scholarly journals Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

2018 ◽  
Author(s):  
Tiannan Guo ◽  
Augustin Luna ◽  
Vinodh N Rajapakse ◽  
Ching Chiek Koh ◽  
Zhicheng Wu ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Biology ◽  
Drug Response ◽  
Missing Values ◽  
Protein Complexes ◽  
Cancer Cell Lines ◽  
Molecular Data ◽  
Data Matrix ◽  
Proteomic Data

SummaryWe describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 SwissProt proteotypic proteins across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a landmark effort toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

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 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 Separation and Identification of HSP-Associated Protein Complexes from Pancreatic Cancer Cell Lines Using 2D CN/SDS-PAGE Coupled with Mass Spectrometry

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiyun Zhao ◽  
Hui Liu ◽  
Xinli Wang ◽  
Xiaodong Wang ◽  
Zhili Li
Keyword(s):  
Pancreatic Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Function ◽  
Pancreatic Cancer Cell ◽  
Protein Complexes ◽  
Early Stage ◽  
Cancer Cell Lines ◽  
Building Models ◽  
Sds Page

Protein complexes are a cornerstone of many biological processes and together they form various types of molecular machinery. A broad understanding of these protein complexes is crucial for revealing and building models of protein function and regulation. Pancreatic cancer is a highly lethal disease which is difficult to diagnose at early stage and even more difficult to cure. In this study, we applied a gradient clear native gel system combined with subsequent second-dimensional SDS-PAGE to separate protein complexes from cell lysates of SW1990 and PANC-1 pancreatic cancer cell lines with different degrees of differentiation. Ten heat-shock-protein- (HSP-) associated protein complexes were separated and identified, and the differentially expressed proteins related to cancers were also found, such as HSP60, protein disulfide-isomerase A4 (ERp72), and transitional endoplasmic reticulum ATPase (TER ATPase).

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

Identifying Functional Differences Between Protein S and Gas-6 in Pancreatic Cancer

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2571-2571
Author(s):  
Cosette Zacarias ◽  
Vijaya Satish Sekhar Pilli ◽  
William E. Plautz ◽  
A'drianne Wells ◽  
Rinku Majumder
Keyword(s):  
Cell Cycle ◽  
Pancreatic Cancer ◽  
Cell Proliferation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Biology ◽  
Pancreatic Cancer Cell ◽  
Protein S ◽  
Cancer Cell Lines ◽  
Cancer Cell Proliferation

Abstract Introduction: Procoagulants such as Factor IX and thrombin play major roles in cancer cell proliferation and migration; however, a role for anticoagulant proteins in cancer biology has not been elucidated. The anticoagulant Protein S (PS), its homologous protein Growth Arrest Specific protein-6 (GAS-6), and the receptors for these proteins, Tyro-3, Axl and Mertk (TAM), are over expressed in many cancer cells. TAM family receptors regulate functions such as cell survival, proliferation, migration, and apoptosis. The consequences of activation of each of these receptors varies, although the mechanism that leads to different outcomes is unknown. We hypothesized that the PS and GAS-6 ligands are responsible for the variations in the functions of these signaling cascades. Methods: We used qPCR to analyze the pancreatic cancer cell lines Miapaca-2 and Panc-1 for variations in the expression of GAS-6 and PS. We sequestered PS and GAS-6 with antibodies and used FACS analysis to detect effects on the cell cycle and on cell cycle regulators. Results: GAS-6 was observed to be highly expressed in proliferating Miapaca-2 cells compared with Panc-1 cells, whereas there was no significant difference in PS mRNA levels between these cell lines. For the cell line Miapaca-2, antibody sequestration of GAS-6 arrested the cell cycle in S-phase and increased p53 phosphorylation; conversely, inhibition of PS reduced p53 phosphorylation. Conclusion: Our results indicate that PS and GAS-6 act antagonistically in controlling pancreatic cancer cell proliferation, and we hypothesize that the ratio of GAS-6 to PS expression is key to this regulation. We will further confirm our hypothesis by overexpressing and knocking down PS and GAS-6 in the pancreatic cancer cell lines. Disclosures No relevant conflicts of interest to declare.

scholarly journals Novel three-dimensional cultures provide insights into thyroid cancer behavior

2020 ◽  
Vol 27 (2) ◽  
pp. 111-121 ◽  
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.

scholarly journals Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

2018 ◽  
Author(s):  
K. Yu ◽  
B. Chen ◽  
D. Aran ◽  
J. Charalel ◽  
A. Butte ◽  
...  
Keyword(s):  
Cancer Research ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Primary Tumor ◽  
Cancer Biology ◽  
Cancer Cell Lines ◽  
Primary Tumors ◽  
Tumor Types

AbstractCancer cell lines are commonly used as models for cancer biology. While they are limited in their ability to capture complex interactions between tumors and their surrounding environment, they are a cornerstone of cancer research and many important findings have been discovered utilizing cell line models. Not all cell lines are appropriate models of primary tumors, however, which may contribute to the difficulty in translating in vitro findings to patients. Previous studies have leveraged public datasets to evaluate cell lines as models of primary tumors, but they have been limited in scope to specific tumor types and typically ignore the presence of tumor infiltrating cells in the primary tumor samples. We present here a comprehensive pan-cancer analysis utilizing approximately 9,000 transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 different tumor types. After adjusting for tumor purity in the primary tumor samples, we performed correlation analysis and differential gene expression analysis between the primary tumor samples and cell lines. We found that cell-cycle pathways are consistently upregulated in cell lines, while no pathways are consistently upregulated across the primary tumor samples. In a case study, we compared colorectal cancer cell lines with primary tumor samples across the colorectal subtypes and identified three colorectal cell lines that were derived from fibroblasts rather than tumor epithelial cells. Lastly, we propose a new set of cell lines panel, the TCGA-110, which contains the most representative cell lines from 22 different tumor types as a more comprehensive and informative alternative to the NCI-60 panel. Our analysis of the other tumor types are available in our web app (http://comphealth.ucsf.edu/TCGA110) as a resource to the cancer research community, and we hope it will allow researchers to select more appropriate cell line models and increase the translatability of in vitro findings.

Deep learning for drug response prediction in cancer

2020 ◽  
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]

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

2014 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
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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