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Journal of Skin Cancer
Volume 2011 (2011), Article ID 541405, 16 pages
http://dx.doi.org/10.1155/2011/541405
Review Article

Deciphering Squamous Cell Carcinoma Using Multidimensional Genomic Approaches

1Department of Integrative Oncology, British Columbia Cancer Agency Research Centre, Vancouver BC, Canada V5Z 1L3
2Department of Pediatrics, Center for Molecular and Human Genetics, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA

Received 2 August 2010; Accepted 26 October 2010

Academic Editor: S. Ugurel

Copyright © 2011 Ewan A. Gibb et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

1. Introduction

Squamous cell carcinoma (SqCC) initiation and development is characterized by the accumulation of genetic alterations. Over the past two decades, technological advances used to identify these alterations have expanded from single-gene queries to genome-wide methods of analysis in a multitude of dimensions, including genomics, epigenomics, and transcriptomics. Mutations and copy number alterations at the DNA level represent genomic alterations, while aberrant methylation patterns and histone modifications reflect epigenetic changes. Gene expression changes manifest either as a direct consequence of genetic and epigenetic alterations or as reactive changes and downstream effects. Furthermore, transcription patterns are also mediated by noncoding RNAs such as microRNAs (miRNAs), which can be deregulated by the aforementioned genetic and epigenetic alterations. The global profiling of each omics dimension represents a remarkable technological and bioinformatic achievement. However, the integration of these individual dimensions must be pursued in order to provide a comprehensive view of the impact of gene disruption in SqCC. Hence, there is a growing need to merge these multiple dimensions of data to identify concerted and complementary alterations that lead to the perturbation of oncogenic pathways and gene networks.

2. Multidimensional Analysis of Cancer Genomes

2.1. Genomic Alterations in Cancer

Genome destabilization is one of the hallmarks of cancer and is reflected by the accumulation of multiple genetic alterations such as chromosomal translocation, DNA copy number alteration, and sequence mutation [1]. Chromosomal translocation has been shown activate oncogenes by gene fusion [24]. Segmental duplication and amplification leads to increased gene dosage and, often, inappropriate expression of oncogenes [5] (Figure 1). Deletion leads to loss of tumor suppressor function through either haploinsufficiency or two-hit inactivation of functional alleles [6, 7]. Examples of such two-hit mechanisms are homozygous deletion or a combination of deletion and gene mutation. DNA mutation can lead to a variety of effects, such as constitutive gene activation or inactivation. A variety of technology platforms have been developed that are tailored to the detection of specific types of genomic alterations. These technologies are summarized in Table 1.

tab1
Table 1: Genome-wide methods for identifying genetic alterations.
fig1
Figure 1: Mechanisms of DNA copy number alteration. (a) Segmental gains and losses can lead to DNA copy number alterations. (b) Allelic imbalance and loss of heterozygosity (LOH) can arise from a deletion event or gene conversion during mitosis.
2.2. Epigenomics of Cancer

Epigenetics is the study of heritable chromosomal changes that influence transcription but do not directly alter the DNA sequence [24]. Mechanisms of epigenetic gene regulation include DNA methylation and histone modification. Altered epigenetic regulation in cancer includes hypo- and hypermethylation of genes, which can lead to increased genomic instability and the activation of oncogenes or the silencing of tumor suppressor genes, respectively. Similarly, the modification of histones can modulate chromatin structure and accessibility (Figure 2). The genome-wide alignment of epigenetic aberrations with genetic alterations can lead to the identification of genes disrupted in both alleles. For example, in the two-hit model one allele could be lost to deletion, while the other copy is silenced by hypermethylation. These events can in turn lead to changes in gene expression levels [2527]. A wide range of methodologies exists for the determination of methylation status, which rely on bisulfite conversion of cytosines [2832], immunoprecipitation of methylated DNA [33, 34], or the sensitivity of endonucleases to DNA methylation [35, 36]. The technologies in methylome analysis are summarized in Table 2 [27]. Histone modification analysis is complex due to the variety of modifications and the different residues being modified. Common methods of assessment such as ChIP-chip and ChIP-seq involve immunoprecipitation using antibodies against specific types of modification and are the subject of recent review articles [3739].

tab2
Table 2: Techniques for genome-wide analysis of the methylome.
541405.fig.002
Figure 2: Mechanisms of epigenetic gene silencing. Unmethylated DNA is accessible to activator proteins and transcription factors (TF) enabling transcription. DNA methylation by DNA methyl transferases (DNMT) causes steric inhibition of transcription complexes thus blocking transcription. Methylated DNA (me) is recognized by methyl binding domain proteins (MBD), histone deacetylases (HDAC), and histone methyltransferases (HMT) which stimulate chromatin remodeling. Compaction of DNA into condensed chromatin (heterochromatin) results in transcriptionally inactive DNA.
2.3. Gene Expression Profiling

The downstream effect of genomic and epigenomic alterations is the dysregulation of gene expression. Global gene expression profiling has been instrumental in both disease classification and cancer gene discovery [4954], as well as in biomarker discovery in a wide range of cancers [5557]. The evolution of gene expression profiling technologies and their applications to cancer biology is well documented in the literature [58, 59]. While microarrays are widely used for expression profiling, sequence-based approaches such as serial analysis of gene expression (SAGE) present a digital alternative to measuring transcript abundance [60]. More recently, the emergence of massively parallel and next-generation sequencing platforms has revolutionized whole transcriptome analysis [2023]. Expression changes are not limited to protein coding genes; alterations in miRNA levels have been well documented in various cancer types, discussed below.

2.4. Noncoding RNAs

Approximately 90% of the human genome is transcribed, with only a small fraction of these transcripts representing protein coding mRNAs [6164]. The remaining transcripts are comprised of a wide range of noncoding RNAs (ncRNAs) including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), miRNAs, and a recently described class, long noncoding RNAs (lncRNAs) [65].

MicroRNAs are small ~22 nucleotide (nt) RNAs involved in posttranscriptional silencing of mRNA targets [66]. These regulatory RNAs exhibit an enormous influence on most fundamental biological processes, altering the expression of proteins by inhibiting translation or promoting mRNA degradation [67]. Aberrant miRNA expression has been linked to a range of diseases, including cancer progression and prognosis [68, 69]. Many of the mechanisms described to alter normal gene expression in cancer can also influence the expression of miRNAs. For instance, the gain or loss of a specific miRNA can function as either an oncogene or a tumor suppressor [7072], and mutations in miRNA sequences or miRNA processing machinery can have a tremendous impact on miRNA regulatory function [73]. Intriguingly, the expression of miRNAs can also be regulated by epigenetic mechanisms [74], while in turn, miRNA expression can modulate epigenetic regulation by targeting enzymes responsible for histone modification and DNA methylation [75, 76].

More recently, lncRNAs have been described to function as epigenetic regulators of transcription, chromatin remodeling and cellular development [65, 77, 78]. While the extent of their involvement in tumorigenesis is unknown, the dysregulation of lncRNAs may prove to be yet another level of complexity in the cancer genomic landscape.

In combination with genomic, epigenomic, and transcriptomic alterations, the capacity of ncRNAs to influence the expression of a range of biological processes highlights the need to merge the multiple dimensions of whole genome analysis. Integrative genomics is the response to this requirement as highlighted below.

2.5. Integrative Genomics

Until recently, the study of cancer genomes, epigenomes, and transcriptomes has largely been done in a solitary manner. With technologies to assess these different dimensions becoming more accessible (Figure 3), the integration of these diverse data dimensions in parallel is the next logical step. In fact, large-scale initiatives, such as those led by The Cancer Genome Atlas (TCGA) Research Network (http://cancergenome.nih.gov/), are proposing the comprehensive characterization of genomic, epigenomic, and transcriptomic alterations for a multitude of cancer types.

