Predictive biomarker discovery and validation for the targeted chemotherapeutic ixabepilone

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 3011-3011 ◽  
Author(s):  
H. Lee ◽  
L. Xu ◽  
S. Wu ◽  
B. Paul ◽  
J. Baselga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Study ◽  
Cell Lines ◽  
Biomarker Discovery ◽  
Clinical Sample ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Predictive Biomarkers ◽  
Molecular Response ◽  
Clinical Markers

3011 Background: Ixabepilone is a microtubule stabilizing agent with significant therapeutic value in breast cancer (BC) patients. To identify predictive biomarkers capable of identifying patients likely to receive optimal benefit from ixabepilone treatment, preclinical and clinical studies were carried out. Several biomarkers discovered using preclinical models were validated in a neoadjuvant BC clinical study ( CA163080 ) and one, estrogen receptor 1 (ER), was shown to double the pathological complete response (pCR) rate in patients treated with ixabepilone. To identify candidate sets of biomarkers that could further increase the pCR rate we have performed post-hoc analyses of the preclinical and clinical data. Methods: Eighteen BC cell lines were classified as sensitive or resistant (S/R) based on the IC50 values for ixabepilone treatment. Gene expression profiling of the BC cell lines was conducted and genes correlated with the S/R classification were identified using a k-Nearest Neighbors algorithm. Patients in clinical study CA163080 underwent a pretreatment core needle biopsy from which RNA was isolated and gene expression profiles generated (data available on 134 patients). Analyses using the preclinical and clinical markers were conducted using various statistical tools. Results: Several markers used in combination with ER were found to be capable of tripling the pCR to ixabepilone in CA163080. In addition to ER other predictive markers were identified that were as predictive as ER, including several genes whose expression is anti-correlated with ER and are part of the ER pathway. Finally, various sub-group analyses were performed and manifested the importance of clinical sample variation that needs to be considered for the analysis. Conclusions: Several single biomarkers identified from preclinical studies were validated in the clinical study CA163080 , demonstrating the utility of this approach. Such markers can be used in combination to better identify patients likely to respond to ixabepilone in future clinical trials. Furthermore, molecular response markers that can be tied to the mechanism of drug resistance can be used for further developing chemotherapy in drug development. [Table: see text]

scholarly journals Revisiting inconsistency in large pharmacogenomic studies

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2333 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Mark Freeman ◽  
Nehme El-Hachem ◽  
Adrian She ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Targeted Drugs ◽  
Software Analysis ◽  
Sensitivity Data

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data.             Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs.             Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

scholarly journals Revisiting inconsistency in large pharmacogenomic studies

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2333 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Mark Freeman ◽  
Nehme El-Hachem ◽  
Adrian She ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Targeted Drugs ◽  
Software Analysis ◽  
Sensitivity Data

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

scholarly journals Revisiting inconsistency in large pharmacogenomic studies

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2333 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Mark Freeman ◽  
Nehme El-Hachem ◽  
Adrian She ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Targeted Drugs ◽  
Software Analysis ◽  
Sensitivity Data

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

P4-124: Comparison of gene expression profiles of prion protein-deficient and wild type neuronal cell lines of mice

2006 ◽  
Vol 2 ◽  
pp. S552-S552
Author(s):  
Boe-Hyun Kim ◽  
Jae-Il Kim ◽  
Eun-Kyoung Choi ◽  
Richard I. Carp ◽  
Yong-Sun Kim
Keyword(s):  
Gene Expression ◽  
Prion Protein ◽  
Cell Lines ◽  
Expression Profiles ◽  
Neuronal Cell ◽  
Gene Expression Profiles ◽  
Wild Type ◽  
Neuronal Cell Lines

scholarly journals cDNA expression array reveals heterogeneous gene expression profiles in three glioblastoma cell lines

Oncogene ◽  
1999 ◽  
Vol 18 (17) ◽  
pp. 2711-2717 ◽  
Author(s):  
Chang Hun Rhee ◽  
Kenneth Hess ◽  
James Jabbur ◽  
Maribelis Ruiz ◽  
Yu Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Glioblastoma Cell ◽  
Expression Array ◽  
Cdna Expression Array ◽  
Cdna Expression ◽  
Glioblastoma Cell Lines

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

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