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. 2019 Feb 22;10(2):171.
doi: 10.3390/genes10020171.

IPCT: Integrated Pharmacogenomic Platform of Human Cancer Cell Lines and Tissues

Muhammad Shoaib  1   2 , Adnan Ahmad Ansari  3   4 , Farhan Haq  5 , Sung Min Ahn  6   7   8
Affiliations

IPCT: Integrated Pharmacogenomic Platform of Human Cancer Cell Lines and Tissues

Muhammad Shoaib et al. Genes (Basel). .

Abstract

: (1) Motivation: The exponential increase in multilayered data, including omics, pathways, chemicals, and experimental models, requires innovative strategies to identify new linkages between drug response information and omics features. Despite the availability of databases such as the Cancer Cell Line Encyclopedia (CCLE), the Cancer Therapeutics Response Portal (CTRP), and The Cancer Genome Atlas (TCGA), it is still challenging for biologists to explore the relationship between drug response and underlying genomic features due to the heterogeneity of the data. In light of this, the Integrated Pharmacogenomic Database of Cancer Cell Lines and Tissues (IPCT) has been developed as a user-friendly way to identify new linkages between drug responses and genomic features, as these findings can lead not only to new biological discoveries but also to new clinical trials. (2) Results: The IPCT allows biologists to compare the genomic features of sensitive cell lines or small molecules with the genomic features of tumor tissues by integrating the CTRP and CCLE databases with the REACTOME, cBioPortal, and Expression Atlas databases. The input consists of a list of small molecules, cell lines, or genes, and the output is a graph containing data entities connected with the queried input. Users can apply filters to the databases, pathways, and genes as well as select computed sensitivity values and mutation frequency scores to generate a relevant graph. Different objects are differentiated based on the background color of the nodes. Moreover, when multiple small molecules, cell lines, or genes are input, users can see their shared connections to explore the data entities common between them. Finally, users can view the resulting graphs in the online interface or download them in multiple image or graph formats. (3) Availability and Implementation: The IPCT is available as a web application with an integrated MySQL database. The web application was developed using Java and deployed on the Tomcat server. The user interface was developed using HTML5, JQuery v.3.1.0 , and the Cytoscape Graph API v.1.0.4. The IPCT can be accessed at http://ipct.ewostech.net. The source code is available at https://github.com/muhammadshoaib/ipct.

Keywords: cell lines; database; drug sensitivity; genomics; pharmacogenomics.

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Conflict of interest statement

The authors declare no conflict of interest

Figures

Figure 1
Figure 1
Entities in the IPCT database.
Figure 2
Figure 2
Connectivity map of CTRP drugs and CCLE cell lines with ChEMBL, REACTOME, the Expression Atlas, and cBioPortal.
Figure 3
Figure 3
IPCT output for small molecule user query lapatinib. The graph shows all data points connected with lapatinib. Yellow nodes represent small molecules; blue nodes show cell lines sensitive to lapatinib; sky-blue nodes represent significant genes (those with multiple genomic aberrations); green and red nodes represent genes that are up-regulated and down-regulated in the sensitive cell lines, respectively; light green and light red represent the amplified and deleted genes in the sensitive cell lines, respectively; white nodes represent mutated genes; and orange nodes represent the REACTOME pathways of mutated genes.
Figure 4
Figure 4
ERBB4’s genetic profile in real tumors extracted from cBioPortal. (A) ERBB4’s differential expression in different cancer studies. (B) ERBB4’s mutation and copy number alteration frequency in different cancer studies.
Figure 5
Figure 5
IPCT output for small molecule user query lapatinib, sorafenib, gefitinib, and sunitinib after disabling the REACTOME and Expression Atlas databases and enabling cell lines and mutated genes only. (A) Gene filter = only cancer genes. (B) Gene filter = exclude common mutations.
Figure 6
Figure 6
IPCT output for small molecule user query lapatinib and afatinib. The graph shows all data points connected with lapatinib and afatinib. Yellow nodes represent small molecules; blue nodes show cell lines sensitive to lapatinib and afatinib; sky-blue nodes represent significant genes (those with multiple genomic aberrations); green and red nodes represent genes that are up-regulated and down-regulated in the sensitive cell lines, respectively; light green and light red represent the amplified and deleted genes in the sensitive cell lines, respectively; white nodes represent mutated genes; and orange nodes represent the REACTOME pathways of mutated genes.
Figure 7
Figure 7
IPCT output for small molecule user query lapatinib and afatinib with the shared connection filter enabled. The graph shows all data points connected with lapatinib and afatinib. Yellow nodes represent small molecules; blue nodes show cell lines sensitive to lapatinib and afatinib; sky-blue nodes represent significant genes (those with multiple genomic aberrations); green and red nodes represent genes that are up-regulated and down-regulated in the sensitive cell lines, respectively; light green and light red represent the amplified and deleted genes in the sensitive cell lines, respectively; white nodes represent mutated genes; and orange nodes represent the REACTOME pathways of mutated genes.
Figure 8
Figure 8
IPCT output for small molecule user query lapatinib and afatinib with the shared connection filter and the relationship filter enabled. The graph shows all data points connected with lapatinib and afatinib. Yellow nodes represent small molecules; blue nodes show cell lines sensitive to lapatinib and afatinib; and sky-blue nodes represent significant genes (those with multiple genomic aberrations).
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