541405.fig.003
Figure 3: Multidimensional omics data integration using SIGMA2 software. Combined genetic, epigenetic and gene expression analysis of cancer samples facilitates identification of oncogenes and tumor suppressor genes which are concertedly disrupted. (a) Examples of annotation tracks. (b) Copy number profile from array CGH experiment—a focal DNA amplification of a region on 17q12 is highlighted in yellow. (c) Allelic status (SNP array). This region is also encompassed in a large stretch of allelic imbalance. Blue horizontal bars indicate loci that become homozygous (loss of heterozygosity) in the tumor sample. (d) DNA methylation analysis (MeDIP-microarray) shows a concurrent loss of methylation, as indicated by a peak shifted to left of the center line. (e) Heat map summary of gene expression profile in the region of interest. The gene boxed in blue on the heatmap is ERBB2. It shows the highest level of differential expression between the tumor line and a panel of normal tissue samples. (f) The histogram displays the relative expression of the tumor sample as compared with the normal samples for ERBB2 (i.e., expanded view from heat map).

While the majority of previous integrative studies have focused on correlating copy number alterations with gene expression changes, there have been a few more recent studies which have incorporated the epigenomic dimension as well [7983]. Moreover, from these studies, it is clear that both quantitative and qualitative benefits are reaped when employing a Multidimensional approach. For example, the abilities to (i) associate more of the observed aberrant gene expression to genetic and epigenetic alterations (ii) identify complementary alterations in different samples which lead to similar downstream effects, and (iii) elucidate complex disruption patterns of both known and novel signaling pathways are some of the key findings that have been made. However, a consequence of assessing the cancer cell at a high resolution is the generation of copious amounts of data and the subsequent need for specialized bioinformatic tools [84]. As a result, there have been a number of recently developed tools to address this need. Table 3 lists these tools and where they can be obtained.

tab3
Table 3: Software for integrative analysis of Multidimensional omics data.

3. Applying Integrative Genomics to Squamous Cell Carcinoma

The unique biology of SqCCs arising from different tissues is reflected in their underlying genetic variability. Here we examine the use of multiple dimensions of omics data in elucidating the underlying genetic features of three common types of SqCCs. (1) Oral SqCC illustrates how elevated genomic instability may predispose early lesions to cancer progression. (2) Lung SqCC has recently been found to harbor critical cell lineage-specific genetic alterations. (3) Finally, skin SqCC demonstrates how a single pathway can be disrupted at multiple nodes, and specific pathways are dysregulated in cancer subtypes. These inherent tissue-specific differences in SqCCs may yield novel genes and pathways suitable for tailoring cancer diagnostics and therapy. The use of Multidimensional, genome wide analysis will be instrumental in facilitating these discoveries.

3.1. Genetic Instability in Oral Squamous Cell Carcinoma

The majority of oral malignancies in the upper aerodigestive tract are SqCCs [93]. The strongest etiological factor for oral carcinogenesis is tobacco smoke, while human papillomavirus (HPV) infection has been frequently detected in nonsmoker and younger oral squamous cell carcinoma (OSCC) patients [9397]. Differences in the pattern of genetic alterations suggest that HPV-positive oral cancers may represent a distinct disease entity that develops via a different genetic pathway [98].

As in other SqCCs, the evolution of OSCC is known to result from the acquisition of multiple genetic events targeting different genes and molecular pathways [99]. Genomic instability increases progressively from hyperplasia through various stages of dysplasia to invasive carcinoma [100]. Although specific genetic alterations have been frequently detected in early stage dysplasias, it is believed that it is the accumulation of genomic instability, rather than the sequence of specific events, that determines oral carcinogenesis [100, 101]. Thus, examining oral premalignant lesions (OPLs) would allow the identification of early genetic events that may be masked by increased genomic instability in late stage tumors and, as such, could potentially identify biomarkers for predicting OPLs that will progress to cancer. Previous studies have identified several loci, including multiple regions on chromosome arm 3p, such as the 3p14 locus that harbors FHIT (Fragile Histidine Triad), to be commonly deleted in oral dysplasias [102104]. However, assaying only several loci does not provide sufficient predictive power for progression. Whole-genome tiling-path array comparative genomic hybridization (aCGH) facilitated comprehensive mapping of genetic alterations in premalignant and malignant oral tissues. It was determined that low-grade dysplasias that would progress had the genomic patterns that resembled those observed in high-grade dysplasias (Figure 4), exhibiting increased genomic instability before phenotypic or histological appearances. In contrast, these genetic alterations were rare in benign low-grade dysplasias that did not progress. [101, 105]. Another frequent genomic alteration present in oral dysplasias that have a high risk of progression was DNA amplification, which is often an indicator of tumor aggressiveness. Specifically, coamplification of the oncogenes EGFR and CCND1 was observed [101, 105, 106].

541405.fig.004
Figure 4: Whole-genome tiling-path array profile of an oral low-grade dysplasia. Normalized log2 signal intensity ratios were plotted using SIGMA software [85, 86]. Cytoband pattern corresponding to the data points was drawn to the left of each data plot. The red and green vertical lines represent signal intensity ratios from −1 to +1 with an increment of 0.5. The magnified insets (for regions of chromosome 4, 8, and 9) include segmental copy number gain highlighted in orange and copy number loss highlighted green.

This pattern of genomic instability was informative, but it is useful to derive the mechanism of cancer progression. To decipher what pathways were disrupted and what genes were involved, the aCGH data was overlaid with publically available expression data. It was discovered that the majority of genes altered at the genomic and transcriptomic level converged into a single pathway, the FGF signalling pathway. Oral cancer is therefore an example of the importance of considering the downstream effect of genomic alterations and determination of biochemical pathways affected. Pathways may be affected early or late, but the ultimate phenotype may be redundant.

3.2. Lineage-Specific Genetic Features in Lung Cancer

Lung cancer is divided into two major categories, small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). The latter accounts for 85% of lung cancer cases and is further subdivided into adenocarcinoma (AC), squamous cell carcinoma (SqCC), and large cell carcinoma (LCC). While the prevalence of AC is rising, SqCC accounts for approximately 30% of lung cancer cases [107, 108].

The differential response to specific therapy highlights the importance of treating lung cancer subtypes as genetically distinct. For example, Gefitinib and Erlotinib target the tyrosine kinase surface receptor, EGFR, which was found to be overexpressed in at least 50% of lung cancers [109]. Strikingly, clinical trials revealed that most patients with NSCLC had moderate to no drug response, except only a subset of patients that were highly responsive [110113]. The responders had mutations in the catalytic domain of EGFR, which resulted in increased drug efficacy [114]. Specifically, EGFR mutations and drug sensitivity were associated with the AC subtype, never-smoking status, Asian ethnicity, and female gender [110]. In another example, drug response variability was seen with the advent of the thymidylate synthase (TS) inhibitor, Pemetrexed. Pemetrexed exhibits a higher efficacy in AC compared to SqCC, likely due to the generally higher levels of TS expression in SqCC [115117]. These clinical results have provided an impetus to delineate the different genetic mechanisms governing lung cancer subtypes, in order to tailor treatment for SqCC and AC.

