Liver Cancer

Snorri S. Thorgeirsson

Joe W. Grisham

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, accounting for at least 600,000 deaths annually.¹ Although most frequent in southeast Asia and sub-Sahara Africa, the incidence and mortality rates of HCC have doubled in the United States and Europe in the past four decades and are expected to double again during the next 10 to 20 years.² As the incidence of HCC has increased, the age distribution of HCC has shifted toward relatively younger ages.² These observations make it clear that liver cancer is a major heath problem in the United States and Europe.

The etiologic agents responsible for the majority of HCC are known (e.g., infections with hepatitis B and C viruses [HBV and HCV], ethanol abuse), and additional causes are being identified (e.g., obesity, type 2 diabetes, nonalcoholic fatty liver disease).³ Furthermore, the liver diseases that are associated with increased risk of HCC and the cellular alterations that precede HCC have been identified.⁴,⁵,⁶ Research into the molecular pathogenesis of HCC currently focuses on the interrelationship of abnormal genomics and consequent alterations in molecular signaling pathways. Implicit in this research is the goal to integrate new data with clinicopathologic aspects of HCC in order to uncover new diagnostic, treatment, and prevention strategies.

Recent introduction of DNA microarray-based technologies makes it possible to measure simultaneously the expression of tens of thousands of genes in a tissue under a variety of conditions (reviewed in ref. 7). High-throughput microarray-based technologies and the recent advent of the next generation of whole genome DNA sequencers offer a unique opportunity to define the descriptive characteristics (i.e., “phenotype”) of a biological system in terms of the genomic readout (e.g., gene expression, coding mutations, insertions and deletions in DNA, copy number variations and chromosomal translocations). Integrated views of biological systems have caused a paradigm shift in biological research methods, that is, from classic reductionism to systems biology.⁸ Fundamental to the systems approach to the study of diseased biological systems is the hypothesis that disease processes are driven by aberrant regulatory networks of genes and proteins that differ from the normal counterparts. Application of multiparametric measurements promises to transform current approaches to diagnosis and therapy, providing the foundation for predictive and preventive personalized medicine.⁸

In this chapter we discuss the application of high-throughput genomic technologies to characterize HCC.

ALLELIC IMBALANCE IN LIVER CANCER

Chromosomal aberrations in tumors are regarded traditionally as evidence of gene deregulation and genome instability, and their detection may facilitate the identification of crucial genes and regulatory pathways that are perturbed in diseases. Several powerful analytical tools are currently available for analyzing chromosomal aberrations (reviewed in ref. 9). Comparative genomic hybridization (CGH), in particular array CGH, enables high-throughput analysis of DNA copy number and yields comprehensive information applicable to determining the molecular pathogenesis of human HCC. Meta-analysis of CGH studies of chromosome aberrations in human HCC shows that specific chromosomal gains and losses correlate with etiology and histologic grade (Table 18.1).¹⁰ In HCC the most frequent amplifications of genomic material involve 1q (57.1%), 8q (46.6%), 6p (22.3%), and 17q (22.2%), whereas losses are most common in 8p (38%), 16q (35.9%), 4q (34.3%), 17p (32.1%), and 13q (26.2%). Deletions of 4q, 16q, 13q, and 8p correlate with HBV infection and lack of HCV infection. Chromosomes 13q and 4q are significantly underrepresented in poorly differentiated HCC, and gains of 1q correlate with other high-frequency alterations.¹¹ Amplifications and deletions often occur on chromosome arms at sites of oncogenes (e.g., MYC on 8q24) and tumor suppressor genes (e.g., RB1 on 13q14), as well as at several loci that contain genes with known and/or
suspected oncogenic functions (e.g., FZD3, WISP1, SIAH-1, and AXIN2, all of which modulate the WNT signaling pathway). In this meta-analysis, etiology and poor differentiation of HCC correlated with specific genomic alterations. In preneoplastic dysplastic nodules (DNs), amplifications are most frequent in 1q and 8q, whereas deletions occur in 8p, 17p, 5p, 13q, 14q, and 16q.¹¹ Gain of 1q appears to be an early event developing in DN, possibly predisposing affected cells to acquire additional chromosomal aberrations.

