[논문]Gene Expression Signatures for Compound Response in Cancers

He, Ningning; Yoon, Suk-Joon

doi:10.5808/gi.2011.9.4.173

Gene Expression Signatures for Compound Response in Cancers 원문보기

Genomics & informatics, v.9 no.4, 2011년, pp.173 - 180

He, Ningning (Department of Biological Sciences, Sookmyung Women's University) , Yoon, Suk-Joon (Department of Biological Sciences, Sookmyung Women's University)

Abstract ▼ AI-Helper

Recent trends in generating multiple, large-scale datasets provide new challenges to manipulating the relationship of different types of components, such as gene expression and drug response data. Integrative analysis of compound response and gene expression datasets generates an opportunity to capture the possible mechanism of compounds by using signature genes on diverse types of cancer cell lines. Here, we integrated datasets of compound response and gene expression profiles on NCI60 cell lines and constructed a network, revealing the relationship for 801 compounds and 341 gene probes. As examples, obtusol, which shows an exclusive sensitivity on a small number of colon cell lines, is related to a set of gene probes that have unique overexpression in colon cell lines. We also found that the SLC7A11 gene, a direct target of miR-26b, might be a key element in understanding the action of many diverse classes of anticancer compounds. We demonstrated that this network might be useful for studying the mechanisms of varied compound response on diverse cancer cell lines.

주제어

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문제 정의

It is possible to obtain a certain number of significant correlations between gene expression and compound response on subsets of cancer samples. In this research, our goal is to create a global network including all known compound data and gene expression profiles, and to subtract specific networks of genes and compounds revealing unique mechanisms of compound action.

제안 방법

Pearson’s correlation coefficient (PCC) and its p-value between compounds and genes were calculated using drug response and gene expression data on 59 NCI60 cell lines.
A large-scale correlation study between compound response and gene expression was carried out using NCI60 cell lines. The relationship was analyzed on a network which represents similarity of GI50s of a compound and fold change of a gene probe over 59 cell lines (Fig. 3). Totals of 801 compounds and 341 gene probes had at least one counterpart with a significantly high correlation (PCC＞0.
To further explore this network, we picked up two sub-networks to examine the correlations between the involved components. Firstly, as shown in the Fig.
They showed a significant response (－logGI50 ＜5) on at least one cell lines and demonstrated varied response on 60 cell lines with missing data on less than 5 cell lines. We carried out a clustering analysis of GI50 data for these 6,652 compounds over 60 cell lines (Fig. 1A). Three major patterns were observed in the compound classification: sensitive or resistant responses on most cell lines, and some compounds exhibiting both sensitive and resistant responses in a cell line-dependent manner.

대상 데이터

NCI60 gene expression data generated using Affymetrix u133A/B microarrays and processed using the MAS5 normalization algorithm (Zhou and Rocke, 2005). Data for a total of 44,928 gene probes screened on 59 cell lines (data missing on NCI-H23 cell line) were retrieved. Gene probes with a standard deviation of ＞2 across all 59 cell lines and a foldchange of ＞4 over the median value on at least one cell line were selected for the further analysis.
Data for a total of 44,928 gene probes screened on 59 cell lines (data missing on NCI-H23 cell line) were retrieved. Gene probes with a standard deviation of ＞2 across all 59 cell lines and a foldchange of ＞4 over the median value on at least one cell line were selected for the further analysis.
gov). The GI50 data released in August 2004 was used in this study. They provided more than 40,000 compounds with a GI50 value to characterize compound sensitivity to diverse NCI60 cancer cell lines.

이론/모형

, 2009). Hierarchical clustering of compound and gene expression data was carried out using complete linkage algorithm (Williams et al., 1976) (http://bonsai.hgc.jp/~mdehoon/software/cluster/). Tree Viewer was used to visualize the clustered data (http://www.
NCI60 gene expression data was also downloaded from the DTP website. NCI60 gene expression data generated using Affymetrix u133A/B microarrays and processed using the MAS5 normalization algorithm (Zhou and Rocke, 2005). Data for a total of 44,928 gene probes screened on 59 cell lines (data missing on NCI-H23 cell line) were retrieved.
A critical question remaining is how to interpret the network and take out the new biological insights or knowledge. Our approach to this question is to calculate the simple Pearson correlation coefficient (PCC) (Liu et al., 2007) (Jobson et al., 2007) of drug response and gene expression profile over diverse NCI60 cell lines. It is possible to obtain a certain number of significant correlations between gene expression and compound response on subsets of cancer samples.

성능/효과

In conclusion, gene expression profiles reveal lineagespecific clustering patterns, indicating that gene signatures effectively represent the distinct biological features in the lineage-based analysis of cell lines. In the other hand, the compound response data shows relatively complicated patterns in the lineage clustering, indicating that the compound response is the outcome of complicated mechanisms beyond lineage-specific features.

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