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Genetic alteration and gene expression modulation during cancer progression
Molecular Cancer volume 3, Article number: 9 (2004)
Cancer progresses through a series of histopathological stages. Progression is thought to be driven by the accumulation of genetic alterations and consequently gene expression pattern changes. The identification of genes and pathways involved will not only enhance our understanding of the biology of this process, it will also provide new targets for early diagnosis and facilitate treatment design. Genomic approaches have proven to be effective in detecting chromosomal alterations and identifying genes disrupted in cancer. Gene expression profiling has led to the subclassification of tumors. In this article, we will describe the current technologies used in cancer gene discovery, the model systems used to validate the significance of the genes and pathways, and some of the genes and pathways implicated in the progression of preneoplastic and early stage cancer.
Using genomic approaches to understand cancer progression
The accumulation of genetic alterations is thought to drive the progression of normal cells through hyperplastic and dysplastic stages to invasive cancer and, finally, metastatic disease. Since the initial efforts to link histopathological changes to the mutation of specific genes in colorectal cancer , progression models have been developed for many tumor types, including lung, breast, head and neck, and prostate [2–5]. Mutational and gene expression analysis of known tumor suppressors and oncogenes in the context of early tumorigenesis has provided insight into the role of these genes in cancer progression [6, 7]. Gene discovery has been greatly facilitated by molecular cytogenetic technologies identifying chromosomal regions associated with various stages and outcomes. Furthermore, high throughput, genome-wide approaches and the complete sequencing of the human genome have accelerated the large-scale discovery of cancer-related genes and pathways .
While genetic alterations in tumors are common, changes found in premalignant stages are more likely to represent causal events initiating and promoting cancer development. These events may be masked by the complex pattern of genetic alterations often associated with genetic instability in later stages of disease. For this reason all stages of progression have to be considered in order to fully understand how malignant tissues develop. To date, genomic and proteomic efforts have been primarily directed at the study of tumors. The relatively limited literature on genetic studies of earlier stage cancers is attributable to challenges associated with accessing premalignant specimens and the fact that genome-wide analysis would require quantities of material far exceeding the size of the minute specimens obtained. Recent advances in cell isolation techniques and miniaturization of genomic technologies have enabled comprehensive molecular profiling of selected cell types and high resolution mapping of gene disruption associated with specific disease phenotypes. This review article describes the current genomic technologies used for analysis of cancer, the model systems used to corroborate the significance of candidate cancer genes and pathways, and the genetic progression models for common types of cancer.
Tumors and precancerous lesions are heterogeneous cell populations harboring normal stromal and inflammatory cells. The presence of these cells could mask the detection of genetic and gene expression alterations in the cancer cells. The development of laser-assisted microdissection techniques addresses this problem by enabling selective isolation of cell populations, for example normal epithelium and hyperplastic cells [9–11] (Fig. 1).
Three commonly used microdissection techniques are laser capture microdissection (LCM), laser microbeam microdissection (LMM), and laser pressure catapult (LPC) . LCM involves the capture of cells by adhering them to a thermoplastic membrane activated by a near-infrared low power laser . The relatively low intensity of the laser does not damage DNA, RNA, or proteins in the captured cells, while the remaining tissue section is left intact on the glass slide . LMM uses a focused laser beam to cut out target cells and to photoablate unwanted adjacent tissue . LMM is often used in conjunction with LPC, a technique that involves the build up of laser-generated high-photon density under a given specimen, causing the selected cells of interest to catapult up along the path of the beam and become available for collection .
Identification of genetic alterations
Current methods for genome-wide detection of genetic alterations fall into three main categories: (1) molecular cytogenetic evaluation of chromosomal aberrations and re-arrangements, (2) DNA polymorphism analysis for detecting loss of heterozygosity (LOH) or allelic imbalance, and (3) comparative genomic hybridization (CGH) approaches for identifying segmental copy number changes.
Cytogenetic approaches are designed to detect aberrations and rearrangements under direct examination of chromosomes and chromosomal targets. G-banding, fluorescence in situ hybridization (FISH), and spectral karyotyping (SKY) are the commonly used methods [14, 15]. G-banding is often used in clinical settings for the analysis of leukaemia and is best suited to detect large chromosomal aberrations, namely structural or numeric changes . This method evaluates stained metaphase chromosome spreads to identify rearrangements and gain or loss of chromosome bands. One of the most comprehensive databases of cytogenetic information for various tumor types is the Mitelman Database of Chromosome Aberrations in Cancer . This and other cytogenetic databases are listed in Table 1.
FISH has helped bridge the gap between molecular genetics and classical cytogenetics. This technology uses specific DNA probes of known chromosomal location to evaluate alterations at a specific locus on a cell-by-cell basis (Fig 2a) . Gain, loss, and splitting of hybridization signals on metaphase or interphase chromosomes reflect duplication, deletion, and translocation events respectively . FISH is useful in fine mapping genetic alterations in very small specimens such as premalignant lesions since it does not require microdissection. With the development of fluorochromes that fluoresce at different wavelengths, multicolor FISH (M-FISH) has enabled the examination of multiple loci in the same experiment .