Evidence from lung tumors is rapidly accumulating to indicate not only that are AC and SqCC composed of different genomic backgrounds (Figure 5), but also that they arise from the disruption of different biochemical pathways. Two key lineage-specific genetic differences between SqCC and AC were recently discovered using integrative genomics technologies. Both genes appear to be specific to the SqCC lineage and were found to be highly amplified. These genes were SOX2, likely involved in squamous pluripotency and differentiation [118], and BRF2, a RNA polymerase III transcription initiation factor controlling cell growth [119]. These discoveries suggest that SqCC and AC are genetically distinct.

fig5
Figure 5: Genome-wide comparison of lung adenocarcinoma (AC) and squamous cell carcinoma (SqCC). Affymetrix SNP 6.0 data for 11 SqCC and 49 AC cell lines were downloaded from the Wellcome Trust Sanger Cancer Genome Project. Data were analyzed against a pool of a normal reference samples using Partek Genomics Suite. For each position in the genome, the frequency of (a) gain and (b) loss are shown for SqCC (purple) and AC (orange). (c) Magnified view of chromosome arm 8p shows that SqCC has a higher frequency of segmental DNA gain at cumulative position 1.43 Gbp as compared to AC.
3.3. Genes and Pathways Disrupted in Squamous Cell Carcinoma of the Skin

Like lung cancer, skin cancer is divided into two types: melanoma skin cancer and nonmelanoma skin cancer (NMSC). Skin SqCC is a subtype of nonmelanoma skin cancer (NMSC), a classification that also includes basal cell carcinoma (BCC). The standard treatment option for NMSC is surgical biopsy, which has been applied frequently and successfully without recurrence [120]. Occasionally, NMSC develops to an advanced stage, and in 3%–5% of cases the tumor metastasizes, lowering the 5-year survival rate to 14%–39% [121]. Advanced cases of NMSC are not always treatable by biopsy alone [120]. Subsequent treatment options include chemotherapy, radiation therapy, or a combination of surgery and radiation [120, 121]. To date, chemotherapeutics such as cisplatin, 5-Fluor-Uracil, Paclitaxel, and combinations have been utilized with limited success [121]. Knowledge of the molecular mechanisms controlling NMSC tumors holds promise in improving treatment.

SqCC and BCC should be regarded as genetically distinct as different biological pathways are disrupted in these disease types [122]. The pathogenesis of SqCC is complex and dependent on many intrinsic and extrinsic factors. It is generally recognized that the most important extrinsic factor is UV sunlight exposure, as incidence of squamous cell carcinoma increases with lifetime UV exposure [123]. Using previously unexposed human skin, exposure to UV was shown to result in a missense point mutation with a UV signature in one TP53 allele, and the remaining allele is deleted [124]. SqCC undergoes the inactivation of multiple tumor suppressor genes, namely, TP53 which is inactivated in 90% of all precancerous lesions and invasive tumors, and also P16 and P14 [125, 126]. TP53 is also mutated in BCC; however, the mutation rate decreases from 90% to 50% [127129]. Broader examination of subtype-specific changes indicates that SqCC and BCC accrue alterations of different biochemical pathways.

In SqCC, deregulation of the PI3K/AKT signaling pathway is common. This pathway has been demonstrated to control cellular functions such as apoptosis, cell proliferation, and cell growth in isogenic cell lines [121, 130]. Mechanistically, the most common mechanisms of alteration are constitutive growth factor receptor activation, PI3K amplification or mutation, AKT amplification or mutation, and PTEN inactivation (Figure 6(a)) [121]. The main affected pathway in BCC is the sonic hedgehog (Shh) signaling pathway. In skin, this pathway regulates the development of follicles and sebaceous glands, as well as maintains stem cell populations [131]. In tumors the patched (PTCH) or smoothened (Smo) genes have been found to be most commonly altered, with mutation rates of 30%–60%. PTCH is inactivated most commonly by chromosomal deletion or by mutation [131, 132], while mutations in sonic hedgehog (Shh) and Smo have also been documented. In addition to genomic alterations, expression level analysis has revealed increased levels of PTCH and hedgehog interacting protein (HIP) mRNA, adding a dimension of change (Figure 6(b)). Much like oral cancer, both SqCC and BCC experience dysregulation of a certain pathway, via disruption of multiple component genes.

fig6
Figure 6: Major pathways affected in NMSC. (a) SqCC undergoes frequent alterations to the PI3K/AKT signaling pathway. (b) BCC undergoes frequent alterations to the sonic hedgehog pathway. Various pathway components affected are highlighted in red.

SqCC is associated with significant chromosomal aberrations at all stages of progression (reviewed in [133, 134]). In general, SqCC has increased genomic instability with 25%–90% of tumors demonstrating DNA aneuploidy. In contrast, BCC genomes are reasonably stable demonstrating 9%–40% aneuploidy. Early studies examined the extent of LOH in SqCC, finding it to be both diverse and widespread. Frequent regions of LOH were determined at 13q (46%), 9p (41%), 17p (33%), 17q (33%), and 3p (23%) [135]. In addition, SqCC has a range of other chromosomal alterations, including gain of chromosomes 3q, 8q, 14q, and 17q and deletion of chromosomes 8p, 9p, 17p, and 18q [133]. In BCC, loss of genetic material was found to be confined to 9q22.3, the locus of the PTCH tumor suppressor gene [136138]. A more complete picture of the genes and pathways disrupted in SqCC development could be obtained if these genomic data were integrated with epigenetic and transcriptomic data sets. However, the literature on genome-wide scans for epigenetic alterations and transcriptional changes in NMSC is scarce; global approaches will be crucial to identifying novel genes and pathways involved in the carcinogenesis of NMSC.

4. Squamous Cell Carcinoma and Integrative Genomics: Present and Future

While genetic, epigenetic, and transcriptional changes are associated with disease, their pathological impact is ultimately held at the levels of protein, cell, tissue, and organismal functions. It is widely believed that integrative phenomics—high throughput phenotyping of any type—will reveal different cancer subtypes with distinct biochemical and biological profiles (Figure 7). After transcriptomics, proteomics and metabolomics were the next types of phenomics to be developed [139].

541405.fig.007
Figure 7: The integrated network of genome-wide analyses comprising the cancer omics landscape. Phenomics or phenotypic effects are the direct consequence of the perturbations of genes and pathways which could be traced to genetic and epigenetic alterations when multiple dimensions of information are integrated to connect upstream causes with downstream effects.

The proteome is the universe of proteins, and proteomics is the study of the structure, regulation, and function of the proteome. The most obvious types of knowledge to be gained are the functional consequences of differential protein expression, profiles of proteins and their posttranslational modification (phosphorylation, ubiquitination, acetylation, palmitoylation, polysialylation, proteolytic cleavage, and dozens more), and identification of protein-protein interactions. Recent reviews address different aspects of cancer proteomics [139142]. The most common proteomics approach has been the coupling of two-dimensional gel electrophoresis with mass spectrometry (MS). This gel methodology can resolve up to 11000 spots but more commonly separates approximately 2000–4000. 2D fluorescence difference gel electrophoresis (2-D DIGE) uses ultrasensitive fluorescent dyes to label multiple samples in different colors, thus allowing the relative quantification of the same proteins from different samples. More recently, gel-free proteomic techniques have been developed, such as multidimensional protein identification technology (MudPIT). There have been many recent developments in MS sample preparation, methodologies, and data analysis [143]. Another large-scale approach to detect proteins is antibody microarrays [144]. The most common techniques used to discover protein-protein interactions are affinity purification coupled with MS and yeast two-hybrid analysis [145]. Among the most important new applications of proteomics are those defining the universe of posttranslational modifications (e.g., related to signaling or metabolism) and conducting functional screens of gene (e.g., using shRNA) and compound libraries. Other recent studies have begun to yield candidate biomarkers and suggest therapeutic pathways.

Metabolomics is the large-scale identification and quantification of metabolites [146]. These include sugars, salts, amino acids, peptides, lipids, acids, bases, steroid hormones, and so forth. There are almost 7000 known endogenous metabolites from 52 classes of compounds [147], but there are likely many more to be discovered. Metabolites are not only the products of cellular metabolism but also signaling molecules involved in the regulation of physiological states (e.g., feedback regulation, homeostasis). Thus, metabolomics may not only lead to the discovery of new biomarkers, but is also likely to reveal altered biochemical and physiological states. That would, in turn, suggest biochemical pathways that could be targeted therapeutically, and cellular assays to screen drugs or biologicals (e.g., siRNA, humanized antibodies). The most common approaches for global metabolomic profiling involve different separation technologies—such as liquid/gas chromatography and capillary electrophoresis—coupled with detection and identification using MS or nuclear magnetic resonance spectrometry (NMR) [148]. The data generated in this field is complex, and there is intense focus on the development of statistical methods [149] and database resources [150]. Oncology is one of the leading applications of metabolomics, both in clinical [151] and animal studies [152]. A major area of recent interest in cancer metabolomics is that related to the Warburg effect—the switch from oxidative phosphorylation to glycolysis that is associated with many cancers [153], including OSCC [154]. The state of the art in cancer metabolomics is illustrated by a prostate study that profiled 1126 metabolites across 262 clinical samples from 42 tissues and 110 each of urine and plasma [155]. The resulting profiles distinguished pathological subtypes (as well as the benign condition) and identified sarcosine as a metabolite that is highly increased with prostate cancer progression.

