Comprehensive transcriptomic analysis of molecularly targeted drugs in cancer for target pathway evaluation.

(Last updated: 07/31/2015)

This website provides cancer researchers gene expression database and related analysis tools to evaluate potential mode-of-action of chemical compounds in comparison with clinically-used anticancer agents The website is provided by Screening Committee of Anticancer Drugs (SCADS) in Scientific Support Programs for Cancer Research, Grant-in-Aid for Scientific Research on Innovative Areas, Ministry of Education, Culture, Sports, Science and Technology, Japan.

Support page for Ushijima et al. "Gene expression database and related analysis tools for evaluation of anticancer compounds." is moved here.

Contents

[I] Anticancer drug gene expression database

Human colon adenocarcinoma HT-29 cells were treated with anticancer compounds at various concentrations for 6 or 16 h. Gene expression changes were analyzed with GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix).

  1. GENE EXPRESSION CHANGE DATA FOR ANTICANCER AGENTS
  2. INFORMATION OF COMPOUNDS

Clustering of compounds

Hierarchical cluster analysis based on the collection of gene signatures of 83 anticancer compounds (129 treatment samples). In total 4869 probe sets were used for clustering. The values in the heatmap are the logarithm of sample-to-control ratio of intensity values. Neither normalizing nor scaling was performed. Up- and down-regulated genes are colored in red and green, respectively.

Fig1

Hierarchical clustering analysis of the gene signatures of HT29 cells treated with 38 kinome-targeted drugs. In total 2458 probe sets were used.

Fig3

Up- or down-regulated genes in the clusters.

Mode of action up-regulated down-regulated
Classical DNA damaging agents
(16 compounds)
DNAdamage_up.csv DNAdamage_down.csv
HDAC inhibitors (5) HDACi_up.csv HDACi_down.csv
Tubulin binding agents (6) Tubulin_up.csv Tubulin_down.csv
proteasome inhibitors (5) proteasomei_up.csv proteasomei_down.csv
ER stress inducers (4) ERstress_up.csv ERstress_down.csv
Raf/MEK/ERK inhibitors (6) RAF_up.csv RAF_down.csv
PI3K/AKT/mTOR inhibitors (7)

PI3K_up.csv

PI3K_down.csv

 

[II] Connectivity scoring analysis

Based on our gene expression data of anticancer agents and the C-map algorithm (Lamb et al. 2006), we provide this calculation program (connectivity scoring analysis) to compare gene signatures of test compounds to those of antitumor agents in our database for prediction of their likely modes of action.

Online version is here

How to use:

  1. Prepare the list of probe set IDs of Affymetrix GeneChip HG-133Plus2.0 array in one column. The file format should be tab-separated text files as below.
  2. Select the files by clicking the buttons of up signature and down signature.
  3. Click the "execute" button.

Example of input files: GR033-up3-300.txt (up) GR033-down3-300.txt (down)

References