SKY uses 24 different probe sets to virtually paint each chromosome a different color. This technique involves the simultaneous excitation of multiple fluorochromes and the use of an interferometer to determine the profile at each pixel  (Fig 2b).
Although whole genome cytogenetic techniques are limited to the identification of intrachromosomal rearrangements and breakpoint determination, they have been the preferred techniques for detailed karyotypic assessment of structural chromosome aberrations .
Assessing LOH using polymorphic markers
Microsatellite analysis uses simple sequence repeat (SSR) polymorphisms as markers for detecting LOH. A polymerase chain reaction (PCR) using primers flanking a repeat should yield two signals corresponding to the two heterozygous alleles. When the signal intensity ratio of the tumor alleles differs from that of the normal alleles, allelic imbalance or LOH is inferred. An example of mapping of LOH at the chromosome scale was the use of 28 markers spanning chromosome 3p to determine three distinct regions of alteration in non-small cell lung cancer . In addition, microsatellite analysis is commonly used for fine mapping minimal regions of LOH. However, this approach is limited by the availability of polymorphic SSR markers in the chromosomal regions of interest. For microdissected, minute premalignant specimens, DNA yield is an additional limitation since each marker requires at least 5 nanograms of DNA per assay . Therefore, although whole-genome allelotyping has been applied to early stage cancer [23–26], efforts have been largely focused on tumors and cell lines where material is not limiting.
Single nucleotide polymorphisms (SNPs) are another source of DNA markers used in identifying LOH. SNPs are common in the human genome and in some instances their variation can be correlated to disease behaviour [27, 28]. The through-put of this approach is greatly enhanced by parallel analysis of multiple loci on microarrays. For example, GeneChip® arrays from Affymetrix® have enabled simultaneous tracking of approximately 1,500 SNPs . The large number of SNPs examined would compensate for the fact that not all loci will be informative (heterozygous). The recently released "Mapping 10 K Array" tracks greater than 10,000 SNPs distributed throughout the genome should increase the information content of an array hybridization experiment.
Unlike microsatellite or SNP analyses, amplified fragment length polymorphism (AFLP)-based approaches require no previous knowledge of polymorphisms. Fingerprinting techniques such as random amplification of polymorphic DNA (RAPD) or arbitrarily primed PCR (AP-PCR) use short primers of 10 to 20 nucleotides to amplify multiple fragments randomly distributed throughout the genome (Fig 3). The PCR products are then separated by electrophoresis to display up to dozens of anonymous DNA polymorphisms [30–32]. It has been applied to a variety of tumor types to study genomic instability, identify novel DNA amplifications and deletions, and to assess changes in methylation state [33–42]. The recently developed methylation-sensitive AFLP (MS-AFLP) technology allows for an unbiased assessment of epigenetic changes in a subset of methylation sites throughout the genome [43, 44]. However, the use of RAPD patterns in predicting prognosis has not yet been widely used.
Comparative genomic hybridization
Comparative Genomic Hybridization (CGH) detects segmental DNA copy number changes. Differentially labeled tumor DNA and control normal DNA are co-hybridized to a metaphase chromosome spread, producing an average fluorescence ratio profile at approximately 20 Mbp resolution . Copy number changes in a variety of cancers – and to a lesser extent, premalignant lesions – have been detected using this method [46–56]. While CGH provides a profile of the entire genome, the resolution is limited and therefore it is difficult to determine the identity of specific gene alterations. CGH is often used in conjunction with FISH in order to fine map alterations to the gene level. As CGH has become a more widely used method, profile databases have been assembled for public access (see Table 1).
Until recently, localized deletion mapping using microsatellite markers has represented the highest resolution method available to identify potential tumor suppressor genes. However, new approaches based on the use of genomic microarrays have been developed. To achieve higher resolution, Pollack et al. made use of cDNA microarrays for analyzing genomic DNA derived probes [57, 58]. However this approach is hampered by suboptimal hybridization which arises because the genomic DNA probe that is used has introns that are absent in the spotted cDNA target. As mentioned above, the recent development of SNP arrays has greatly facilitated deletion detection, though the resolution of SNP arrays is currently limited to approximately 10,000 SNPs. One would expect that only a subset of these loci will be informative (heterozygous). Another technology called representational oligonucleotide microarray analysis (ROMA) provides a means of detecting genetic alterations in cancer tissue using a high density oligonucleotide array to profile subtractive hybridization products generated through representational differential analysis [59, 60].
Complementary to these array-based CGH techniques, bacterial artificial chromosome (BAC) array CGH allows the detection of segmental copy number changes [45, 61]. BAC array CGH is similar to conventional chromosomal CGH except that it uses segments of human DNA as hybridization targets instead of a metaphase spread of chromosomes [45, 61, 62] (Fig 4). Hybridization onto such arrays overcomes the low resolution that limits conventional CGH. As with conventional CGH, total genomic DNA from a tumor and a normal cell population are differentially labeled and co-hybridized onto an array. The ratio of the fluorescence intensities on each DNA spot on the array is proportional to the copy number of the corresponding sequence.
High resolution arrays allow for the delineation of amplification and deletion boundaries in a single experiment. These arrays have been instrumental in detailed analysis of specific chromosomal regions [42, 63–68]. High resolution analysis of entire chromosome arm for segmental copy number alterations is made possible with whole chromosome or chromosome arm BAC arrays [69–71].
