scholarly journals Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Vol 36 (5) ◽  
pp. 1607-1613 ◽  
Author(s):  
Joseph C Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Supplementary Information ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Author(s):  
Joseph C. Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Disease Model ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

AbstractHigh Content Screening is an important tool in drug discovery and characterisation. Often, high content drug screens are performed on one single cell line. Yet, a single cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterise drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. The contribution of this paper is twofold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimisation of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multitask autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.

scholarly journals Molecular phenotyping of breast cancer cell lines at the single-cell level for automated cancer diagnosis and prediction of drug sensitivity

2021 ◽  
Author(s):  
G Gambardella ◽  
G Viscido ◽  
B Tumaini ◽  
A Isacchi ◽  
R Bosotti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Anticancer Agents ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Breast Cancer Cell Lines ◽  
Isogenic Cell Line

ABSTRACTBrest Cancer (BC) patient stratification is mainly driven by receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potentiality of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 32 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient’s tumour biopsy. We found that BC cell lines arbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (DRug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows transcriptional heterogeneity is common, dynamic and plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinics.

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

Thieno[2,3-d]pyrimidin-4(3H)-one Derivatives of Benzimidazole as Potential Anti-Breast Cancer (MDA-MB-231, MCF-7) Agents.

2020 ◽  
Vol 20 ◽  
Author(s):  
Stefan Dimov ◽  
Anelia Ts. Mavrova ◽  
Denitsa Yancheva ◽  
Biliana Nikolova ◽  
Iana Tsoneva
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Biologically Active ◽  
Breast Cancer Cell Lines ◽  
3T3 Cells ◽  
Fluorescence Study ◽  
Compound 21 ◽  
Mutational Activation ◽  
Mcf 7

Aims: The purpose was the synthesis of some new thienopyrimidines derivative of 1,3-disubstituted benzimidazoles and the evaluation of their cytotoxicity towards MDA-MB-231 and MCF-7 cell lines as well 3T3 cells. Background: An overexpression or mutational activation of TK receptors EGFR and HER2/neu are characteristic for tumors. It has been found that some thieno[2,3-d]pyrimidines exhibit better inhibitory activity against epidermal growth factor receptor (EGFR/ErbB-2) tyrosine kinase in comparison to aminoquinazolines. Breast cancer activity towards MDAMB-231 and MCF-7 cell lines by inhibiting EGFR was revealed by a novel 2-arylbenzimidazole. This motivated the synthesis of new thienopyrimidines possessing benzimidazole fragment in order to evaluate their cytotoxicity to the above mentioned cell lines. Objective: The objectives were the design and synthesis of a novel series thieno[2,3-d]pyrimidines bearing biologically active moieties as 1,3-disubstituted-benzimidazole heterocycle structurally similar to diaryl ureas in order to evaluate their cytotoxicity against MDA-MB-231, MCF-7 breast cancer cell lines. Methods: N,N-disubstituted benzimidazole-2-one carbonitriles were synthesized by Aza-Michael addition and used as precursors to generate some of the new thieno[2,3-d]pyrimidines in acidic medium. The interaction of chloroethyl-2- thienopyrimidines and 2-amino-benzimidazole resp. benzimidazol-2-one nitriles under solid-liquid transfer catalysis conditions lead to obtaining of new thienopyrimidines. MTT assay for cells survival was performed in order to establish the cytotoxicity of the tested compounds. Fluorescence study was used to elucidate some aspect of mechanism. Results: The effect of nine of the synthesized compounds was investigated towards MDA-MB-231 and MCF-7 cells as well as to 3T3 cells. Thieno[2,3-d]pyirimidine-4-one 16 (IC50 – 0.058 μM) and 21 (IC50 – 0.029 μM) possess high cytotoxicity against MDA-MB-231 cells after 24h. The most toxic against breast cancer MCF-7 cells was compounds 21 (IC50 – 0.074 μM), revealing lower cytotoxicity towards mouse fibroblast 3T3 cells with IC50 – 0.20 μM. SAR analisys was performed. Fluorescence study of the treatment of MDA-MB cells with compound 21 was carried out in order to clarify some aspects of mechanism of action. Conclusion: The relationship between cytotoxicity of compounds 14 and 20 against MCF-7 and 3T3 cells can suggest a similar mechanism of action. The antitumor potential of the tested compounds proves the necessity for further investigation to estimate the exact inhibition pathway in the cellular processes. The fluorescence study of the treatment of MDA-MB cells with compound 21 showed a rapid process of apoptosis.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning

2021 ◽  
Author(s):  
Yang Lin ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Cell Line ◽  
Cell Lines ◽  
Short Term Memory ◽  
Computational Method ◽  
Language Models ◽  
Supplementary Information ◽  
Deep Model ◽  
A Cell ◽  
Non Coding Rnas

Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cell-line-specific predictor lncLocator 2.0, which trains an end-to-end deep model per cell line, for predicting lncRNA subcellular localization from sequences.We first construct benchmark datasets of lncRNA subcellular localizations for 15 cell lines. Then we learn word embeddings using natural language models, and these learned embeddings are fed into convolutional neural network, long short-term memory and multilayer perceptron to classify subcellular localizations. lncLocator 2.0 achieves varying effectiveness for different cell lines and demonstrates the necessity of training cell-line-specific models. Furthermore, we adopt Integrated Gradients to explain the proposed model in lncLocator 2.0, and find some potential patterns that determine the subcellular localizations of lncRNAs, suggesting that the subcellular localization of lncRNAs is linked to some specific nucleotides. Availability The lncLocator 2.0 is available at www.csbio.sjtu.edu.cn/bioinf/lncLocator2 and the source code can be found at https://github.com/Yang-J-LIN/lncLocator2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Synthesis and Experimental Validation of New Designed Heterocyclic Compounds with Antiproliferative Activity versus Breast Cancer Cell Lines MCF-7 and MDA-MB-231

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Vincenza Barresi ◽  
Carmela Bonaccorso ◽  
Domenico A. Cristaldi ◽  
Maria N. Modica ◽  
Nicolò Musso ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Anticancer Agents ◽  
Cancer Cell Line ◽  
Breast Cancer Cell Lines ◽  
Design And Synthesis ◽  
Human Breast Cell ◽  
Mcf 7

Recent drug discovery efforts are highly focused towards identification, design, and synthesis of small molecules as anticancer agents. With this aim, we recently designed and synthesized novel compounds with high efficacy and specificity for the treatment of breast tumors. Based on the obtained results, we constructed a Volsurf+ (VS+) model using a dataset of 59 compounds able to predict the in vitro antitumor activity against MCF-7 cancer cell line for new derivatives. In the present paper, in order to further verify the robustness of this model, we report the results of the projection of more than 150 known molecules and 9 newly synthesized compounds. We predict their activity versus MCF-7 cell line and experimentally verify the in silico results for some promising chosen molecules in two human breast cell lines, MCF-7 and MDA-MB-231.

scholarly journals Coexpression of CAV-1, AT1-R and FOXM1 in prostate and breast cancer and normal cell lines and their influence on metastatic properties

2016 ◽  
Vol 63 (3) ◽  
Author(s):  
Karolina Kowalska ◽  
Magdalena Nowakowska ◽  
Kamila Domińska ◽  
Agnieszka W. Piastowska-Ciesielska
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Normal Cell ◽  
Metastatic Potential ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Prostate Cell

