scholarly journals ELIMINATOR: Essentiality anaLysIs using MultIsystem Networks And inTeger prOgRamming

2021 ◽  
Author(s):  
Asier Antoranz ◽  
Maria Ortiz ◽  
Jon Pey
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
In Silico ◽  
Large Scale ◽  
Gene Knockout ◽  
Patient Specific ◽  
Gene Essentiality ◽  
Molecular Features ◽  
The Impact

A gene is considered as essential when it is indispensable for cells to grow and replicate under a certain environment. However, gene essentiality is not a structural property but rather a contextual one, which depends on the specific biological conditions affecting the cell. This circumstantial essentiality of genes is what brings the attention of scientist since we can identify genes essential for cancer cells but not essential for healthy cells. This same contextuality makes their identification extremely challenging. Huge experimental efforts such as Project Achilles where the essentiality of thousands of genes is measured in over one thousand cell lines together with a plethora of molecular data (transcriptomics, copy number, mutations, etc.) can shed light on the causality behind the essentiality of a gene in a given environment by associating the measured essentiality to molecular features of the cell line. Here, we present an in-silico method for the identification of patient-specific essential genes using constraint-based modelling (CBM). Our method expands the ideas behind traditional CBM to accommodate multisystem networks, that is a biological network that focuses on complex interactions within several biological systems. In essence, it first calculates the minimum number of non-expressed genes required to be active by the cell to sustain life as defined by a set of requirements; and second, it performs an exhaustive in-silico gene knockout to find those that lead to the need of activating extra non-expressed genes. We validated the proposed methodology using a set of 452 cancer cell lines derived from the Cancer Cell Line Encyclopedia where an exhaustive experimental large-scale gene knockout study using CRISPR (Achilles Project) evaluates the impact of each removal. We also show that the integration of different essentiality predictions per gene, what we called Essentiality Congruity Score, (derived from multiple pathways) reduces the number of false positives. Finally, we explored the gene essentiality predictions for a breast cancer patient dataset, and our results showed high concordance with previous publications. These findings suggest that identifying genes whose activity are fundamental to sustain cellular life in a patient-specific manner is feasible using in-silico methods. The patient-level gene essentiality predictions can pave the way for precision medicine by identifying potential drug targets whose deletion can induce death in tumour cells.

scholarly journals Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

2018 ◽  
Author(s):  
James M McFarland ◽  
Zandra V Ho ◽  
Guillaume Kugener ◽  
Joshua M Dempster ◽  
Phillip G Montgomery ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Analytical Framework ◽  
Quality Parameters ◽  
Diverse Range ◽  
Hierarchical Bayesian Inference ◽  
Genome Scale

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

scholarly journals Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Koras ◽  
Ewa Kizling ◽  
Dilafruz Juraeva ◽  
Eike Staub ◽  
Ewa Szczurek
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Recommender System ◽  
Computational Models ◽  
Drug Sensitivity ◽  
Kinase Inhibitor ◽  
Cancer Cell Lines ◽  
Molecular Features

AbstractComputational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R $$=$$ =  0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.

DDRE-16. DRUG REPURPOSING SCREEN REVEALS GLIOBLASTOMA CELL LINE SUSCEPTIBILITY TO STATINS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi77-vi78
Author(s):  
Dylan Harwood ◽  
Signe Michaelsen ◽  
Filip Mundt ◽  
Bjarne Kristensen
Keyword(s):  
Cell Viability ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gene Knockout ◽  
Tumor Resection ◽  
Glioblastoma Cell ◽  
Potential Drug ◽  
Drug Candidates ◽  
Glioblastoma Cell Lines

Abstract BACKGROUND The standard therapy for glioblastoma patients is tumor resection followed by radiotherapy and temozolomide chemotherapy. Although glioblastoma has been extensively molecularly profiled along with other cancers, this knowledge has not yet been translated into improved survival outcomes. We used a bioinformatics approach to identify potential novel therapeutic strategies for glioblastoma. OBJECTIVES: Comprehensive online datasets which have assessed up to 1376 cancer cell lines in multiple ways were interrogated to identify potential drug candidates for glioblastoma. METHODS Datasets included were from the cancer cell line encyclopedia (mRNA expression), the Achilles project (cell viability following Crispr-Cas9 knockout) and PRISM (drug treatment). A t-test comparing cell viability of glioblastoma cell lines versus other cancers was used to identify potential drug candidates, followed by the use of multiple statistical tools to investigate potential mechanism of action and status of biomarkers. RESULTS Fluvastatin and pitavastatin produced the most significant effects in glioblastoma cell lines. The anti-cancer properties of statins have previously been attributed to the inhibition of HMG-Coa reductase. Here, we found their effects correlated with erastin, an enhancer of ferroptosis and with gene knockout of UBIAD1, which participates in non-mitochondrial ubiquinone synthesis. These effects were both found in glioblastoma cells and other cancers with a mesenchymal-like phenotype. CONCLUSION Statins appeared to be especially effective against glioblastoma lines and the effect could be linked to ferroptosis and inhibition of UBIAD1. In vitro validation of this finding is ongoing.

scholarly journals CNpare: matching DNA copy number profiles

2021 ◽  
Author(s):  
Blas Chaves-Urbano ◽  
Bárbara Hernando ◽  
Maria J Garcia ◽  
Geoff Macintyre
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Copy Number ◽  
Large Scale ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Dna Copy Number ◽  
Molecular Features ◽  
Genome Wide ◽  
Pan Cancer

Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Cell lines are often chosen based on their tissue of origin, however, the results of large-scale pan-cancer studies suggest that matching lines based on molecular features may be more appropriate. Existing approaches are available for matching lines based on gene expression, DNA methylation or low resolution DNA copy number features. However, a specific tool for computing similarity based on high resolution genome-wide copy number profiles is lacking. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number. CNpare compares copy number profiles using four different similarity metrics, quantifies the extent of genome differences between pairs, and facilitates comparison based on copy number signatures. CNpare incorporates a precomputed database of 1,170 human cancer cell line profiles for comparison. In an analysis of separate cultures of 304 cell line pairs, CNpare identified the matched lines in all cases. CNpare provides a powerful solution to the problem of selecting the best cell line models for cancer research, especially in the context of studying chromosomal instability.

scholarly journals A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale

2021 ◽  
Author(s):  
Vítor Vieira ◽  
Jorge Ferreira ◽  
Miguel Rocha
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Breast Cancer Cell Lines ◽  
Mathematical Framework ◽  
Cancer Aggressiveness ◽  
Transcriptomics Data ◽  
Metabolic Models ◽  
Context Specific

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction

In-silico studies and wet lab validation of Camptothecin derivatives for anti-cancer activity against liver (HepG2) and lung (A549) cancer cell lines

2021 ◽  
Vol 21 ◽  
Author(s):  
Komal Kalani ◽  
Dharmendra Kumar Yadav ◽  
Sarfaraz Alam ◽  
Feroz Khan ◽  
Mahendra P. Kashyap ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
A549 Cell ◽  
In Silico ◽  
Topoisomerase I ◽  
Cancer Cell Lines ◽  
In Silico Admet ◽  
In Silico Studies

Bcakground: In the present study we have explored the utility of QSAR modelling, in silico ADMET, docking, chemical semi-synthesis and in vitro evaluation studies for the identification of active camptothecin (CPT) derivatives against cancer targeting human liver (HepG2) and lung (A549) cancer cell lines. Methods: Two QSAR models were developed as screenings tools using multiple linear regression (MLR) method followed by ADMET and docking studies. The regression coefficient (r2) and cross-validation regression coefficients (rCV2T) of the QSAR model for HepG2 cell line was 0.95 and 0.90 respectively, and for A549 cell line it was 0.93 and 0.81, respectively. Results: In silico studies show that CPT derivatives (CPT-1 and CPT-6) possess drug-like properties. Docking performed on DNA Topoisomerase-I, showed significant binding affinity. Finally, predicted active derivatives were chemically semi synthesized, spectroscopically characterized and evaluated in-vitro for cytotoxic/anticancer activity against HepG2 and A549 cell lines. Conclusion: The experimental results agreed with the predicted results. These findings may be of immense importance in the anticancer drug development from an inexpensive and widely available natural product, camptothecin.

scholarly journals Network-based clustering for drug sensitivity prediction in cancer cell lines

2019 ◽  
Author(s):  
Maryam Pouryahya ◽  
Jung Hun Oh ◽  
James C. Mathews ◽  
Zehor Belkhatir ◽  
Caroline Moosmüller ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Computational Models ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Drug Sensitivity Prediction

AbstractThe study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We show that prior clustering of the heterogenous cell lines and structurally diverse drugs significantly improves the accuracy of the prediction. In addition, it facilities the interpretability of the results and identification of molecular biomarkers which are significant for both clustering of the cell lines and predicting the drug response.

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

Antioxidant effects and in vitro cytotoxicity on human cancer cell lines of flavonoid-rich Flamboyant (Delonix regia (Bojer) Raf.) flower extract

2020 ◽  
Vol 21 ◽  
Author(s):  
Putthiporn Khongkaew ◽  
Phanphen Wattanaarsakit ◽  
Konstantinos I. Papadopoulos ◽  
Watcharaphong Chaemsawang
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Leukemia Cell ◽  
Leukemia Cell Line ◽  
Flower Extract ◽  
Dependent Inhibition ◽  
Dose Dependent Inhibition ◽  
Murine Leukemia Cell ◽  
Dose Dependent

Background: Cancer is a noncommunicable disease with increasing incidence and mortality rates both worldwide and in Thailand. Its apparent lack of effective treatments is posing challenging public health issues. Introduction: Encouraging research results indicating probable anti-cancer properties of the Delonix regia flower extract (DRE) have prompted us to evaluate the feasibility of developing a type of product for future cancer prevention or treatment. Methods and Results: In the present report, using High Performance Liquid Chromatography (HPLC), we demonstrate in the DRE, the presence of high concentrations of three identifiable flavonoids, namely rutin 4.15±0.30 % w/w, isoquercitrin 3.04±0.02 %w/w, and myricetin 2.61±0.01 % w/w respectively while the IC50 of DPPH and ABTS assay antioxidation activity was 66.88±6.30 µg/ml and 53.65±7.24 µg/ml respectively. Discussion: Our cancer cell line studies using the MTT assay demonstrated DREs potent and dose dependent inhibition of murine leukemia cell line (P-388: 35.28±4.07% of cell viability remaining), as well as of human breast adenocarcinoma (MCF-7), human cervical carcinoma (HeLa), human oral cavity carcinoma (KB), and human colon carcinoma (HT-29) cell lines in that order of magnitude. Conclusion: Three identifiable flavonoids (rutin, isoquercitrin and myricetin) with high antioxidation activity and potent and dose dependent inhibition of murine leukemia cell line and five other cancer cell lines were documented in the DRE. The extract’s lack of cytotoxicity in 3 normal cell lines is a rare advantage not usually seen in current antineoplastic agents. Yet another challenge of the DRE was its low dissolution rate and long-term storage stability, issues to be resolved before a future product can be formulated.

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.

Sign in / Sign up

Export Citation Format

Share Document