The application of this technology for genome-wide profiling was first described by Snijders et al., who used 2460 marker BACs and P1 clones to generate an array with clones positioned at ~1.4 Mbp intervals . Arrays of similar resolution have been reported by other groups [73, 74]. This technology has been applied to analyze cell lines and tumors from lymphoma, bladder, breast, prostate, and kidney [75–80].
Further advancement of this technology to tiling resolution of the whole genome has eliminated the need for inferring continuity between marker BACs. This was achieved by using an ordered set of 32,433 BAC clones that provide full coverage of the genome, allowing the profiling of the entire genome in a single experiment [61, 81, 82] (Fig 5).
Digital karyotyping is a genome wide approach for identifying copy number alterations . This technique involves the isolation and enumeration of short sequence tags from specific genomic loci, namely tags adjacent to Sac I restriction enzyme cut sites throughout the genome. Digital enumeration of the tags at intervals along each chromosome reflects DNA content. The concept behind this DNA profiling technique is analogous to that of serial analysis of gene expression (SAGE) described below , except that the DNA tags concatenated for sequence analysis are derived from fragmented genomic DNA rather than from a cDNA population. The sensitivity and specificity of digital karyotyping depends on the combination of mapping and fragmenting enzymes employed as well as the number of tags sampled. The identification of high-copy-number amplifications can be detected with fewer tag counts.
Ultimately, the genome-wide search for oncogenes and tumor suppressors will require the integration of both genomic and expression analysis approaches. Integration of genetic and gene expression data will validate the candidate genes in regions of DNA alteration as well as highlight the downstream effects.
The two main types of microarrays are cDNA microarrays and oligonucleotide microarrays [85, 86]. cDNA microarrays have PCR-generated "target" cDNAs deposited onto glass whereas oligonucleotide microarrays are manufactured using either a photolithographic process that directly synthesizes them on the glass slide or deposition of oligonucleotides onto glass slides [87, 88]. Both types of microarray are hybridized with cDNA samples derived from tissues of interest to assess changes in expression levels. After competitive hybridization of the cDNA samples, differentially labeled with dyes such as Cyanine 3 and Cyanine 5, the slides are washed to remove unspecific binding and then scanned to determine the relative intensities of each channel. Normalization of the samples allows for differences in labeling and detection efficiencies so that the two datasets can be compared .
Approximately a quarter of microarray-related literature pertains to cancer, with tumor and cell line transcriptome profiling providing numerous insights into disease . The development of the "lymphochip" cDNA microarray and other cDNA and oligonucleotide arrays has allowed the subclassification of many disease types including lymphoma, leukaemia, and cancers of the breast and lung [91–100]. Analysis of small specimens, such as those derived from premalignant tissue, has been facilitated by the introduction of RNA amplification methods where cDNA is linearly amplified, thus preserving the composition of the original RNA population [101, 102]. This analysis of premalignant lesions has led to the discovery of new biomarkers for determining prognosis and new targets for treatment. Frequently used microarray databases are listed in Table 1.
Serial analysis of gene expression
Unlike microarray technology, which focuses analysis to only those cDNAs represented on a chip, Serial Analysis of Gene Expression (SAGE) provides an unbiased profile of the transcriptome by taking a raw count of sequence tags, each representing a transcript in an RNA population . The tag count is accomplished through the creation and quantification of concatenated tags generated from tissue mRNAs . (Figure 6 summarizes the steps of SAGE library construction) Absolute quantification of the transcriptome allows the creation of gene expression profiles that can subsequently be compared against profiles from other cell types. The longSAGE variation of the SAGE protocol allows more specific tag mapping, notably to cDNAs but also to genomic sequence . The microSAGE protocol, on the other hand, reduces the amount of RNA required for library construction and therefore facilitates examination of the early stages in carcinogenesis [105, 106]. There are a number of web resources for SAGE (see Table 1). SAGEnet provides multiple protocols, while SAGEmap and SAGE Genie provide analysis tools and databases [107, 108].
SAGE-based research to identify cancer markers has been conducted for a variety of primary cancers and cell lines, including breast, kidney, prostate, liver, lung, gastric, colorectal, and pancreatic cancer [109–128]. In a few of these instances, such as the work on breast cancer by Porter et al., libraries have been generated for early histopathological stages of cancer that demonstrate expression profiles distinct to each stage [108, 114, 127]. These authors suggested that some of the observed gene expression changes tied to progression through the in situ stages of disease were likely involved with cell growth, differentiation, and survival.
