Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties

BMC Systems Biology

Christine Nardini¹  Suning Chen²  Valentina Devescovi¹  Yuanhua Liu¹ 

[1]Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

[2]First Affiliated Hospital of Suzhou University, Jiangsu Institute of Hematology, , Suzhou, China

关键词: NCI-60 cell panel;    Linear discriminant analysis;    Factor analysis;    Emergent property;    Multi-omic;   

Others  :  1143210
DOI  :  10.1186/1752-0509-7-14

 received in 2012-05-10, accepted in 2013-01-29,  发布年份 2013

【 摘 要 】

Background

High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.

Results

We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.

Conclusions

The integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.

【 授权许可】

   
2013 Liu et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

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