scholarly journals Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi
Keyword(s):  
Feature Selection ◽  
Personalized Medicine ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Matrix Factorization ◽  
Drug Response ◽  
Hidden Markov ◽  
Cancer Cell Lines ◽  
Selection Of

Abstract Background Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution. Results This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. Conclusions We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF.

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

2021 ◽  
Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Matrix Factorization ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Tissue Type ◽  
Factorization Approach ◽  
Clinical Drug

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

Guide for Selection of Relevant Cell Lines During the Evaluation of new Anti-Cancer Compounds

2018 ◽  
Vol 18 (8) ◽  
pp. 1072-1081
Author(s):  
Angel J. Ruiz-Moreno ◽  
Patricia Torres-Barrera ◽  
Mireya Velázquez-Paniagua ◽  
Alexander Dömling ◽  
Marco A. Velasco-Velázquez
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Activity Analysis ◽  
Anti Cancer ◽  
Line Selection ◽  
Cell Line Selection ◽  
Selection Of

Background: Human cancer cell lines are valuable models for anti-cancer drug development. Although all cancer cells share common biological features, each cancer cell line has unique genotypic/ phenotypic characteristics that affect drug response. Thus, the information obtained with a specific cancer cell line cannot be easily extrapolated to other cancer cells. Consequently, cell line selection during experimental design is critical for providing proper and clinically relevant structure-activity analysis. Methods: Herein, we critically review the use of cancer cell lines as tools for activity analysis by comparing two different scenarios: i) the use of multiple cancer cell lines, with the NCI-60 Program as the most representative example; and, ii) the selection of a single cell line with specific biological characteristics that match the rationale of compound design. Results: Considering that most laboratories evaluate the activity of new compounds using few cell lines, we provide a systematic strategy for selection based on the expression levels and genetic status of the target and the effectiveness of target inhibition or silencing. We exemplify the use of public databases for data retrieval and analysis as well as the critical comparison of such information with published results. Conclusion: This approach refines cell line selection, avoiding the perpetuation of published poor selection and enhancing the relevance of the results.

scholarly journals CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer *

2020 ◽  
Author(s):  
Banabithi Bose ◽  
Serdar Bozdag
Keyword(s):  
Precision Medicine ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Expression Profiles ◽  
Cancer Cell Lines ◽  
List Type ◽  
Primary Tumors ◽  
A Genome

ABSTRACTIn cancer research and drug development, human tumor-derived cell lines are used as popular model for cancer patients to evaluate the biological functions of genes, drug efficacy, side-effects, and drug metabolism. Using these cell lines, the functional relationship between genes and drug response and prediction of drug response based on genomic and chemical features have been studied. Knowing the drug response on the real patients, however, is a more important and challenging task. To tackle this challenge, some studies integrate data from primary tumors and cancer cell lines to find associations between cell lines and tumors. These studies, however, do not integrate multi-omics datasets to their full extent. Also, several studies rely on a genome-wide correlation-based approach between cell lines and bulk tumor samples without considering the heterogeneous cell population in bulk tumors. To address these gaps, we developed a computational pipeline, CTDPathSim, a pathway activity-based approach to compute similarity between primary tumor samples and cell lines at genetic, genomic, and epigenetic levels integrating multi-omics datasets. We utilized a deconvolution method to get cell type-specific DNA methylation and gene expression profiles and computed deconvoluted methylation and expression profiles of tumor samples. We assessed CTDPathSim by applying on breast and ovarian cancer data in The Cancer Genome Atlas (TCGA) and cancer cell lines data in the Cancer Cell Line Encyclopedia (CCLE) databases. Our results showed that highly similar sample-cell line pairs have similar drug response compared to lowly similar pairs in several FDA-approved cancer drugs, such as Paclitaxel, Vinorelbine and Mitomycin-c. CTDPathSim outperformed state-of-the-art methods in recapitulating the known drug responses between samples and cell lines. Also, CTDPathSim selected higher number of significant cell lines belonging to the same cancer types than other methods. Furthermore, our aligned cell lines to samples were found to be clinical biomarkers for patients’ survival whereas unaligned cell lines were not. Our method could guide the selection of appropriate cell lines to be more intently serve as proxy of patient tumors and could direct the pre-clinical translation of drug testing into clinical platform towards the personalized therapies. Furthermore, this study could guide the new uses for old drugs and benefits the development of new drugs in cancer treatments.CCS CONCEPTSComputational biologyGenomicsSystems biologyBioinformaticsGeneticsACM Reference formatBanabithi Bose, Serdar Bozdag. 2020. CTDPathSim: Cell line-tumor deconvoluted pathway-based similarity in the context of precision medicine in cancer.