Whole genome profiling approaches, such as SAGE and microarrays, yield candidate genes that require verification. Given that biological specimens are often limited in size, traditional Northern blot analysis may not always be possible. Reverse-transcriptase polymerase chain reaction (RT-PCR) provides semi-quantitative assessment of relative abundance of specific transcripts using gene-specific primers . Real time RT-PCR measures product amount after each cycle of amplification based on association of fluorescence to the amount of DNA accumulated during the PCR [130–133]. Three common real-time approaches are SYBR Green® staining, the TaqMan® system, and the molecular beacon system . In the SYBR Green® method, fluorescent DNA dye that is bound non-specifically to double-stranded DNA is measured to quantify the accumulation of PCR products. In the Taqman® system, a fluorescence resonance energy transfer (FRET) oligonucleotide probe complementary to the target sequence is used as the reporter system. The fluorescence of the reporter molecule at the 5' end of the oligonucleotide is interfered with by a quencher molecule at the 3' end. When strand synthesis occurs in PCR, the nuclease activity of Taq polymerase degrades the FRET probe and releases the reporter from the quencher, producing fluorescence. In the Molecular Beacon method, the 3' quencher and 5' reporter of FRET probes initially exhibit no fluorescence because the oligonucleotide forms a hairpin loop that brings these two factors into close proximity. Binding of the probe at a target sequence separates the two fluorochromes, allowing the reporter to fluoresce.
Immunohistochemistry, tissue microarrays, and proteomic approaches
Basic immunohistochemical (IHC) techniques, when applied to tissue microarrays (TMA), allow for high throughput analysis of multiple tissues [135–137]. In the construction of TMAs, core samples taken from multiple archival specimens are re-embedded in a paraffin block so that each section of the TMA would contain multiple samples for parallel analysis . Similarly, cytology microarrays, with cell suspensions spotted in an array format, facilitate parallel analysis of intact cells .
While IHC examines individual targets, proteomic approaches aim to assess global changes at the protein level [8, 140]. For more than a quarter century, two-dimensional polyacrylamide gel electrophoresis has been a commonly used method for displaying the proteome . This approach separates proteins based on isoelectric focusing (pI) and size (polyacrylamide gel electrophoresis). A recently developed method for resolving proteins is isotope-coded affinity tagging (ICAT) which allows quantitative analysis of paired protein samples through the use of stable isotope labeling . Isotopic tags covalently bind cysteine residues within a protein. Tagged proteins are separated and identified by liquid chromatography and mass spectrometry. An assessment of these two methodologies was provided by Patton et al. .
In contrast to gel electrophoresis, mass spectrometry assesses protein size by time of flight (TOF) analysis [144, 145]. A technique that incorporates this approach is surface-enhanced laser desorption/ionization (SELDI)-TOF, an affinity-based method in which proteins adsorb to a given chemically modified surface and, subsequently, the bound proteins are resolved by TOF analysis [146, 147]. This technique is commonly used for detecting disease-associated proteins in cell lysates as well as serum.
Recently, high throughput proteomic approaches have been used for identifying protein interactions with other proteins, nucleic acids, lipids, antibodies, and drugs. These approaches include protein array-based and phage display-based methodologies. Cell lysates or protein samples are differentially labeled and competitively hybridized to individual protein targets arrayed on a small surface. Signal intensity ratios are used to calculate the relative abundance of a given molecule. Commercially available antibody microarrays have immobilized selected antibodies targeted against components of known cellular pathways such as signal transduction, cell cycle regulation, gene transcription, or apoptosis .
Proteins may also be displayed on the surface of bacteriophage, serving as an alternative to protein arrays for high throughput screening [149–151]. In this system, cDNA libraries are inserted into vectors that generate fusion products with a bacterial phage coat protein. These recombinant proteins are expressed on the surface of the bacteriophage and can be screened for interactions with proteins of interest.
Gene silencing and overexpression
Epigenetic changes may alter gene expression. In general, they are heritable and do not arise due to alterations of DNA sequence . Methylation is the best characterized epigenetic change, typically occurring at CpG dinucleotides within the mammalian genome . CpG dinucleotides are commonly found in promoter regions, in "CpG islands" which are long portions of DNA with high GC content. With the exception of the X chromosome, CpG residues in promoter regions are typically unmethylated [154, 155]. Methylation occurs by the attachment of a methyl group to C5 of the cytosine residue after DNA replication has occurred, resulting in the loss of gene expression. The relative amount of methylation can vary, a decrease termed hypomethylation and an increase known as hypermethylation.
Methylated DNA can be distinguished from unmethylated DNA by virtue of resistance to 1) methylation sensitive restriction enzyme digestion and 2) bisulfite treatment. In the first case, isoschizimers such as Hpa II and Msp I (which recognize CCGG) and Xma I and Sma I (which recognize CCCGGG) are often used to detect methylation, since cleavage by Hpa II and Xma I are impaired by internal cytosine methylation of the recognition sequence. This distinguishing feature is the basis of global methylation detection methods such as restriction landmark genomic scanning (RLGS) of CpG island methylation and methylation target arrays [154, 156–159]. In methylation target arrays a multitude of CpG islands are spotted onto an array and hybridized with probes generated by linker-mediated PCR-amplification of sample DNA pre-digested with a methylation-specific enzyme [160–162]. Methyl-CpG binding proteins can be used to identify the unique distribution of CpG islands by using chromatin immunoprecipitation . Methylated DNA bound to these proteins serves to identify novel targets of epigenetic inactivation in human cancer. Localization of these targets can be achieved by hybridization to CpG island microarrays or through CGH. Bisulfite treatment of DNA causes selective deamination of cytosine to uracil . However, in contrast to cytosine, 5-methyl-cytosine does not react with bisulfite, hence oligonucleotide primers can be tailored to recognize altered or unaltered sequence in order to distinguish unmethylated and methylated targets in a methylation-specific PCR assay.
