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Development of liver cancers is driven largely by genomic alterations that deregulate signaling pathways, influencing growth and survival of cancer cells. Because of the hundreds or thousands of genomic/epigenomic alterations that have accumulated in the cancer genome, it is very challenging to find and test candidate genes driving tumor development and progression. Systematic studies of the liver cancer genome have become available in recent years. These studies have uncovered new potential driver genes, including those not previously known to be involved in the development of liver cancer. Novel approaches combining multiple datasets from patient tissues have created an unparalleled opportunity to uncover potential new therapeutic targets and prognostic/predictive biomarkers for personalized therapy that can improve clinical outcomes of the patients with liver cancer.
The development of hepatocellular carcinoma (HCC) is a complex process, and HCC arises from the accumulation of multiple genetic alterations leading to changes in the genomic landscape. Current advances in genomic technologies have revolutionized the search for genetic alterations in cancer genomes. Recent studies in which all coding exons in HCC were sequenced have shed new light on the genomic landscape of this malignant disease. Catalogues of these somatic mutations and systematic analysis of catalogued mutations will lead us to uncover candidate HCC driver genes, although further functional validation is needed to determine whether these genes play a causal role in the development of HCC. This review provides an overview of previously known oncogenes and new oncogene candidates in HCC that were uncovered from recent exome or whole-genome sequencing studies. This knowledge provides direction for future personalized treatment approaches for patients with HCC. (Clin Mol Hepatol 2015;21:220-229)
The Cancer Genome Atlas (TCGA) has compiled genomic, epigenomic, and proteomic data from more than 10,000 samples derived from 33 types of cancer, aiming to improve our understanding of the molecular basis of cancer development. Availability of these genome-wide information provides an unprecedented opportunity for uncovering new key regulators of signaling pathways or new roles of pre-existing members in pathways. To take advantage of the advancement, it will be necessary to learn systematic approaches that can help to uncover novel genes reflecting genetic alterations, prognosis, or response to treatments. This minireview describes the updated status of TCGA project and explains how to use TCGA data.
The Cancer Genome Atlas (TCGA) has compiled genomic, epigenomic, and proteomic data from more than 10,000 samples derived from 33 types of cancer, aiming to improve our understanding of the molecular basis of cancer development. Availability of these genome-wide information provides an unprecedented opportunity for uncovering new key regulators of signaling pathways or new roles of pre-existing members in pathways. To take advantage of the advancement, it will be necessary to learn systematic approaches that can help to uncover novel genes reflecting genetic alterations, prognosis, or response to treatments. This minireview describes the updated status of TCGA project and explains how to use TCGA data. [BMB Reports 2016; 49(11): 607-611]
All cancers arise as a result of accumulated genetic and epigenetic alterations. Therefore, analyses of cancer genome sequences and structures provide insights for understanding cancer biology, diagnosis and therapy. The application of microarray or second- generation sequencing technologies is allowing substantial advances in cancer genomics. Thus, our understanding of the complexity of cancer has significantly increased through large-scale genomic studies from large collaborations such as the International Cancer Genome Consortium (ICGC http://www.icgc.org/) and The Cancer Genome Atlas (TCGA http://cancergenome.nih.gov/). However, the translation of these data sets into clinically actionable information is still in its infancy; nevertheless, insights from sequencing studies have led to the discovery of a variety of novel diagnostic and prognostic biomarkers and potentially actionable therapeutic targets. Here, I will present recent development of cancer genomics in liver cancer and discuss what the new findings have taught us about cancer biology and, more importantly, how these new findings guide more effective diagnostic and treatment strategies in liver cancer.
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.
Recent massive sequencing studies of HCC genomes revealed many new genetic alterations that might be accountable for HCC development and provided comprehensive view of malignant disease. However, genomic profiling of tumors is limited by a loose correlation between genetic alterations and their functional products such as proteins and metabolites. To overcome such limitation, several approaches such as proteomics and metabolomics have been developed to add more functional information to genomic characteristics of tumors. Reverse-phase protein array (RPPA) is one of such approaches and allows us to simultaneously measure multiple protein features, such as expression, modification of proteins, and interaction with ligands from the samples. By integrating multiple data sets from same tissues, several clinically distinct subtypes were identified and further analysis of integrated data revealed characteristics of underlying biology that may dictate clinical outcomes of each subtype. Further analysis showed that proteomic subtype is more correlated with copy number alterations than somatic mutations. Integration of multiple data sets also identified many genetic and proteomic alterations significantly correlated with clinical outcomes. Functional validation with cell lines demonstrated that some of correlated genes are essential for growth and survival of HCC cells. In conclusion, HCC can be classified into distinct subtypes by proteomic features independent of mutation profile. Proteomic analysis has identified potential key biomarkers with prognostic importance that can be easily translated to clinics. Current study demonstrated merit of integrated analysis of proteomic data with genomic data to uncover potential driver genes of HCC development.