Cancer Res Treat.  2011 Dec;43(4):205-211.

Systems Biology Approaches to Decoding the Genome of Liver Cancer

• Lee JS
• Kim JH
• Park YY
• Mills GB

Abstract

Molecular classification of cancers has been significantly improved patient outcomes through the implementation of treatment protocols tailored to the abnormalities present in each patient's cancer cells. Breast cancer represents the poster child with marked improvements in outcome occurring due to the implementation of targeted therapies for estrogen receptor or human epidermal growth factor receptor-2 positive breast cancers. Important subtypes with characteristic molecular features as potential therapeutic targets are likely to exist for all tumor lineages including hepatocellular carcinoma (HCC) but have yet to be discovered and validated as targets. Because each tumor accumulates hundreds or thousands of genomic and epigenetic alterations of critical genes, it is challenging to identify and validate candidate tumor aberrations as therapeutic targets or biomarkers that predict prognosis or response to therapy. Therefore, there is an urgent need to devise new experimental and analytical strategies to overcome this problem. Systems biology approaches integrating multiple data sets and technologies analyzing patient tissues holds great promise for the identification of novel therapeutic targets and linked predictive biomarkers allowing implementation of personalized medicine for HCC patients.

Keyword

Oligonucleotide array sequence analysis; Gene expression profiling; Hepatocellular carcinoma; Genomics; Systems biology; Proteomics

MeSH Terms

Biomarkers
Breast
Breast Neoplasms
Carcinoma, Hepatocellular
Child
Clinical Protocols
Epidermal Growth Factor
Epigenomics
Estrogens
Gene Expression Profiling
Genome
Genomics
Humans
Precision Medicine
Liver
Liver Neoplasms
Oligonucleotide Array Sequence Analysis
Prognosis
Proteomics
Systems Biology
Epidermal Growth Factor
Estrogens

Figure

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  Fig. 1 Applications of microarray-based technology. HCC, hepatocellular carcinoma; CGH, genomic hybridization; RPPA, reversephase protein array.

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  Fig. 2 Integration of genomics and proteomics. Genomics data (expression, promoter methylation, and copy number of genes) or proteomics data (expression and posttranslational modification of proteins) alone provide too many candidate driver genes or proteins. Integrating these independently generated data from the same specimens greatly enhances the probability of identifying true driver genes or therapeutic targets.

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