With respect to the progression of cancer, the genetic changes associated with disease development are often accompanied by significant changes in methylation state . The idea that epigenetic changes can be a mechanism for altering gene expression and driving tumorigenesis has been supported by recent work, examples including work on 14-3-3σ and CCND2 in breast cancer, p16INK4A and RASSF1A in lung cancer, and HPP1 and SFRP1 in colorectal cancer [165–170].
Deducing function of novel genes
Cell culture models are often used to deduce gene function through the introduction of a foreign gene or by disruption of endogenous gene function, thereby creating a new phenotype or altering cell behaviour.
A new approach for disrupting gene function is RNA interference (RNAi). This method targets specific genes by way of post-transcriptional gene silencing. The natural function of RNAi is thought to be protection of the genome against invasion by mobile genetic elements such as transposons and viruses, which produce aberrant RNA or dsRNA in the host cell when they become active . Efforts to develop an RNAi microarray will ultimately allow for knockdown analysis of gene function to be undertaken on a genome-wide scale [172, 173].
Animal models serve two broad functions in terms of identifying and characterizing genes involved in cancer and cancer progression. First, sequence homology between known animal genes and previously unidentified human genes allows for speculation as to the gene's function in humans. This is possible because there are an increasing number of whole genome sequences available for a variety of animals (e.g. Fugu, Drosophila, mouse, chimpanzee) [174–177]. Second, animals serve as functional models for cancer, allowing researchers to assess the effects of gene disruption, treatment regimes, and disease progression. Mammalian models are expected to more closely mimic the intricacies of human conditions . The expansive body of literature pertaining to murine malignancy and the completion of the mouse genome sequence makes the mouse the leading model for cancer gene discovery [179, 180].
Initial efforts to examine cancer genetics in the mouse involved incorporation of embryonic stem (ES) cells containing mutated forms of a gene of interest into a developing mouse [181, 182]. Conditional mutants allow spatial and temporal control over the expression of the introduced genotypic alteration, an example being the Cre-lox system [182, 183]. Briefly, this system involves the generation of parallel lines of mice, one having been manipulated to have the gene of interest flanked by P1 bacteriophage loxP sites and the other having the Cre recombinase expressed under the control of a tissue-specific promoter. When these lines are crossed, the gene book-ended by loxP sites is excised in that tissue where Cre is expressed, thereby disrupting expression of the gene of interest and allowing researchers to assess its role in tumor development in that tissue. There are numerous variations on this technique currently in use and the Cre-lox system has been widely applied in cancer progression research [184, 185].
Current cancer progression models
The use of genome-wide analysis has resulted in the discovery of genes involved in cancer progression. This section summarizes the cumulative information pertaining to the genetic alterations and gene expression changes associated with the progressional stages in four major cancer types.
Histopathological stages of the most common form of breast cancer include atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma . Inherited alterations at the BRCA1 or BRCA2 loci can predispose individuals to breast cancer, the histology in these cases differing from that seen in sporadic disease [186, 187]. Altered expression of the FHIT tumor suppressor locus is common in many breast cancer types, especially in individuals carrying BRCA2 mutations . Recent gene expression profiling studies have served to identify a genetic basis for the disease stages listed above [3, 53, 100, 114, 127, 189, 190]. SAGE and microarray data have demonstrated that relative expression of genes within the transcriptome vary from stage to stage, with some of the genes being expressed solely in a specific stage. Correlation of expression changes between multiple cases has led to the characterization of prognostic biomarkers [97, 98, 187, 191–195]. Furthermore, proteomic studies have identified additional changes in DCIS not detected by nucleic acid-based assays [140, 196]. Methylation changes driving breast cancer progression have been identified using both high throughput techniques and more established techniques (e.g. methylation-specific PCR) [113, 162, 166, 167, 197–200]. This has lead to the discovery of epigenetic changes that correlate to disease outcome and therefore have strong prognostic value. Figure 7 provides a summary of those genes and chromosomal regions implicated in breast cancer progression.
Prostate cancer is multifocal and heterogeneous, meaning that benign, premalignant, and malignant tissues coexist within the same patient . The prostate cancer progression model suggests that normal prostatic epithelium changes to prostatic intraepithelial neoplasm (PIN), which in turn becomes localized invasive cancer, metastatic, and, finally, hormone refractory disease with increasing severity reflected in a higher Gleason grade [201, 202]. The hormone refractory stage occurs after metastasis, when patients cease to respond to hormone therapy and quickly succumb to the disease . Both conventional and high throughput techniques have been employed to assess the progression of prostate cancer in terms of chromosomal instability and methylation [203–210]. Most genes that have been implicated in prostate cancer development have been identified through linkage analysis. Brothmann et al. summarized cytogenetic and molecular genetic alterations associated with hereditary and sporadic prostate cancer, as well as epigenetic changes . With new technology, such as LCM, it is now possible to procure isolated populations of cells to deduce somatic events. In addition, cDNA microarray and SAGE technologies have elucidated gene expression changes tied to prostate cancer progression at each histopathological stage [202, 211–214]. These same technologies have been used to identify potential biomarkers, taking advantage of correlation between the expression of specific genes and Gleason score to generate a prognostic model for patients that have undergone prostatectomy based solely on gene expression data . Integration of gene expression profiles with tissue microarray data has allowed multiplex assessment of biomarkers for diagnostics and prognostics in prostate cancer [216, 217]. Those genetic, epigenetic, and chromosomal alterations that have been characterized for prostate cancer are shown in Figure 8.