scholarly journals kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 844
Author(s):  
Abhishek Majumdar ◽  
Yueze Liu ◽  
Yaoqin Lu ◽  
Shaofeng Wu ◽  
Lijun Cheng
Keyword(s):  
Gene Expression ◽  
Support Vector Regression ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Cancer Cell Lines ◽  
Support Vector

Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.

ANTITUMOR EFFECT OF SUNITINIB AND BORTEZOMIB ON BREAST CANCER CELL LINE IN VITRO

2017 ◽  
Vol 63 (1) ◽  
pp. 141-145
Author(s):  
Yuliya Khochenkova ◽  
Eliso Solomko ◽  
Oksana Ryabaya ◽  
Yevgeniya Stepanova ◽  
Dmitriy Khochenkov
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Antitumor Effect ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Actual Problem

The discovery for effective combinations of anticancer drugs for treatment for breast cancer is the actual problem in the experimental chemotherapy. In this paper we conducted a study of antitumor effect of the combination of sunitinib and bortezomib against MDA-MB-231 and SKBR-3 breast cancer cell lines in vitro. We found that bortezomib in non-toxic concentrations can potentiate the antitumor activity of sunitinib. MDA-MB-231 cell line has showed great sensitivity to the combination of bortezomib and sunitinib in vitro. Bortezomib and sunitinib caused reduced expression of receptor tyrosine kinases VEGFR1, VEGFR2, PDGFRa, PDGFRß and c-Kit on HER2- and HER2+ breast cancer cell lines

Synthesis and Characterization of Quinoline-3-Carboxamide Derivatives as Inhibitors of the ATM Kinase

2020 ◽  
Vol 20 (23) ◽  
pp. 2070-2079
Author(s):  
Srimadhavi Ravi ◽  
Sugata Barui ◽  
Sivapriya Kirubakaran ◽  
Parul Duhan ◽  
Kaushik Bhowmik
Keyword(s):  
Western Blot ◽  
Cancer Cells ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Blot Analysis ◽  
Western Blot Analysis ◽  
Cancer Cell Lines ◽  
Atm Kinase ◽  
Cytotoxicity Studies

Background: The importance of inhibiting the kinases of the DDR pathway for radiosensitizing cancer cells is well established. Cancer cells exploit these kinases for their survival, which leads to the development of resistance towards DNA damaging therapeutics. Objective: In this article, the focus is on targeting the key mediator of the DDR pathway, the ATM kinase. A new set of quinoline-3-carboxamides, as potential inhibitors of ATM, is reported. Methods: Quinoline-3-carboxamide derivatives were synthesized and cytotoxicity assay was performed to analyze the effect of molecules on different cancer cell lines like HCT116, MDA-MB-468, and MDA-MB-231. Results: Three of the synthesized compounds showed promising cytotoxicity towards a selected set of cancer cell lines. Western Blot analysis was also performed by pre-treating the cells with quercetin, a known ATM upregulator, by causing DNA double-strand breaks. SAR studies suggested the importance of the electron-donating nature of the R group for the molecule to be toxic. Finally, Western-Blot analysis confirmed the down-regulation of ATM in the cells. Additionally, the PTEN negative cell line, MDA-MB-468, was more sensitive towards the compounds in comparison with the PTEN positive cell line, MDA-MB-231. Cytotoxicity studies against 293T cells showed that the compounds were at least three times less toxic when compared with HCT116. Conclusion: In conclusion, these experiments will lay the groundwork for the evolution of potent and selective ATM inhibitors for the radio- and chemo-sensitization of cancer cells.