Many areas of proteomics and metabolomics have unique methodological requirements and are considered subdisciplines—for example, glycomics, lipidomics, spliceosomics, phosphoproteomics, and ubiquinomics. A major challenge for human studies is that available tissues are generally limited to biopsy samples, circulating blood cells (including purified lymphocytes, hematopoietic stem cells, platelets, etc.), and body fluids [142]. Recently, methodologies have been developed to allow analysis of not only fresh and frozen tissues but also formalin-fixed and paraffin-embedded tissue. Notably, workflows are being developed to collect and process diverse clinical samples for integrative omics studies [140].

In conclusion, from the early studies integrating genomic and epigenomic data, it is apparent that much can be gained when a multifaceted approach is employed. With multiple large-scale initiatives prioritizing this style of analysis on a diverse spectrum of tumor types, and with the continuing evolution of high throughput technologies to measure additional dimensions, the ability to perform bona fide molecular systems biology in clinical specimens may finally be in sight.

Acknowledgments

The authors wish to thank Emily Vucic and Ian Wilson for their useful comments. This work was supported by funds from the Canadian Institutes for Health Research (MOP 86731, MOP 77903), Canadian Cancer Society (CCS20485), NCI Early Detection Research Network (EDRN) (5U01 CA84971-10), and the Canary Foundation. C.E. Alvarez was supported by the United States National Institutes of Health (HG004663). R. Chari and I.F.L. Tsui were supported by scholarships from the Canadian Institutes for Health Research and the Michael Smith Foundation for Health Research. E. Gibb and K. Enfield contributed equally.

