Gene expression database and related analysis tools for evaluation of anticancer compounds.

(Last updated: 11/07/2012)

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.


[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).


Clustering of compounds

Hierarchical cluster analysis based on the collection of gene signatures of 35 anticancer compounds (55 treatment samples). In total 3237 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.


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
mTOR inhibitors (5) mTORi_up.csv mTORi_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


[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 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)


[III] KEGG pathway analysis

Sorry, only a source code of R program is available now.
GUI version is under construction.

[IV] R source code and related files for experts

Connectivity scoring analysis

Data and program for download:


> source("Cscore_analysis.R")
> cscore_analysis(file.up, file.down, file.out)

Input file format (file.up, file.down):

List of the probeset IDs of Affymetrix HG-U133Plus2.0 array in one column

Kegg pathway analysis

This program depends on the BioConductor packages as follows:

Program for download:


> source("KEGG_analysis.R")
> KEGG_analysis(, file.out)

Input file format:

List of the probeset IDs of Affymetrix HG-U133Plus2.0 array in one column