Open Access Open Badges Research article

Deciphering causal and statistical relations of molecular aberrations and gene expressions in NCI-60 cell lines

Shyh-Dar Li2, Tatsuaki Tagami3, Ying-Fu Ho1 and Chen-Hsiang Yeang1*

Author Affiliations

1 Institute of Statistical Science, Academia Sinica, Academia Road, Sec 2, Taipei, Taiwan

2 Ontario Institute for Cancer Research, 101 College Street, Toronto, Canada

3 Nagoya City University, Nagoya, Japan

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BMC Systems Biology 2011, 5:186  doi:10.1186/1752-0509-5-186

Published: 4 November 2011



Cancer cells harbor a large number of molecular alterations such as mutations, amplifications and deletions on DNA sequences and epigenetic changes on DNA methylations. These aberrations may dysregulate gene expressions, which in turn drive the malignancy of tumors. Deciphering the causal and statistical relations of molecular aberrations and gene expressions is critical for understanding the molecular mechanisms of clinical phenotypes.


In this work, we proposed a computational method to reconstruct association modules containing driver aberrations, passenger mRNA or microRNA expressions, and putative regulators that mediate the effects from drivers to passengers. By applying the module-finding algorithm to the integrated datasets of NCI-60 cancer cell lines, we found that gene expressions were driven by diverse molecular aberrations including chromosomal segments' copy number variations, gene mutations and DNA methylations, microRNA expressions, and the expressions of transcription factors. In-silico validation indicated that passenger genes were enriched with the regulator binding motifs, functional categories or pathways where the drivers were involved, and co-citations with the driver/regulator genes. Moreover, 6 of 11 predicted MYB targets were down-regulated in an MYB-siRNA treated leukemia cell line. In addition, microRNA expressions were driven by distinct mechanisms from mRNA expressions.


The results provide rich mechanistic information regarding molecular aberrations and gene expressions in cancer genomes. This kind of integrative analysis will become an important tool for the diagnosis and treatment of cancer in the era of personalized medicine.