References

  1. D. Hanahan and R. A. Weinberg, “The hallmarks of cancer,” Cell, vol. 100, no. 1, pp. 57–70, 2000. View at Scopus
  2. A. de Klein, “Oncogene activation by chromosomal rearrangement in chronic myelocytic leukemia,” Mutation Research, vol. 186, no. 2, pp. 161–172, 1987. View at Scopus
  3. F. G. Haluska, Y. Tsujimoto, and C. M. Croce, “Oncogene activation by chromosome translocation in human malignancy,” Annual Review of Genetics, vol. 21, pp. 321–345, 1987. View at Scopus
  4. B. Dessars, L. E. de Raeve, H. E. Housni et al., “Chromosomal translocations as a mechanism of BRAF activation in two cases of large congenital melanocytic nevi,” Journal of Investigative Dermatology, vol. 127, no. 6, pp. 1468–1470, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. W. W. Lockwood, R. Chari, B. P. Coe et al., “DNA amplification is a ubiquitous mechanism of oncogene activation in lung and other cancers,” Oncogene, vol. 27, no. 33, pp. 4615–4624, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. D. G. Albertson, C. Collins, F. McCormick, and J. W. Gray, “Chromosome aberrations in solid tumors,” Nature Genetics, vol. 34, no. 4, pp. 369–376, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. J. T. Dong, “Chromosomal deletions and tumor suppressor genes in prostate cancer,” Cancer and Metastasis Reviews, vol. 20, no. 3-4, pp. 173–193, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. E. Schröck, S. du Manoir, T. Veldman et al., “Multicolor spectral karyotyping of human chromosomes,” Science, vol. 273, no. 5274, pp. 494–497, 1996. View at Scopus
  9. C. Fauth and M. R. Speicher, “Classifying by colors: FISH-based genome analysis,” Cytogenetics and Cell Genetics, vol. 93, no. 1-2, pp. 1–10, 2001. View at Scopus
  10. M. R. Speicher and D. C. Ward, “The coloring of cytogenetics,” Nature Medicine, vol. 2, no. 9, pp. 1046–1048, 1996. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Lichter, “Multicolor fishing: what's the catch?” Trends in Genetics, vol. 13, no. 12, pp. 475–479, 1997. View at Publisher · View at Google Scholar · View at Scopus
  12. J. M. Levsky and R. H. Singer, “Fluorescence in situ hybridization: past, present and future,” Journal of Cell Science, vol. 116, no. 14, pp. 2833–2838, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Pinkel, R. Segraves, D. Sudar et al., “High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays,” Nature Genetics, vol. 20, no. 2, pp. 207–211, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. A. S. Ishkanian, C. A. Malloff, S. K. Watson et al., “A tiling resolution DNA microarray with complete coverage of the human genome,” Nature Genetics, vol. 36, no. 3, pp. 299–303, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. T. J. Parrett and H. Yan, “Digital karyotyping technology: exploring the cancer genome,” Expert Review of Molecular Diagnostics, vol. 5, no. 6, pp. 917–925, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Salani, C. L. Chang, L. Cope, and T. L. Wang, “Digital karyotyping: an update of its applications in cancer,” Molecular Diagnosis and Therapy, vol. 10, no. 4, pp. 231–237, 2006. View at Scopus
  17. Ie. M. Shih and T. L. Wang, “Apply innovative technologies to explore cancer genome,” Current Opinion in Oncology, vol. 17, no. 1, pp. 33–38, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. G. M. Hampton, A. A. Larson, R. N. Baergen, R. L. Sommers, S. Kern, and W. K. Cavenee, “Simultaneous assessment of loss of heterozygosity at multiple microsatellite loci using semi-automated fluorescence-based detection: subregional mapping of chromosome 4 in cervical carcinoma,” Proceedings of the National Academy of Sciences of the United States of America, vol. 93, no. 13, pp. 6704–6705, 1996. View at Scopus
  19. R. Beroukhim, M. Lin, Y. Park et al., “Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays,” PLoS Computational Biology, vol. 2, no. 5, article e41, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. S. C. Schuster, “Next-generation sequencing transforms today's biology,” Nature Methods, vol. 5, no. 1, pp. 16–18, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. E. R. Mardis, “The impact of next-generation sequencing technology on genetics,” Trends in Genetics, vol. 24, no. 3, pp. 133–141, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. J. M. Rothberg and J. H. Leamon, “The development and impact of 454 sequencing,” Nature Biotechnology, vol. 26, no. 10, pp. 1117–1124, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Choi, U. I. Scholl, W. Ji et al., “Genetic diagnosis by whole exome capture and massively parallel DNA sequencing,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 45, pp. 19096–19101, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Sharma, T. K. Kelly, and P. A. Jones, “Epigenetics in cancer,” Carcinogenesis, vol. 31, no. 1, pp. 27–36, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. R. J. Shaw, G. L. Hall, D. Lowe et al., “The role of pyrosequencing in head and neck cancer epigenetics: correlation of quantitative methylation data with gene expression,” Archives of Otolaryngology—Head and Neck Surgery, vol. 134, no. 3, pp. 251–256, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. E. A. Vucic, I. M. Wilson, J. M. Campbell, and W. L. Lam, “Methylation analysis by DNA immunoprecipitation (MeDIP),” Methods in Molecular Biology, vol. 556, pp. 141–153, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. K. L. Thu, E. A. Vucic, J. Y. Kennett et al., “Methylated DNA immunoprecipitation,” Journal of Visualized Experiments, no. 23, article 935, 2009. View at Scopus
  28. M. Frommer, L. E. McDonald, D. S. Millar et al., “A genomic sequencing protocol that yields a positive display of 5- methylcytosine residues in individual DNA strands,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 5, pp. 1827–1831, 1992. View at Scopus
  29. S. J. Clark, J. Harrison, C. L. Paul, and M. Frommer, “High sensitivity mapping of methylated cytosines,” Nucleic Acids Research, vol. 22, no. 15, pp. 2990–2997, 1994. View at Scopus
  30. C. A. B. Chung, V. L. Boyd, K. J. McKernan et al., “Whole methylome analysis by ultra-deep sequencing using two-base encoding,” PLoS ONE, vol. 5, no. 2, article e9320, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Zilberman and S. Henikoff, “Genome-wide analysis of DNA methylation patterns,” Development, vol. 134, no. 22, pp. 3959–3965, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. V. Beier, C. Mund, and J. D. Hoheisel, “Monitoring methylation changes in cancer,” Advances in Biochemical Engineering/Biotechnology, vol. 104, pp. 1–11, 2007. View at Scopus
  33. I. Keshet, Y. Schlesinger, S. Farkash et al., “Evidence for an instructive mechanism of de novo methylation in cancer cells,” Nature Genetics, vol. 38, no. 2, pp. 149–153, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Weber, J. J. Davies, D. Wittig et al., “Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells,” Nature Genetics, vol. 37, no. 8, pp. 853–862, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Nouzova, N. Holtan, M. M. Oshiro et al., “Epigenomic changes during leukemia cell differentiation: analysis of histone acetylation and cytosine methylation using CpG island microarrays,” Journal of Pharmacology and Experimental Therapeutics, vol. 311, no. 3, pp. 968–981, 2004. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. Lippman, A. V. Gendrel, V. Colot, and R. Martienssen, “Profiling DNA methylation pattern using genomic tiling microarrays,” Nature Methods, vol. 2, no. 3, pp. 219–224, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Wu, L. T. Smith, C. Plass, and T. H. M. Huang, “ChIP-chip comes of age for genome-wide functional analysis,” Cancer Research, vol. 66, no. 14, pp. 6899–6902, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. P. J. Park, “ChIP-seq: advantages and challenges of a maturing technology,” Nature Reviews Genetics, vol. 10, no. 10, pp. 669–680, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Barski and K. Zhao, “Genomic location analysis by ChIP-seq,” Journal of Cellular Biochemistry, vol. 107, no. 1, pp. 11–18, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. R. Lister, M. Pelizzola, R. H. Dowen et al., “Human DNA methylomes at base resolution show widespread epigenomic differences,” Nature, vol. 462, no. 7271, pp. 315–322, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Bibikova, Z. Lin, L. Zhou et al., “High-throughput DNA methylation profiling using universal bead arrays,” Genome Research, vol. 16, no. 3, pp. 383–393, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. V. J. Bailey, H. Easwaran, Y. Zhang et al., “MS-qFRET: a quantum dot-based method for analysis of DNA methylation,” Genome Research, vol. 19, no. 8, pp. 1455–1461, 2009. View at Publisher · View at Google Scholar · View at Scopus