Synthesis, Anticancer Activity on Prostate Cancer Cell Lines and Molecular Modeling Studies of Flurbiprofen-Thioether Derivatives as Potential Target of MetAP (Type II)

2020 ◽  
Vol 16 (6) ◽  
pp. 735-749 ◽  
Author(s):  
Özgür Yılmaz ◽  
Burak Bayer ◽  
Hatice Bekçi ◽  
Abdullahi I. Uba ◽  
Ahmet Cumaoğlu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Modeling ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Prostate Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Prostate Cancer Cell Lines ◽  
Molecular Modeling Studies

Background:: Prostate cancer is still one of the serious causes of mortality and morbidity in men. Despite recent advances in anticancer therapy, there is a still need of novel agents with more efficacy and specificity in the treatment of prostate cancer. Because of its function on angiogenesis and overexpression in the prostate cancer, methionine aminopeptidase-2 (MetAP-2) has been a potential target for novel drug design recently. Objective:: A novel series of Flurbiprofen derivatives N-(substituted)-2-(2-(2-fluoro-[1,1'- biphenyl]-4-il)propanoyl)hydrazinocarbothioamide (3a-c), 4-substituted-3-(1-(2-fluoro-[1,1'-biphenyl]- 4-yl)ethyl)-1H-1,2,4-triazole-5(4H)-thione (4a-d), 3-(substitutedthio)-4-(substituted-phenyl)- 5-(1-(2-fluoro-[1,1'-biphenyl]-4-yl)ethyl)-4H-1,2,4-triazole (5a-y) were synthesized. The purpose of the research was to evaluate these derivatives against MetAP-2 in vitro and in silico to obtain novel specific and effective anticancer agents against prostate cancer. Methods: The chemical structures and purities of the compounds were defined by spectral methods (1H-NMR, 13C-NMR, HR-MS and FT-IR) and elemental analysis. Anticancer activities of the compounds were evaluated in vitro by using MTS method against PC-3 and DU-143 (androgenindependent human prostate cancer cell lines) and LNCaP (androgen-sensitive human prostate adenocarcinoma) prostate cancer cell lines. Cisplatin was used as a positive sensitivity reference standard. Results:: Compounds 5b and 5u; 3c, 5b and 5y; 4d and 5o showed the most potent biological activity against PC3 cancer cell line (IC50= 27.1 μM, and 5.12 μM, respectively), DU-145 cancer cell line (IC50= 11.55 μM, 6.9 μM and 9.54 μM, respectively) and LNCaP cancer cell line (IC50= 11.45 μM and 26.91 μM, respectively). Some compounds were evaluated for their apoptotic caspases protein expression (EGFR/PI3K/AKT pathway) by Western blot analysis in androgen independent- PC3 cells. BAX, caspase 9, caspsase 3 and anti-apoptotic BcL-2 mRNA levels of some compounds were also investigated. In addition, molecular modeling studies of the compounds on MetAP-2 enzyme active site were evaluated in order to get insight into binding mode and energy. Conclusion:: A series of Flurbiprofen-thioether derivatives were synthesized. This study presented that some of the synthesized compounds have remarkable anticancer and apoptotic activities against prostate cancer cells. Also, molecular modeling studies exhibited that there is a correlation between molecular modeling and anticancer activity results.