The aim of this study was to evaluate the coexpression of caveolin-1 (CAV-1), angiotensin II type 1 receptor (AT1-R) and forkhead box Ml (FOXM1) in prostate and breast cancer cell lines, in comparison with normal cell lines. CAV-1, AT1-R and FOXM1 expression was determined by reverse transcription-quantitative polymerase chain reaction and western blot analysis in the prostate cancer cell lines PC3, DU145 and LNCaP; prostate normal cell line PNT1A; breast cancer cell lines MCF-7 and MDA-MB-231; and the normal breast cell line 184A1. A correlation between the expression levels of the investigated genes and their metastatic properties was determined by the Spearman's rank test (P<0.05) and Aspin-Welsch t-test, respectively. In prostate cell lines, a significant correlation was noted between CAV-1 and AT1-R expression and between FOXM1 and CAV-1 expression. A correlation between the expression levels of the investigated genes and their metastatic potential was also observed, with relatively high expression of all the investigated genes in the normal prostate cell line PNT1A. In comparison to prostate cancer cell lines, an adverse dependency between CAV-1, AT1-R, FOXM1 expression and metastatic potential was observed in the breast cancer cell lines. Relatively high expression of all tested genes was observed in the normal breast cell line 184A1, which was decreasing respectively with increasing metastatic potential of breast cancer cell lines. The results obtained here indicate that CAV-1, FOXM1 and AT1-R may be potential markers of tumorigenesis in certain types of cancer in vitro.

scholarly journals Integrative analysis of mRNA and miRNA profiles identifies miR-193 and miR-210 as potential regulatory biomarkers in different molecular subtypes of breast cancer

2020 ◽  
Author(s):  
Adriane Feijo Evangelista ◽  
Renato J Oliveira ◽  
Viviane A O Silva ◽  
Rene A D C Vieira ◽  
Rui M Reis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Proliferation ◽  
Cell Line ◽  
Cell Lines ◽  
Breast Cancer Cell ◽  
Cancer Progression ◽  
Triple Negative ◽  
Molecular Subtypes ◽  
Breast Cancer Cell Lines ◽  
Mcf 7

Abstract Introduction: Breast cancer is the most frequently diagnosed malignancy among women. However, the role of microRNA expression in breast cancer progression is not fully understood. In this study we examined predictive interactions between differentially expressed miRNAs and mRNAs in breast cancer cell lines representative of the common molecular subtypes. Integrative bioinformatics analysis identified miR-193 and miR-210 as potential regulatory biomarkers of mRNA in breast cancer. Several recent studies have investigated these miRNAs in a broad range of tumors, but the mechanism of their involvement in cancer progression has not previously been investigated. Methods: The miRNA-mRNA interactions in breast cancer cell lines were identified by parallel expression analysis and miRNA target prediction programs. The expression profiles of mRNA and miRNAs from luminal (MCF-7, MCF-7/AZ and T47D), HER2 (BT20 and SK-BR3) and triple negative subtypes (Hs578T e MDA-MB-231) could be clearly separated by unsupervised analysis using HB4A cell line as a control. Breast cancer miRNA data from TCGA patients were grouped according to molecular subtypes and then used to validate these findings. Expression of miR-193 and miR-210 was investigated by miRNA transient silencing assays using the MCF7, BT20 and MDA-MB-231 cell lines. Functional studies included, xCELLigence system, ApoTox-Glo triplex, flow cytometry and transwell assays were performed to determine cell proliferation, cytotoxicity, apoptosis, migration and invasion, respectively. Results: The most evident effects were associated with cell proliferation after miR-210 silencing in triple negative subtype cell line MDA-MB-231. Using in silico prediction algorithms, TNFRSF10 was identified as one of the potential downstream targets for both miRNAs. The TNFRSF10C and TNFRSF10D mRNA expression inversely correlated with the expression levels of miR-193 and miR210 in breast cell lines and breast cancer patients, respectively. Other potential regulated genes whose expression also inversely correlated with both miRNAs were CCND1, a mediator on invasion and metastasis, and the tumor suppressor gene RUNX3. Conclusion: In summary, our findings identify miR-193 and miR-210 as potential regulatory miRNA in different molecular subtypes of breast cancer and suggest that miR-210 may have specific role in MDA-MB-231 proliferation. Our results highlight important new downstream regulated targets that may serve as promising therapeutic pathways for aggressive breast cancers.

scholarly journals Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Noemi Andor ◽  
Billy T Lau ◽  
Claudia Catalanotti ◽  
Anuja Sathe ◽  
Matthew Kubit ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Cancer Cell Lines ◽  
Gastric Cancer Cell Lines

Abstract Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

Sign in / Sign up

Export Citation Format

Share Document