  43. M. P. Ball, J. B. Li, Y. Gao et al., “Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells,” Nature Biotechnology, vol. 27, no. 4, pp. 361–368, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. Y. Shen, S. D. Fouse, and G. Fan, “Genome-wide DNA methylation profiling: the mDIP-chip technology,” Methods in Molecular Biology, vol. 568, pp. 203–216, 2009. View at Publisher · View at Google Scholar · View at Scopus
  45. T. Rauch and G. P. Pfeifer, “Methylated-CpG island recovery assay: a new technique for the rapid detection of methylated-CpG islands in cancer,” Laboratory Investigation, vol. 85, no. 9, pp. 1172–1180, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. R. A. Irizarry, C. Ladd-Acosta, B. Carvalho et al., “Comprehensive high-throughput arrays for relative methylation (CHARM),” Genome Research, vol. 18, no. 5, pp. 780–790, 2008. View at Publisher · View at Google Scholar · View at Scopus
  47. B. Khulan, R. F. Thompson, K. Ye et al., “Comparative isoschizomer profiling of cytosine methylation: the HELP assay,” Genome Research, vol. 16, no. 8, pp. 1046–1055, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. J. F. Costello, C. Hong, C. Plass, and D. J. Smiraglia, “Restriction landmark genomic scanning: analysis of CpG islands in genomes by 2D gel electrophoresis,” Methods in Molecular Biology, vol. 507, pp. 131–148, 2009. View at Scopus
  49. T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–527, 1999. View at Publisher · View at Google Scholar · View at Scopus
  50. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar · View at Scopus
  51. T. Sørlie, C. M. Perou, R. Tibshirani et al., “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 19, pp. 10869–10874, 2001. View at Publisher · View at Google Scholar · View at Scopus
  52. M. Bittner, P. Meltzer, Y. Chen et al., “Molecular classification of cutaneous malignant melanoma by gene expression profiling,” Nature, vol. 406, no. 6795, pp. 536–540, 2000. View at Publisher · View at Google Scholar · View at Scopus
  53. M. E. Garber, O. G. Troyanskaya, K. Schluens et al., “Diversity of gene expression in adenocarcinoma of the lung,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 24, pp. 13784–13789, 2001. View at Publisher · View at Google Scholar · View at Scopus
  54. A. Bhattacharjee, W. G. Richards, J. Staunton et al., “Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 24, pp. 13790–13795, 2001. View at Publisher · View at Google Scholar · View at Scopus
  55. I. Alevizos, M. Mahadevappa, X. Zhang et al., “Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis,” Oncogene, vol. 20, no. 43, pp. 6196–6204, 2001. View at Publisher · View at Google Scholar · View at Scopus
  56. E. A. Clark, T. R. Golub, E. S. Lander, and R. O. Hynes, “Genomic analysis of metastasis reveals an essential role for RhoC,” Nature, vol. 406, no. 6795, pp. 532–535, 2000. View at Publisher · View at Google Scholar · View at Scopus
  57. P. Hegde, R. Qi, R. Gaspard et al., “Identification of tumor markers in models of human colorectal cancer using a 19,200-element complementary DNA microarray,” Cancer Research, vol. 61, no. 21, pp. 7792–7797, 2001. View at Scopus
  58. P. F. Macgregor and J. A. Squire, “Application of microarrays to the analysis of gene expression in cancer,” Clinical Chemistry, vol. 48, no. 8, pp. 1170–1177, 2002. View at Scopus
  59. B. P. Coe, R. Chari, W. W. Lockwood, and W. L. Lam, “Evolving strategies for global gene expression analysis of cancer,” Journal of Cellular Physiology, vol. 217, no. 3, pp. 590–597, 2008. View at Publisher · View at Google Scholar · View at Scopus
  60. V. E. Velculescu, L. Zhang, B. Vogelstein, and K. W. Kinzler, “Serial analysis of gene expression,” Science, vol. 270, no. 5235, pp. 484–487, 1995. View at Scopus
  61. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008. View at Publisher · View at Google Scholar · View at Scopus
  62. Q. Pan, O. Shai, L. J. Lee, B. J. Frey, and B. J. Blencowe, “Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing,” Nature Genetics, vol. 40, no. 12, pp. 1413–1415, 2008. View at Publisher · View at Google Scholar · View at Scopus
  63. B. T. Wilhelm, S. Marguerat, S. Watt et al., “Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution,” Nature, vol. 453, no. 7199, pp. 1239–1243, 2008. View at Publisher · View at Google Scholar · View at Scopus
  64. S. Djebali, P. Kapranov, S. Foissac et al., “Efficient targeted transcript discovery via array-based normalization of RACE libraries,” Nature Methods, vol. 5, no. 7, pp. 629–635, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. F. F. Costa, “Non-coding RNAs: meet thy masters,” BioEssays, vol. 32, no. 7, pp. 599–608, 2010. View at Publisher · View at Google Scholar
  66. D. P. Bartel, “MicroRNAs: genomics, biogenesis, mechanism, and function,” Cell, vol. 116, no. 2, pp. 281–297, 2004. View at Publisher · View at Google Scholar · View at Scopus
  67. W. Filipowicz, S. N. Bhattacharyya, and N. Sonenberg, “Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?” Nature Reviews Genetics, vol. 9, no. 2, pp. 102–114, 2008. View at Publisher · View at Google Scholar · View at Scopus
  68. M. S. Kumar, J. Lu, K. L. Mercer, T. R. Golub, and T. Jacks, “Impaired microRNA processing enhances cellular transformation and tumorigenesis,” Nature Genetics, vol. 39, no. 5, pp. 673–677, 2007. View at Publisher · View at Google Scholar · View at Scopus
  69. G. A. Calin and C. M. Croce, “MicroRNA signatures in human cancers,” Nature Reviews Cancer, vol. 6, no. 11, pp. 857–866, 2006. View at Publisher · View at Google Scholar · View at Scopus
  70. O. A. Kent and J. T. Mendell, “A small piece in the cancer puzzle: microRNAs as tumor suppressors and oncogenes,” Oncogene, vol. 25, no. 46, pp. 6188–6196, 2006. View at Publisher · View at Google Scholar · View at Scopus
  71. A. Esquela-Kerscher and F. J. Slack, “Oncomirs—microRNAs with a role in cancer,” Nature Reviews Cancer, vol. 6, no. 4, pp. 259–269, 2006. View at Publisher · View at Google Scholar · View at Scopus
  72. D. T. Starczynowski, F. Kuchenbauer, B. Argiropoulos et al., “Identification of miR-145 and miR-146a as mediators of the 5q-syndrome phenotype,” Nature Medicine, vol. 16, no. 1, pp. 49–58, 2010. View at Publisher · View at Google Scholar · View at Scopus
  73. B. M. Ryan, A. I. Robles, and C. C. Harris, “Genetic variation in microRNA networks: the implications for cancer research,” Nature Reviews Cancer, vol. 10, no. 6, pp. 389–402, 2010. View at Publisher · View at Google Scholar · View at Scopus
  74. Y. Saito and P. A. Jones, “Epigenetic activation of tumor suppressor microRNAs in human cancer cells,” Cell Cycle, vol. 5, no. 19, pp. 2220–2222, 2006. View at Scopus
  75. M. Fabbri, R. Garzon, A. Cimmino et al., “MicroRNA-29 family reverts aberrant methylation in lung cancer by targeting DNA methyltransferases 3A and 3B,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 40, pp. 15805–15810, 2007. View at Publisher · View at Google Scholar · View at Scopus
  76. J. M. Friedman, G. Liang, C. C. Liu et al., “The putative tumor suppressor microRNA-101 modulates the cancer epigenome by repressing the polycomb group protein EZH2,” Cancer Research, vol. 69, no. 6, pp. 2623–2629, 2009. View at Publisher · View at Google Scholar · View at Scopus
  77. D. P. Caley, R. C. Pink, D. Trujillano, and D. R. F. Carter, “Long noncoding RNAs, chromatin, and development,” TheScientificWorldJournal, vol. 10, pp. 90–102, 2010. View at Publisher · View at Google Scholar · View at Scopus
  78. B. Malecová and K. V. Morris, “Transcriptional gene silencing through epigenetic changes mediated by non-coding RNAs,” Current Opinion in Molecular Therapeutics, vol. 12, no. 2, pp. 214–222, 2010. View at Scopus
  79. J. W. Gray and C. Collins, “Genome changes and gene expression in human solid tumors,” Carcinogenesis, vol. 21, no. 3, pp. 443–452, 2000. View at Scopus
  80. R. Chari, B. P. Coe, E. A. Vucic, W. W. Lockwood, and W. L. Lam, “An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer,” BMC Systems Biology, vol. 4, article 67, 2010. View at Publisher · View at Google Scholar · View at Scopus
  81. R. McLendon, A. Friedman, D. Bigner et al., “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, no. 7216, pp. 1061–1068, 2008. View at Publisher · View at Google Scholar · View at Scopus
  82. B. Sadikovic, M. Yoshimoto, S. Chilton-MacNeill, P. Thorner, J. A. Squire, and M. Zielenska, “Identification of interactive networks of gene expression associated with osteosarcoma oncogenesis by integrated molecular profiling,” Human Molecular Genetics, vol. 18, no. 11, pp. 1962–1975, 2009. View at Publisher · View at Google Scholar · View at Scopus
