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

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.

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

Biocomputing 2017 ◽

10.1142/9789813207813_0046 ◽

2016 ◽

Cited By ~ 3

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

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Novel three-dimensional cultures provide insights into thyroid cancer behavior

Endocrine Related Cancer ◽

10.1530/erc-19-0374 ◽

2020 ◽

Vol 27 (2) ◽

pp. 111-121 ◽

Cited By ~ 1

Author(s):

Mason A Lee ◽

Kensey N Bergdorf ◽

Courtney J Phifer ◽

Caroline Y Jones ◽

Sonia Y Byon ◽

...

Keyword(s):

Thyroid Cancer ◽

Cell Lines ◽

Cancer Cell ◽

High Throughput ◽

Drug Response ◽

3D Culture ◽

The United States ◽

Cancer Cell Lines ◽

Thyroid Cancer Cell ◽

Thyroid Cancer Cell Lines

Thyroid cancer has the fastest growing incidence of any cancer in the United States, as measured by the number of new cases per year. Despite advances in tissue culture techniques, a robust model for thyroid cancer spheroid culture is yet to be developed. Using eight established thyroid cancer cell lines, we created an efficient and cost-effective 3D culture system that can enhance our understanding of in vivo treatment response. We found that all eight cell lines readily form spheroids in culture with unique morphology, size, and cytoskeletal organization. In addition, we developed a high-throughput workflow that allows for drug screening of spheroids. Using this approach, we found that spheroids from K1 and TPC1 cells demonstrate significant differences in their sensitivities to dabrafenib treatment that closely model expected patient drug response. In addition, K1 spheroids have increased sensitivity to dabrafenib when compared to monolayer K1 cultures. Utilizing traditional 2D cultures of these cell lines, we evaluated the mechanisms of this drug response, showing dramatic and acute changes in their actin cytoskeleton as well as inhibition of migratory behavior in response to dabrafenib treatment. Our study is the first to describe the development of a robust spheroid system from established cultured thyroid cancer cell lines and adaptation to a high-throughput format. We show that combining 3D culture with traditional 2D methods provides a complementary and powerful approach to uncover drug sensitivity and mechanisms of inhibition in thyroid cancer.

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Deep learning for drug response prediction in cancer

Briefings in Bioinformatics ◽

10.1093/bib/bbz171 ◽

2020 ◽

Cited By ~ 9

Author(s):

Delora Baptista ◽

Pedro G Ferreira ◽

Miguel Rocha

Keyword(s):

Deep Learning ◽

Cell Lines ◽

Cancer Cell ◽

High Throughput Screening ◽

Drug Response ◽

Prediction Models ◽

Response Prediction ◽

Cancer Cell Lines ◽

Cancer Treatments ◽

Novel Drugs

Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:[email protected]

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Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

BMC Systems Biology ◽

10.1186/1752-0509-8-75 ◽

2014 ◽

Vol 8 (1) ◽

pp. 75 ◽

Cited By ~ 20

Author(s):

Silvia der Heyde ◽

Christian Bender ◽

Frauke Henjes ◽

Johanna Sonntag ◽

Ulrike Korf ◽

...

Keyword(s):

Breast Cancer ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Drug Response ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines ◽

Individual Drug ◽

Individual Drug Response

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

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720021500359 ◽

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.

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