scholarly journals Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

2020 ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

SUMMARYHuman cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ∼17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. We further demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our predictions could have clinical relevance for patients’ care.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell Lines

2020 ◽  
Author(s):  
YuanYuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background: human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results: in this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions: we demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our predictions could have clinical relevance for patients’ care.

Regulation of DNA repair gene expression in human cancer cell lines

2006 ◽  
Vol 97 (5) ◽  
pp. 1121-1136 ◽  
Author(s):  
Claire J. McGurk ◽  
Michele Cummings ◽  
Beate Köberle ◽  
John A. Hartley ◽  
R. Timothy Oliver ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Repair ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Repair Gene ◽  
Human Cancer Cell Lines ◽  
Dna Repair Gene

Do wine polyphenols modulate p53 gene expression in human cancer cell lines?

2001 ◽  
Vol 34 (5) ◽  
pp. 415-420 ◽  
Author(s):  
George J Soleas ◽  
David M Goldberg ◽  
Linda Grass ◽  
Michael Levesque ◽  
Eleftherios P Diamandis
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
P53 Gene ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Wine Polyphenols

scholarly journals Identification of candidate predictive markers of anticancer drug sensitivity using a panel of human cancer cell lines

2003 ◽  
Vol 94 (12) ◽  
pp. 1074-1082 ◽  
Author(s):  
Shingo Dan ◽  
Mieko Shirakawa ◽  
Yumiko Mukai ◽  
Yoko Yoshida ◽  
Kanami Yamazaki ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Predictive Markers ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines

scholarly journals P11.20 Assessing TTFields-response and associated gene expression in various human cancer cell lines

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii46-iii47
Author(s):  
A Kinzel ◽  
G Lavy-Shahaf ◽  
M Giladi ◽  
R Schneiderman ◽  
K Gotlib ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Synergistic Effects ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Optimal Frequency ◽  
Human Cancer Cell Lines ◽  
Cell Counts

Abstract BACKGROUND Various cancer cell lines were reported to be affected in an inhibitory manner of varying magnitude by tumor treating fields (TTFields). Here, we aimed to detect response markers for TTFields treatment by analyzing specific properties of cell lines according to their response pattern to these alternating electric fields of intermediate frequency and low intensity. MATERIAL AND METHODS We treated 45 cell lines of diverse types of human cancer with TTFields at their specific optimal frequency and equal nominal intensity of 1.7 V/cm for 72 h. In addition to investigating cytotoxicity and clonogenic potential, we used the Cancer Cell Line Encyclopedia (CCLE) database for further analysis: First, to functionally examine patterns of differentially expressed genes or mutations associated with response to TTFields; and second, to compare sensitivity to TTFields using pharmacological profiling (CCLE). RESULTS TTFields had a cytotoxic effect on tested cell lines of 50 % on average (range: 14–86% reduced cell counts), whereas the clonogenic effect varied between no effect and 88 % reduction in the number of colonies. With regard to differential gene expression and mutation analysis, our analysis detected upregulated pathways associated with migration, DNA damage repair response, oxidative stress, and hypoxia. Further, cells identified as having a better response to TTFields were also more sensitive to lapatinib, PHA-665752 and PLX-4720. CONCLUSION In this study, we determined the optimal frequency for maximum response to TTFields in numerous human cancer cell lines. Our results argue strongly for a vast effectiveness of TTFields treatment in cancer cells, and synergistic effects in combination with other therapeutic agents might be revealed in future studies using pharmacological profiling. Beyond that, further research is needed on the role of identified response-associated mutations.

SRCgene expression in human cancer: the role of transcriptional activation

2004 ◽  
Vol 82 (2) ◽  
pp. 263-274 ◽  
Author(s):  
Scott M Dehm ◽  
Keith Bonham
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Tyrosine Kinase ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cellular Proliferation ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Src Gene ◽  
Src Activation

Human pp60c-Src(or c-Src) is a 60 kDa nonreceptor tyrosine kinase encoded by the SRC gene and is the cellular homologue to the potent transforming v-Src viral oncogene. c-Src functions at the hub of a vast array of signal transduction cascades that influence cellular proliferation, differentiation, motility, and survival. c-Src activation has been documented in upwards of 50% of tumors derived from the colon, liver, lung, breast, and pancreas. Therefore, a major focus has been to understand the mechanisms of c-Src activation in human cancer. Early studies concentrated on post-translational mechanisms that lead to increased c-Src kinase activity, which often correlated with overexpression of c-Src protein. More recently, the discovery of an activating SRC mutation in a small subset of advanced colon tumors has been reported. In addition, elevated SRC transcription has been identified as yet another mechanism contributing significantly to c-Src activation in a subset of human colon cancer cell lines. Interestingly, histone deacetylase (HDAC) inhibitors, agents with well-documented anti-cancer activity, repress SRC transcription in a wide variety of human cancer cell lines. Analysis of the mechanisms behind HDAC inhibitor mediated repression could be utilized in the future to specifically inhibit SRC gene expression in human cancer.Key words: c-Src, tyrosine kinase, gene expression, transcription, colon cancer.

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