  83. R. Chari, K. L. Thu, I. M. Wilson et al., “Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer,” Cancer and Metastasis Reviews, vol. 29, no. 1, pp. 73–93, 2010. View at Publisher · View at Google Scholar · View at Scopus
  84. B. P. Coe, R. Chari, C. MacAulay, and W. L. Lam, “FACADE: a fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data,” Nucleic Acids Research, vol. 38, no. 15, pp. e157–e157, 2010. View at Publisher · View at Google Scholar
  85. R. Chari, W. W. Lockwood, B. P. Coe et al., “SIGMA: a system for integrative genomic microarray analysis of cancer genomes,” BMC Genomics, vol. 7, article 324, 2006. View at Publisher · View at Google Scholar · View at Scopus
  86. R. Chari, B. P. Coe, C. Wedseltoft et al., “SIGMA: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes,” BMC Bioinformatics, vol. 9, article 422, 2008. View at Publisher · View at Google Scholar · View at Scopus
  87. L. Conde, D. Montaner, J. Burguet-Castell et al., “ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling,” Nucleic Acids Research, vol. 35, pp. W81–W85, 2007. View at Publisher · View at Google Scholar
  88. P. La Rosa, E. Viara, P. Hupé et al., “VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles,” Bioinformatics, vol. 22, no. 17, pp. 2066–2073, 2006. View at Publisher · View at Google Scholar
  89. K. Shinde, M. Phatak, F. M. Johannes et al., “Genomics Portals: integrative web-platform for mining genomics data,” BMC Genomics, vol. 11, no. 1, article 27, 2010. View at Publisher · View at Google Scholar · View at Scopus
  90. M. Lee and Y. Kim, “CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effecsts on the expression profile of the genome,” BMC Bioinformatics, vol. 10, article 424, 2009. View at Publisher · View at Google Scholar
  91. K. A. Lê Cao, I. González, and S. Déjean, “IntegrOmics: an R package to unravel relationships between two omics datasets,” Bioinformatics, vol. 25, no. 21, pp. 2855–2856, 2009. View at Publisher · View at Google Scholar · View at Scopus
  92. A. Gribov, M. Sill, S. Lück et al., “SEURAT: visual analytics for the integrated analysis of microarray data,” BMC Medical Genomics, vol. 3, article 21, 2010. View at Publisher · View at Google Scholar · View at Scopus
  93. S. Warnakulasuriya, “Global epidemiology of oral and oropharyngeal cancer,” Oral Oncology, vol. 45, no. 4-5, pp. 309–316, 2009. View at Publisher · View at Google Scholar · View at Scopus
  94. L. Zhang, X. Cheng, Y. H. Li et al., “High frequency of allelic loss in dysplastic lichenoid lesions,” Laboratory Investigation, vol. 80, no. 2, pp. 233–237, 2000. View at Scopus
  95. S. Begum, D. Cao, M. Gillison, M. Zahurak, and W. H. Westra, “Tissue distribution of human papillomavirus 16 DNA integration in patients with tonsillar carcinoma,” Clinical Cancer Research, vol. 11, no. 16, pp. 5694–5699, 2005. View at Publisher · View at Google Scholar · View at Scopus
  96. M. L. Gillison, W. M. Koch, R. B. Capone et al., “Evidence for a causal association between human papillomavirus and a subset of head and neck cancers,” Journal of the National Cancer Institute, vol. 92, no. 9, pp. 709–720, 2000. View at Scopus
  97. A. R. Kreimer, G. M. Clifford, P. J. F. Snijders et al., “HPV16 semiquantitative viral load and serologic biomarkers in oral and oropharyngeal squamous cell carcinomas,” International Journal of Cancer, vol. 115, no. 2, pp. 329–332, 2005. View at Publisher · View at Google Scholar · View at Scopus
  98. S. J. Smeets, B. J. M. Braakhuis, S. Abbas et al., “Genome-wide DNA copy number alterations in head and neck squamous cell carcinomas with or without oncogene-expressing human papillomavirus,” Oncogene, vol. 25, no. 17, pp. 2558–2564, 2006. View at Publisher · View at Google Scholar · View at Scopus
  99. L. Mao, W. K. Hong, and V. A. Papadimitrakopoulou, “Focus on head and neck cancer,” Cancer Cell, vol. 5, no. 4, pp. 311–316, 2004. View at Publisher · View at Google Scholar · View at Scopus
  100. K. D. Hunter, J. K. Thurlow, J. Fleming et al., “Divergent routes to oral cancer,” Cancer Research, vol. 66, no. 15, pp. 7405–7413, 2006. View at Publisher · View at Google Scholar · View at Scopus
  101. C. Garnis, R. Chari, T. P. H. Buys et al., “Genomic imbalances in precancerous tissues signal oral cancer risk,” Molecular Cancer, vol. 8, article 50, 2009. View at Publisher · View at Google Scholar · View at Scopus
  102. J. Califano, P. van der Riet, W. Westra et al., “Genetic progression model for head and neck cancer: implications for field cancerization,” Cancer Research, vol. 56, no. 11, pp. 2488–2492, 1996. View at Scopus
  103. I. F. Tsui, M. P. Rosin, L. Zhang, R. T. Ng, and W. L. Lam, “Multiple aberrations of chromosome 3p detected in oral premalignant lesions,” Cancer Prevention Research, vol. 1, no. 6, pp. 424–429, 2008. View at Publisher · View at Google Scholar · View at Scopus
  104. M. P. Rosin, X. Cheng, C. Poh et al., “Use of allelic loss to predict malignant risk for low-grade oral epithelial dysplasia,” Clinical Cancer Research, vol. 6, no. 2, pp. 357–362, 2000. View at Scopus
  105. I. F. L. Tsui, C. F. Poh, C. Garnis, M. P. Rosin, L. Zhang, and W. L. Lam, “Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias,” International Journal of Cancer, vol. 125, no. 9, pp. 2219–2228, 2009. View at Publisher · View at Google Scholar · View at Scopus
  106. S. Myllykangas, T. Böhling, and S. Knuutila, “Specificity, selection and significance of gene amplifications in cancer,” Seminars in Cancer Biology, vol. 17, no. 1, pp. 42–55, 2007. View at Publisher · View at Google Scholar · View at Scopus
  107. W. D. Travis, D. J. Giroux, K. Chansky et al., “The IASLC lung cancer staging project: proposals for the inclusion of broncho-pulmonary carcinoid tumors in the forthcoming (seventh) edition of the TNM classification for lung cancer,” Journal of Thoracic Oncology, vol. 3, no. 11, pp. 1213–1223, 2008. View at Publisher · View at Google Scholar · View at Scopus
  108. G. Sica, A. Yoshizawa, C. S. Sima et al., “A grading system of lung adenocarcinomas based on histologic pattern is predictive of disease recurrence in stage i tumors,” American Journal of Surgical Pathology, vol. 34, no. 8, pp. 1155–1162, 2010. View at Publisher · View at Google Scholar
  109. V. Rusch, J. Baselga, C. Cordon-Cardo et al., “Differential expression of the epidermal growth factor receptor and its ligands in primary non-small cell lung cancers and adjacent benign lung,” Cancer Research, vol. 53, no. 10, pp. 2379–2385, 1993. View at Scopus
  110. M. Fukuoka, S. Yano, G. Giaccone et al., “Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer (The IDEAL 1 Trial) [corrected],” Journal of Clinical Oncology, vol. 21, no. 12, pp. 2237–2246, 2003. View at Publisher · View at Google Scholar
  111. P. A. Jänne, S. Gurubhagavatula, B. Y. Yeap et al., “Outcomes of patients with advanced non-small cell lung cancer treated with gefitinib (ZD1839, 'Iressa') on an expanded access study,” Lung Cancer, vol. 44, no. 2, pp. 221–230, 2004. View at Publisher · View at Google Scholar · View at Scopus
  112. M. G. Kris, R. B. Natale, R. S. Herbst et al., “Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial,” Journal of the American Medical Association, vol. 290, no. 16, pp. 2149–2158, 2003. View at Publisher · View at Google Scholar · View at Scopus
  113. V. A. Miller, M. G. Kris, N. Shah et al., “Bronchioloalveolar pathologic subtype and smoking history predict sensitivity to gefitinib in advanced non-small-cell lung cancer,” Journal of Clinical Oncology, vol. 22, no. 6, pp. 1103–1109, 2004. View at Publisher · View at Google Scholar · View at Scopus
  114. J. G. Paez, P. A. Jänne, J. C. Lee et al., “EGFR mutations in lung, cancer: correlation with clinical response to gefitinib therapy,” Science, vol. 304, no. 5676, pp. 1497–1500, 2004. View at Publisher · View at Google Scholar · View at Scopus
  115. G. Selvaggi and G. V. Scagliotti, “Histologic subtype in NSCLC: does it matter?” Oncology, vol. 23, no. 13, pp. 1133–1140, 2009. View at Scopus
  116. C. Shin, V. J. Chen, L. S. Gossett et al., “Ly231514, a pyrrolo[2,3-d]pyrimidine-based antifolate that inhibits multiple folate-requiring enzymes,” Cancer Research, vol. 57, no. 6, pp. 1116–1123, 1997. View at Scopus
  117. P. Ceppi, M. Volante, S. Saviozzi et al., “Squamous cell carcinoma of the lung compared with other histotypes shows higher messenger RNA and protein levels for thymidylate synthase,” Cancer, vol. 107, no. 7, pp. 1589–1596, 2006. View at Publisher · View at Google Scholar · View at Scopus
  118. A. J. Bass, H. Watanabe, C. H. Mermel et al., “SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas,” Nature Genetics, vol. 41, no. 11, pp. 1238–1242, 2009. View at Publisher · View at Google Scholar · View at Scopus