Network Propagation Reveals Novel Features Predicting Drug Response of Cancer Cell Lines

2016 ◽  
Vol 11 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Jiguang Wang ◽  
Judith Kribelbauer ◽  
Raul Rabadan
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Network Propagation

Design, Synthesis, Molecular Docking, and Anticancer Evaluation of Pyrazole Linked Pyrazoline Derivatives with Carbothioamide Tail as EGFR Kinase Inhibitors

2020 ◽  
Vol 21 (1) ◽  
pp. 42-60
Author(s):  
Farah Nawaz ◽  
Ozair Alam ◽  
Ahmad Perwez ◽  
Moshahid A. Rizvi ◽  
Mohd. Javed Naim ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Kinase Assay ◽  
Cervix Uteri ◽  
Egfr Kinase

Background: The Epidermal Growth Factor Receptor (known as EGFR) induces cell differentiation and proliferation upon activation through the binding of its ligands. Since EGFR is thought to be involved in the development of cancer, the identification of new target inhibitors is the most viable approach, which recently gained momentum as a potential anticancer therapy. Objective: To assess various pyrazole linked pyrazoline derivatives with carbothioamide for EGFR kinase inhibitory as well as anti-proliferative activity against human cancer cell lines viz. A549 (non-small cell lung tumor), MCF-7 (breast cancer cell line), SiHa (cancerous tissues of the cervix uteri), and HCT-116 (colon cancer cell line). Methods: In vitro EGFR kinase assay, in vitro MTT assay, Lactate dehydrogenase release, nuclear staining (DAPI), and flow cytometry cell analysis. Results: Compounds 6h and 6j inhibited EGFR kinase at concentrations of 1.66μM and 1.9μM, respectively. Furthermore, compounds 6h and 6j showed the most potent anti-proliferative results against the A549 KRAS mutation cell line (IC50 = 9.3 & 10.2μM). Through DAPI staining and phase contrast microscopy, it was established that compounds 6h and 6j also induced apoptotic activity in A549 cells. This activity was further confirmed by FACS using Annexin-V-FITC and Propidium Iodide (PI) labeling. Molecular docking studies performed on 6h and 6j suggested that the compounds can bind to the hinge region of ATP binding site of EGFR tyrosine kinase in a similar pose as that of the standard drug gefitinib. Conclusion: The potential anticancer activity of compounds 6h and 6j was confirmed and need further exploration in cancer cell lines of different tissue origin and signaling pathways, as well as in animal models of cancer development.

scholarly journals Underutilized Mexican Plants: Screening of Antioxidant and Antiproliferative Properties of Mexican Cactus Fruit Juices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 368
Author(s):  
Elda M. Melchor Martínez ◽  
Luisaldo Sandate-Flores ◽  
José Rodríguez-Rodríguez ◽  
Magdalena Rostro-Alanis ◽  
Lizeth Parra-Arroyo ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Liver Cancer ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
In Vitro Assays ◽  
Total Phenolic ◽  
Liver Cancer Cell ◽  
Antiproliferative Activities

Cacti fruits are known to possess antioxidant and antiproliferative activities among other health benefits. The following paper evaluated the antioxidant capacity and bioactivity of five clarified juices from different cacti fruits (Stenocereus spp., Opuntia spp. and M. geomettizans) on four cancer cell lines as well as one normal cell line. Their antioxidant compositions were measured by three different protocols. Their phenolic compositions were quantified through high performance liquid chromatography and the percentages of cell proliferation of fibroblasts as well as breast, prostate, colorectal, and liver cancer cell lines were evaluated though in vitro assays. The results were further processed by principal component analysis. The clarified juice from M. geomettizans fruit showed the highest concentration of total phenolic compounds and induced cell death in liver and colorectal cancer cells lines as well as fibroblasts. The clarified juice extracted from yellow Opuntia ficus-indica fruit displayed antioxidant activity as well as a selective cytotoxic effect on a liver cancer cell line with no toxic effect on fibroblasts. In conclusion, the work supplies evidence on the antioxidant and antiproliferative activities that cacti juices possess, presenting potential as cancer cell proliferation preventing agents.

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