scholarly journals Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

2017 ◽  
Author(s):  
Fabian Fröhlich ◽  
Thomas Kessler ◽  
Daniel Weindl ◽  
Alexey Shadrin ◽  
Leonard Schmiester ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Drug Response ◽  
Mechanistic Model ◽  
Cancer Cell Lines ◽  
Mechanistic Models ◽  
Starting Point ◽  
Parameterized Model

The response of cancer cells to drugs is determined by various factors, including the cells’ mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

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

scholarly journals Saudi anti-human cancer plants database (SACPD): A collection of plants with anti-human cancer activities

2018 ◽  
Author(s):  
Ateeq Ahmed Al-Zahrani
Keyword(s):  
Saudi Arabia ◽  
Cell Lines ◽  
Anticancer Drugs ◽  
Plant Species ◽  
Cancer Cell ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Anticancer Activities ◽  
Excellent Starting Point ◽  
Starting Point

Several anticancer drugs have been developed from natural products such as plants. Successful experiments in inhibiting the growth of human cancer cell lines using Saudi plants were published over the last three decades. Up to date, there is no Saudi anticancer plants database as a comprehensive source for the interesting data generated from these experiments. Therefore, there was a need for creating a database to collect, organize, search and retrieve such data. As a result, the current paper describes the generation of the Saudi anti-human cancer plants database (SACPD). The database contains most of the reported information about the naturally growing Saudi anticancer plants. SACPD comprises the scientific and local names of 91 plant species that grow naturally in Saudi Arabia. These species belong to 38 different taxonomic families. In Addition, 18 species that represent16 family of medicinal plants and are intensively sold in the local markets in Saudi Arabia were added to the database. The website provides interesting details, including plant part containing the anticancer bioactive compounds, plants locations and cancer/cell type against which they exhibit their anticancer activity. Our survey revealed that breast, liver and leukemia were the most studied cancer cell lines in Saudi Arabia with percentages of 27%, 19% and 15%, respectively. The current SACPD represents a nucleus around which more development efforts can expand to accommodate all future submissions about new Saudi plant species with anticancer activities. SACPD will provide an excellent starting point for researchers and pharmaceutical companies who are interested in developing new anticancer drugs. SACPD is available online at https://teeqrani1.wixsite.com/sapd

scholarly journals Integrated analysis of breast cancer cell lines reveals unique signaling pathways

2009 ◽  
Vol 10 (3) ◽  
pp. R31 ◽  
Author(s):  
Laura M Heiser ◽  
Nicholas J Wang ◽  
Carolyn L Talcott ◽  
Keith R Laderoute ◽  
Merrill Knapp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Signaling Pathways ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Integrated Analysis

scholarly journals Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Farnoosh Abbas-Aghababazadeh ◽  
Pengcheng Lu ◽  
Brooke L. Fridley
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Functional Form ◽  
Cancer Cell Lines ◽  
Mixed Effects ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Modeling Framework ◽  
Response Data

Abstract Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.

scholarly journals Platform-integrated mRNA isoform quantification

2019 ◽  
Vol 36 (8) ◽  
pp. 2466-2473 ◽  
Author(s):  
Jiao Sun ◽  
Jae-Woong Chang ◽  
Teng Zhang ◽  
Jeongsik Yong ◽  
Rui Kuang ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Lines ◽  
Supplementary Information ◽  
Rna Seq ◽  
Mrna Quantification ◽  
Transcript Quantification ◽  
Isoform Expression ◽  
Isoform Quantification

Abstract Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DIFFERENTIAL PATHWAY DEPENDENCY DISCOVERY ASSOCIATED WITH DRUG RESPONSE ACROSS CANCER CELL LINES

2016 ◽  
Author(s):  
GIL SPEYER ◽  
DIVYA MAHENDRA ◽  
HAI J. TRAN ◽  
JEFF KIEFER ◽  
STUART L. SCHREIBER ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines

scholarly journals Large-scale screening identifies a novel microRNA, miR-15a-3p, which induces apoptosis in human cancer cell lines

RNA Biology ◽  
2013 ◽  
Vol 10 (2) ◽  
pp. 287-300 ◽  
Author(s):  
Aliaksandr Druz ◽  
Yu-Chi Chen ◽  
Rajarshi Guha ◽  
Michael Betenbaugh ◽  
Scott E. Martin ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Large Scale Screening
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