Pathogenesis of lung cancer is thought to differ for small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) . Classification of lung cancer subtypes is possible based solely on differential expression patterns [94–96, 219–223]. Analysis of gene expression, methylation, and chromosomal changes in lung cancer have served to better shape the existing lung cancer progression model [37, 167, 219, 220, 224–236]. Disease progression is best characterized in bronchial squamous cell carcinoma, a NSCLC subtype where normal epithelium develops hyperplasia or metaplasia, followed by varying degrees of dysplasia, and then carcinoma in situ and invasive cancer [2, 237–241]. Alterations on chromosome 3p followed by alterations on 9p are believed to be the earliest genetic events to occur in the progression of the disease . Analysis of the early stages of squamous cell carcinoma has been facilitated by the development of fluorescence bronchoscopy technology (e.g. the LIFE-Lung device) allowing the detection and capture of minute lesions [243, 244] (Fig 9). hTERT, located on chromosome 5p, has also been studied extensively in lung cancer . The expression pattern for hTERT has been reported to be slightly increased in the early premalignant stages of development and gradually increase as the lesion becomes more severe [226, 245]. Figure 10 shows the genetic alterations understood to drive progression.
Colorectal cancer typically progresses from normal epithelium through dysplasia and adenoma stages to carcinoma in situ and finally to invasive cancer [246, 247]. Genetic instability is a hallmark of colorectal cancer; microsatellite instability (MIN) is attributed to DNA mismatch repair genes, whereas chromosomal instability (CIN) is characterized by gross chromosomal changes arising during cell division and commonly involves APC and β-catenin mutations [248–250]. cDNA microarray analysis has revealed different gene expression patterns for cell cycle regulation and DNA repair genes in colorectal cancer cell lines characterized by CIN or MIN . Furthermore, gene expression profiling with SAGE, oligonucleotide arrays, and cDNA microarrays has been applied to identify staging and prognostic markers [112, 118, 252–259]. Those genes implicated are typically involved with cell cycle control, apoptosis, angiogenesis, and transcription machinery. Figure 11 details those alterations understood to drive tumorigenesis in the colorectal region.
Advances in technology have provided the means for a global look at an increased resolution. Using a global approach, identification of genetic alterations and gene expression changes at the early and late stages of cancer progression is possible. Through the integration of analysis at the level of the genome, transcriptome, and proteome, key pathways and functions can be defined. This will give a better understanding of the critical steps driving disease progression.
Knowledge of causal events driving progression will allow for a mechanistic basis for subclassification of disease and provide novel targets for early diagnosis and the creation of more specific treatment regimens .
Vogelstein B, Fearon ER, Hamilton SR, Kern SE, Preisinger AC, Leppert M, Nakamura Y, White R, Smits AM, Bos JL: Genetic alterations during colorectal-tumor development. N Engl J Med. 1988, 319: 525-532.
Minna JD, Roth JA, Gazdar AF: Focus on lung cancer. Cancer Cell. 2002, 1: 49-52.
Polyak K: Molecular alterations in ductal carcinoma in situ of the breast. Curr Opin Oncol. 2002, 14: 92-96.
Califano J, van der Riet P, Westra W, Nawroz H, Clayman G, Piantadosi S, Corio R, Lee D, Greenberg B, Koch W, Sidransky D: Genetic progression model for head and neck cancer: implications for field cancerization. Cancer Res. 1996, 56: 2488-2492.
Abate-Shen C, Shen MM: Molecular genetics of prostate cancer. Genes Dev. 2000, 14: 2410-2434.
Hahn WC, Weinberg RA: Rules for making human tumor cells. N Engl J Med. 2002, 347: 1593-1603.
Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70.
Baak JP, Path FR, Hermsen MA, Meijer G, Schmidt J, Janssen EA: Genomics and proteomics in cancer. Eur J Cancer. 2003, 39: 1199-1215.
Emmert-Buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z, Goldstein SR, Weiss RA, Liotta LA: Laser capture microdissection. Science. 1996, 274: 998-1001.
Bonner RF, Emmert-Buck M, Cole K, Pohida T, Chuaqui R, Goldstein S, Liotta LA: Laser capture microdissection: molecular analysis of tissue. Science. 1997, 278: 1481, 1483-
Eltoum IA, Siegal GP, Frost AR: Microdissection of histologic sections: past, present, and future. Adv Anat Pathol. 2002, 9: 316-322.
Bohm M, Wieland I, Schutze K, Rubben H: Microbeam MOMeNT: non-contact laser microdissection of membrane-mounted native tissue. Am J Pathol. 1997, 151: 63-67.
Schutze K, Posl H, Lahr G: Laser micromanipulation systems as universal tools in cellular and molecular biology and in medicine. Cell Mol Biol (Noisy-le-grand). 1998, 44: 735-746.
Roylance R: Methods of molecular analysis: assessing losses and gains in tumours. Mol Pathol. 2002, 55: 25-28.