  119. W. W. Lockwood, R. Chari, B. P. Coe et al., “Integrative genomic analyses identify BRF2 as a novel lineage-specific oncogene in lung squamous cell carcinoma,” PLoS Medicine, vol. 7, no. 7, Article ID e1000315, 2010. View at Publisher · View at Google Scholar
  120. J. S. Kirby and C. J. Miller, “Intralesional chemotherapy for nonmelanoma skin cancer: a practical review,” Journal of the American Academy of Dermatology, vol. 63, no. 4, pp. 689–702, 2010. View at Publisher · View at Google Scholar
  121. S. Claerhout, L. Verschooten, S. van Kelst, et al., “Concomitant inhibition of AKT and autophagy is required for efficient cisplatin-induced apoptosis of metastatic skin carcinoma,” International Journal of Cancer, vol. 127, no. 12, pp. 2790–2803, 2010.
  122. C. Dessinioti, C. Antoniou, A. Katsambas, and A. J. Stratigos, “Basal cell carcinoma: what's new under the sun,” Photochemistry and Photobiology, vol. 86, no. 3, pp. 481–491, 2010. View at Publisher · View at Google Scholar · View at Scopus
  123. R. Marks, “Squamous cell carcinoma,” Lancet, vol. 347, no. 9003, pp. 735–738, 1996. View at Publisher · View at Google Scholar · View at Scopus
  124. A. E. Persson, D. W. Edstrom, H. Backvall et al., “The mutagenic effect of ultraviolet-A1 on human skin demonstrated by sequencing the p53 gene in single keratinocytes,” Photodermatology Photoimmunology and Photomedicine, vol. 18, no. 6, pp. 287–293, 2002. View at Publisher · View at Google Scholar · View at Scopus
  125. V. L. Brown, C. A. Harwood, T. Crook, J. G. Cronin, D. R. Kelsell, and C. M. Proby, “p16 and p14 tumor suppressor genes are commonly inactivated in cutaneous squamous cell carcinoma,” Journal of Investigative Dermatology, vol. 122, no. 5, pp. 1284–1292, 2004. View at Publisher · View at Google Scholar · View at Scopus
  126. R. M. Shulstad and S. Proper, “Squamous cell carcinoma: a review of etiology, pathogenesis, treatment, and variants,” Journal of the Dermatology Nurses' Association, vol. 2, no. 1, pp. 12–16, 2010.
  127. A. Ziegler, A. S. Jonason, D. J. Leffell et al., “Sunburn and p53 in the onset of skin cancer,” Nature, vol. 372, no. 6508, pp. 773–776, 1994. View at Scopus
  128. C. L. Benjamin, S. E. Ullrich, M. L. Kripke, and H. N. Ananthaswamy, “p53 tumor suppressor gene: a critical molecular target for UV induction and prevention of skin cancer,” Photochemistry and Photobiology, vol. 84, no. 1, pp. 55–62, 2008. View at Publisher · View at Google Scholar · View at Scopus
  129. C. A. Elmets and M. Athar, “Targeting ornithine decarboxylase for the prevention of nonmelanoma skin cancer in humans,” Cancer Prevention Research, vol. 3, no. 1, pp. 8–11, 2010. View at Publisher · View at Google Scholar · View at Scopus
  130. I. Vivanco and C. L. Sawyers, “The phosphatidylinositol 3-kinase-AKT pathway in human cancer,” Nature Reviews Cancer, vol. 2, no. 7, pp. 489–501, 2002. View at Scopus
  131. M. Athar, X. Tang, J. L. Lee, L. Kopelovich, and A. L. Kim, “Hedgehog signalling in skin development and cancer,” Experimental Dermatology, vol. 15, no. 9, pp. 667–677, 2006. View at Publisher · View at Google Scholar · View at Scopus
  132. C. Koike, T. Mizutani, T. Ito et al., “Introduction of wild-type patched gene suppresses the oncogenic potential of human squamous cell carcinoma cell lines including A431,” Oncogene, vol. 21, no. 17, pp. 2670–2678, 2002. View at Publisher · View at Google Scholar · View at Scopus
  133. K. J. Ashton, M. A. Carless, and L. R. Griffiths, “Cytogenetic alterations in nonmelanoma skin cancer: a review,” Genes Chromosomes and Cancer, vol. 43, no. 3, pp. 239–248, 2005. View at Publisher · View at Google Scholar · View at Scopus
  134. M. A. Carless and L. R. Griffiths, “Cytogenetics of melanoma and nonmelanoma skin cancer,” Advances in Experimental Medicine and Biology, vol. 624, pp. 227–240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  135. A. G. Quinn, S. Sikkink, and J. L. Rees, “Basal cell carcinomas and squamous cell carcinomas of human skin show distinct patterns of chromosome loss,” Cancer Research, vol. 54, no. 17, pp. 4756–4759, 1994. View at Scopus
  136. Y. Jin, C. Martins, C. Jin et al., “Nonrandom karyotypic features in squamous cell carcinomas of the skin,” Genes Chromosomes and Cancer, vol. 26, no. 4, pp. 295–303, 1999. View at Publisher · View at Google Scholar · View at Scopus
  137. Y. Jin, C. Martins, L. Salemark et al., “Nonrandom karyotypic features in basal cell carcinomas of the skin,” Cancer Genetics and Cytogenetics, vol. 131, no. 2, pp. 109–119, 2001. View at Publisher · View at Google Scholar · View at Scopus
  138. K. J. Ashton, S. R. Weinstein, D. J. Maguire, and L. R. Griffiths, “Molecular cytogenetic analysis of basal cell carcinoma DNA using comparative genomic hybridization,” Journal of Investigative Dermatology, vol. 117, no. 3, pp. 683–686, 2001. View at Publisher · View at Google Scholar · View at Scopus
  139. M. A. Tainsky, “Genomic and proteomic biomarkers for cancer: a multitude of opportunities,” Biochimica et Biophysica Acta, vol. 1796, no. 2, pp. 176–193, 2009. View at Publisher · View at Google Scholar · View at Scopus
  140. R. Apweiler, C. Aslanidis, T. Deufel et al., “Approaching clinical proteomics: current state and future fields of application in cellular proteomics,” Cytometry A, vol. 75, no. 10, pp. 816–832, 2009. View at Publisher · View at Google Scholar · View at Scopus
  141. X. Q. Peng, F. Wang, X. Geng, and W. M. Zhang, “Current advances in tumor proteomics and candidate biomarkers for hepatic cancer,” Expert Review of Proteomics, vol. 6, no. 5, pp. 551–561, 2009. View at Publisher · View at Google Scholar · View at Scopus
  142. A. Zamò and D. Cecconi, “Proteomic analysis of lymphoid and haematopoietic neoplasms: there's more than biomarker discovery,” Journal of Proteomics, vol. 73, no. 3, pp. 508–520, 2010. View at Publisher · View at Google Scholar · View at Scopus
  143. J. R. Yates, C. I. Ruse, and A. Nakorchevsky, “Proteomics by mass spectrometry: approaches, advances, and applications,” Annual Review of Biomedical Engineering, vol. 11, pp. 49–79, 2009. View at Publisher · View at Google Scholar · View at Scopus
  144. C. Wingren and C. A. Borrebaeck, “Antibody-based microarrays,” Methods in Molecular Biology, vol. 509, pp. 57–84, 2009. View at Publisher · View at Google Scholar · View at Scopus
  145. D. Figeys, “Mapping the human protein interactome,” Cell Research, vol. 18, no. 7, pp. 716–724, 2008. View at Publisher · View at Google Scholar · View at Scopus
  146. R. Kaddurah-Daouk, B. S. Kristal, and R. M. Weinshilboum, “Metabolomics: a global biochemical approach to drug response and disease,” Annual Review of Pharmacology and Toxicology, vol. 48, pp. 653–683, 2008. View at Publisher · View at Google Scholar · View at Scopus
  147. D. S. Wishart, C. Knox, A. C. Guo et al., “HMDB: a knowledgebase for the human metabolome,” Nucleic Acids Research, vol. 37, no. 1, pp. D603–D610, 2009. View at Publisher · View at Google Scholar · View at Scopus
  148. J. C. Lindon and J. K. Nicholson, “Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics,” Annual Review of Analytical Chemistry, vol. 1, no. 1, pp. 45–69, 2008. View at Publisher · View at Google Scholar · View at Scopus
  149. H. J. Issaq, Q. N. Van, T. J. Waybright, G. M. Muschik, and T. D. Veenstra, “Analytical and statistical approaches to metabolomics research,” Journal of Separation Science, vol. 32, no. 13, pp. 2183–2199, 2009. View at Publisher · View at Google Scholar · View at Scopus
  150. E. P. Go, “Database resources in metabolomics: an overview,” Journal of Neuroimmune Pharmacology, vol. 5, no. 1, pp. 18–30, 2010. View at Publisher · View at Google Scholar · View at Scopus
  151. J. L. Spratlin, N. J. Serkova, and S. G. Eckhardt, “Clinical applications of metabolomics in oncology: a review,” Clinical Cancer Research, vol. 15, no. 2, pp. 431–440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  152. J. L. Griffin and R. A. Kauppinen, “Tumour metabolomics in animal models of human cancer,” Journal of Proteome Research, vol. 6, no. 2, pp. 498–505, 2007. View at Publisher · View at Google Scholar · View at Scopus
  153. M. G. V. Heiden, L. C. Cantley, and C. B. Thompson, “Understanding the warburg effect: the metabolic requirements of cell proliferation,” Science, vol. 324, no. 5930, pp. 1029–1033, 2009. View at Publisher · View at Google Scholar
  154. T. McFate, A. Mohyeldin, H. Lu et al., “Pyruvate dehydrogenase complex activity controls metabolic and malignant phenotype in cancer cells,” Journal of Biological Chemistry, vol. 283, no. 33, pp. 22700–22708, 2008. View at Publisher · View at Google Scholar · View at Scopus
  155. A. Sreekumar, L. M. Poisson, T. M. Rajendiran et al., “Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression,” Nature, vol. 457, no. 7231, pp. 910–914, 2009. View at Publisher · View at Google Scholar · View at Scopus