) decreased CD44+/CD24− population. Consistently, HER2 expression was closely correlated with the sphere formation efficiency (SFE) of ovarian cancer cells. Second, we found that NFκB inhibition by specific inhibitor JSH23 or siRNA targetting subunit p65 dramatically impaired the induction of ovarian cancer stem cells by HER2, indicating that NFκB mediated HER2-induced ovarian cancer stem cells. Third, we found that HER2 KD significantly attenuated the tumorigenicity of ovarian cancer cells. Further, we found that HER2 inhibition increased drastically the sensitivity of ovarian cancer cells to doxorubicin (DOX) or paclitaxel (PTX). Finally, we examined the correlation between HER2 status and stem cell-related genes expression in human ovarian tumor tissues, and found that expressions of OCT4, COX2, and Nanog were higher in HER2 positive tumors than in HER2 negative tumors. Consistently, the 5-year tumor-free survival rate of HER2 positive patients was dramatically lower than HER2 negative patients. Taken together, our data indicate that HER2 decreases drug sensitivity of ovarian cancer cells via inducing stem cell-like property.

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Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

10.1101/2020.02.06.930503 ◽

2020 ◽

Author(s):

Lei Deng ◽

Yideng Cai ◽

Wenhao Zhang ◽

Wenyi Yang ◽

Bo Gao ◽

...

Keyword(s):

Neural Network ◽

Cancer Cells ◽

Drug Targets ◽

Deep Neural Network ◽

Drug Sensitivity ◽

Predictive Ability ◽

Biological Knowledge ◽

Data Sets ◽

Source Codes ◽

Sensitivity Data

AbstractMotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity.ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells.AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

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ROS as a Novel Indicator to Predict Anticancer Drug Efficacy

10.21203/rs.2.11911/v2 ◽

2019 ◽

Author(s):

Tarek Zaidieh ◽

James Smith ◽

Karen Ball ◽

Qian An

Keyword(s):

Cancer Cells ◽

Copy Number ◽

Drug Sensitivity ◽

Drug Efficacy ◽

Ros Generation ◽

Targeting Therapy ◽

Mitochondrial Dna Copy Number ◽

Conventional Drug ◽

Apoptosis Pathways ◽

Mitochondria Targeting

Abstract Background Mitochondria are considered a primary intracellular site of reactive oxygen species (ROS) generation. Generally, cancer cells with mitochondrial genetic abnormalities (copy number change and mutations) have escalated ROS levels compared to normal cells. Since high levels of ROS can trigger apoptosis, treating cancer cells with low doses of mitochondria-targeting / ROS-stimulating agents may offer cancer-specific therapy. This study aimed to investigate how baseline ROS levels might influence cancer cells’ response to ROS-stimulating therapy. Methods Four cancer and one normal cell lines were treated with a conventional drug (cisplatin) and a mitochondria-targeting agent (dequalinium chloride hydrate) separately and jointly. Cell viability was assessed and drug combination synergisms were indicated by the combination index (CI). Mitochondrial DNA copy number (MtDNAcn), ROS and mitochondrial membrane potential (MMP) were measured, and the relative expression levels of the genes and proteins involved in ROS-mediated apoptosis pathways were also investigated. Results Our data showed a correlation between the baseline ROS level, mtDNAcn and drug sensitivity in the tested cells. Synergistic effect of both drugs was also observed with ROS being the key contributor in cell death. Conclusions Our findings suggest that mitochondria-targeting therapy could be more effective compared to conventional treatments. In addition, cancer cells with low levels of ROS may be more sensitive to the treatment, while cells with high levels of ROS may be more resistant. This study provides an insight into understanding the influence of intracellular ROS on drug sensitivity, and may lead to the development of new therapeutic strategies to improve efficacy of anticancer therapy.

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