Bayani JM, Squire JA: Applications of SKY in cancer cytogenetics. Cancer Invest. 2002, 20: 373-386.
Zhao L, Hayes K, Khan Z, Glassman A: Spectral karyotyping study of chromosome abnormalities in human leukemia. Cancer Genet Cytogenet. 2001, 127: 143-147.
Mitelman F, Johansson B, Mertens F: Mitelman Database of Chromosome Aberrations in Cancer. 2003, http://cgap.nci.nih.gov/Chromosomes/Mitelman
Macoska JA, Beheshti B, Rhim JS, Hukku B, Lehr J, Pienta KJ, Squire JA: Genetic characterization of immortalized human prostate epithelial cell cultures. Evidence for structural rearrangements of chromosome 8 and i(8q) chromosome formation in primary tumor-derived cells. Cancer Genet Cytogenet. 2000, 120: 50-57.
Trask BJ: Fluorescence in situ hybridization: applications in cytogenetics and gene mapping. Trends Genet. 1991, 7: 149-154.
Gray JW, Lucas J, Kallioniemi O, Kallioniemi A, Kuo WL, Straume T, Tkachuk D, Tenjin T, Weier HU, Pinkel D: Applications of fluorescence in situ hybridization in biological dosimetry and detection of disease-specific chromosome aberrations. Prog Clin Biol Res. 1991, 372: 399-411.
Wistuba , II, Berry J, Behrens C, Maitra A, Shivapurkar N, Milchgrub S, Mackay B, Minna JD, Gazdar AF: Molecular changes in the bronchial epithelium of patients with small cell lung cancer. Clin Cancer Res. 2000, 6: 2604-2610.
Zhang L, Michelsen C, Cheng X, Zeng T, Priddy R, Rosin MP: Molecular analysis of oral lichen planus. A premalignant lesion?. Am J Pathol. 1997, 151: 323-327.
Maitra A, Wistuba , II, Washington C, Virmani AK, Ashfaq R, Milchgrub S, Gazdar AF, Minna JD: High-resolution chromosome 3p allelotyping of breast carcinomas and precursor lesions demonstrates frequent loss of heterozygosity and a discontinuous pattern of allele loss. Am J Pathol. 2001, 159: 119-130.
Li Z, Meng ZH, Sayeed A, Shalaby R, Ljung BM, Dairkee SH: Genome-wide allelotyping of a new in vitro model system reveals early events in breast cancer progression. Cancer Res. 2002, 62: 5980-5987.
Wistuba , II, Maitra A, Carrasco R, Tang M, Troncoso P, Minna JD, Gazdar AF: High resolution chromosome 3p, 8p, 9q and 22q allelotyping analysis in the pathogenesis of gallbladder carcinoma. Br J Cancer. 2002, 87: 432-440.
Simpson DJ, Bicknell EJ, Buch HN, Cutty SJ, Clayton RN, Farrell WE: Genome-wide amplification and allelotyping of sporadic pituitary adenomas identify novel regions of genetic loss. Genes, Chromosomes Cancer. 2003, 37: 225-236.
Wang DG, Fan JB, Siao CJ, Berno A, Young P, Sapolsky R, Ghandour G, Perkins N, Winchester E, Spencer J, Kruglyak L, Stein L, Hsie L, Topaloglou T, Hubbell E, Robinson E, Mittmann M, Morris MS, Shen N, Kilburn D, Rioux J, Nusbaum C, Rozen S, Hudson TJ, Lander ES, : Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science. 1998, 280: 1077-1082.
Kallioniemi OP: Biochip technologies in cancer research. Ann Med. 2001, 33: 142-147.
Lindblad-Toh K, Tanenbaum DM, Daly MJ, Winchester E, Lui WO, Villapakkam A, Stanton SE, Larsson C, Hudson TJ, Johnson BE, Lander ES, Meyerson M: Loss-of-heterozygosity analysis of small-cell lung carcinomas using single-nucleotide polymorphism arrays. Nat Biotechnol. 2000, 18: 1001-1005.
Welsh J, McClelland M: Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Res. 1990, 18: 7213-7218.
Williams JG, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV: DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res. 1990, 18: 6531-6535.
Siwoski A, Ishkanian A, Garnis C, Zhang L, Rosin M, Lam WL: An efficient method for the assessment of DNA quality of archival microdissected specimens. Mod Path. 2002, 15: 889-892. 10.1097/01.MP.0000024288.63070.4F.
Arribas R, Capella G, Tortola S, Masramon L, Grizzle WE, Perucho M, Peinado MA: Assessment of genomic damage in colorectal cancer by DNA fingerprinting: prognostic applications. J Clin Oncol. 1997, 15: 3230-3240.
de Juan C, Iniesta P, Vega FJ, Peinado MA, Fernandez C, Caldes T, Massa MJ, Lopez JA, Sanchez A, Torres AJ, Balibrea JL, Benito M: Prognostic value of genomic damage in non-small-cell lung cancer. Br J Cancer. 1998, 77: 1971-1977.
de Juan C, Iniesta P, Cruces J, Sanchez A, Massa MJ, Gonzalez-Quevedo R, Torres AJ, Balibrea JL, Benito M: DNA amplification on chromosome 6p12 in non small cell lung cancer detected by arbitrarily primed polymerase chain reaction. Int J Cancer. 1999, 84: 344-349.