TABLE 18.1 FREQUENCIES IN CHROMOSOMAL ABERRATION IN HUMAN HEPATOCELLULAR CARCINOMA^a

	p-Arm				q-Arm
Chromosome	Loss (%)	Gain (%)	High Frequency	Genes	Loss (%)	Gain (%)	High Frequency	Genes
1	15.4	5.2	—	—	0.6	57.1	q21.1-q44	WNT14, FASL
2	1.4	7.1	—	—	2.9	8	—	—
3	3.9	5	—	—	1.9	8.8	—	—
4	10.6	6	—	—	34.3	1.7	q21.1-q35	LEF1, CCNA
5	1.7	13.6	—	—	7.8	11.1	—	—
6	1	22.3	—	PIM1, CDKN1A	15	7.9	—	—
7	0.9	15	—	—	3.1	16.8	—	—
8	38	4.6	p21.1-p22	FZD3, PLK3	1.9	46.6	q22.1-q24.3	MYC, WISP1
9	14	3.3	—	—	11.1	2.9	—	—
10	2.7	8.3	—	—	11.1	4.1	—	—
11	5.4	4.3	—	—	10.2	9.4	—	—
12	6.5	2.4	—	—	2.9	6.9	—	—
13	0	0	—	—	26.2	7.4	q14.1-q22	RB1, BRCA3
14	0	0	—	—	11.3	4.1	—	—
15	0	0	—	—	5.4	4.6	—	—
16	16.8	3.4	—	—	35.9	1.8	q12.1-q24	SIAH1, CDH1
17	32.1	2.9	p13	p53, HIC1	3.7	22.2	q23-q25	AXIN2, TIMP2
18	4.1	5.5	—	—	10.8	5	—	—
19	6.9	5	—	—	3.8	10.4	—	—
20	2	14.9	—	—	0.9	18.6	—	—
21	0	0	—	—	8.8	2.2	—	—
22	0	0	—	—	6.4	2.8	—	—
X	5	11.2	—	—	4.5	15	—	—
Y	5.1	2.3	—	—	5.6	2.3	—	—
^aSummary of 785 different comparative genomic hybridization analyses of human hepatocellular carcinoma. Frequencies more than 20% are highlighted in bold. Region of highest frequency of imbalance on respective chromosomal arms are highlighted in bold. Examples of known tumor-relevant genes located on respective chromosomal high-frequency region are shown.
(Modified from ref. 10 , with permission.)

Bioinformatic analysis of CGH data was recently used to develop a progression model for human HCC.¹¹ Based on an evolutionary tree constructed from statistically significant CGH events, three subgroups of patients with different patterns of HCC progression were identified. The subgroups reflect the extent of tumor progression as indicated by the number of chromosomal aberrations, tumor stage, tumor size, and disease outcome. Gains of 1q21-23 and 8q22-24 appear to be early genomic events in development of HCC and gain of 3q22-24 a late genomic event, the latter associated with tumor recurrence and poor survival. The HCC progression model uncovered chromosomal imbalances associated with clinical pathologic characteristics of the disease and explained a significant part of the variations in clinical outcome among the HCC patients.¹¹

These two studies illustrate the power of CGH analysis to identify the functional significance of genomic alterations in human HCC. Nevertheless, because CGH only analyzes genomic DNA, additional studies are required to measure and integrate data on global gene expression to confirm the roles of candidate genes. This can be accomplished by adapting the expression imbalance
map method and array CGH analysis (reviewed in ref. 12). This approach was recently applied to human HCC.¹³ Using regional pattern recognition approaches, the authors discovered the most probable copy number-dependent regions and 50 potential driver genes (Table 18.2). At each step of the gene selection process, the functional relevance of the selected genes was evaluated by estimating the prognostic significance of the selected genes. Further validation using small interference RNA-mediated knockdown experiments showed proof-of-principle evidence for the potential driver roles of the genes in HCC progression (i.e., NCSTN and SCRIB). In addition, systemic prediction of drug responses implicated the association of the 50 genes with specific signaling molecules (mTOR, AMPK, and EGFR). It was concluded that the application of an unbiased and integrative analysis of multidimensional genomic data sets can effectively screen for potential driver genes and provides novel mechanistic and clinical insights into the pathobiology of HCC.

It seems inevitable that new and improved array designs for both CGH and gene expression and the advent of whole genome sequencing combined with better software for statistical analysis of the data will continue to emerge.

CLASSIFICATION AND PROGNOSTIC PREDICTION OF HEPATOCELLULAR CARCINOMA

The application of microarray technologies to characterize tumors on the basis of global gene expression has had a significant impact on both basic and clinical oncology.⁷ The goal of tumor microarray studies generally includes discovery of subsets of tumors (class discovery), which enables diagnostic classification (class comparison), prediction of clinical outcome (class prediction), and mechanistic analysis. Verification and validation of the primary results are essential for discovery of oncogenic pathways and identification of therapeutic targets (for technical details on microarray analysis see Chapter 2

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