Navarro JM, Jorcano JL: The use of arbitrarily primed polymerase chain reaction in cancer research. Electrophoresis. 1999, 20: 283-290.
Yamada T, Kohno T, Navarro JM, Ohwada S, Perucho M, Yokota J: Frequent chromosome 8q gains in human small cell lung carcinoma detected by arbitrarily primed-PCR genomic fingerprinting. Cancer Genet Cytogenet. 2000, 120: 11-17.
Scarpa A, Moore PS, Rigaud G, Menestrina F: Genetic alterations in primary mediastinal B-cell lymphoma: an update. Leukemia & Lymphoma. 2001, 41: 47-53.
Odero MD, Soto JL, Matutes E, Martin-Subero JI, Zudaire I, Rao PH, Cigudosa JC, Ardanaz MT, Chaganti RS, Perucho M, Calasanz MJ: Comparative genomic hybridization and amplotyping by arbitrarily primed PCR in stage A B-CLL. Cancer Genet Cytogenet. 2001, 130: 8-13.
Papadopoulos S, Benter T, Anastassiou G, Pape M, Gerhard S, Bornfeld N, Ludwig WD, Dorken B: Assessment of genomic instability in breast cancer and uveal melanoma by random amplified polymorphic DNA analysis. Int J Cancer. 2002, 99: 193-200.
Viswanathan M, Sangiliyandi G, Vinod SS, Mohanprasad BK, Shanmugam G: Genomic Instability and Tumor-specific Alterations in Oral Squamous Cell Carcinomas Assessed by Inter- (Simple Sequence Repeat) PCR. Clin Cancer Res. 2003, 9: 1057-1062.
Garnis C, Coe BP, Ishkanian A, Zhang L, Rosin MP, Lam WL: Novel regions of amplification on 8q distinct from the MYC locus and frequently altered in oral dysplasia and cancer. Genes, Chromosomes Cancer. 2004, 39: 93-98.
Yamamoto F, Yamamoto M, Soto JL, Kojima E, Wang EN, Perucho M, Sekiya T, Yamanaka H: Notl-Msell methylation-sensitive amplied fragment length polymorhism for DNA methylation analysis of human cancers. Electrophoresis. 2001, 22: 1946-1956.
Yamashita K, Dai T, Dai Y, Yamamoto F, Perucho M: Genetics supersedes epigenetics in colon cancer phenotype. Cancer Cell. 2003, 4: 121-131.
Pinkel D, Segraves R, Sudar D, Clark S, Poole I, Kowbel D, Collins C, Kuo WL, Chen C, Zhai Y, Dairkee SH, Ljung BM, Gray JW, Albertson DG: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet. 1998, 20: 207-211.
Kirchhoff M, Rose H, Petersen BL, Maahr J, Gerdes T, Lundsteen C, Bryndorf T, Kryger-Baggesen N, Christensen L, Engelholm SA, Philip J: Comparative genomic hybridization reveals a recurrent pattern of chromosomal aberrations in severe dysplasia/carcinoma in situ of the cervix and in advanced-stage cervical carcinoma. Genes, Chromosomes Cancer. 1999, 24: 144-150.
Kiechle M, Hinrichs M, Jacobsen A, Luttges J, Pfisterer J, Kommoss F, Arnold N: Genetic imbalances in precursor lesions of endometrial cancer detected by comparative genomic hybridization. Am J Pathol. 2000, 156: 1827-1833.
Kashiwagi H, Uchida K: Genome-wide profiling of gene amplification and deletion in cancer. Human Cell. 2000, 13: 135-141.
Tachdjian G, Aboura A, Lapierre JM, Viguie F: Cytogenetic analysis from DNA by comparative genomic hybridization. Ann Genet. 2000, 43: 147-154.
Kirchhoff M, Rose H, Petersen BL, Maahr J, Gerdes T, Philip J, Lundsteen C: Comparative genomic hybridization reveals non-random chromosomal aberrations in early preinvasive cervical lesions. Cancer Genet Cytogenet. 2001, 129: 47-51.
Marchio A, Terris B, Meddeb M, Pineau P, Duverger A, Tiollais P, Bernheim A, Dejean A: Chromosomal abnormalities in liver cell dysplasia detected by comparative genomic hybridisation. Mol Pathol. 2001, 54: 270-274.
Oga A, Kawauchi S, Izumi H, Ping LX, Furuya T, Sasaki K: New perspectives for tumor pathology provided by comparative genomic hybridization. Int J Clin Oncol. 2002, 7: 133-137.
Aubele M, Werner M, Hofler H: Genetic alterations in presumptive precursor lesions of breast carcinomas. Anal Cell Pathol. 2002, 24: 69-76.
Alcock HE, Stephenson TJ, Royds JA, Hammond DW: Analysis of colorectal tumor progression by microdissection and comparative genomic hybridization. Genes, Chromosomes Cancer. 2003, 37: 369-380.
Ullmann R, Bongiovanni M, Halbwedl I, Petzmann S, Gogg-Kammerer M, Sapino A, Papotti M, Bussolati G, Popper HH: Bronchiolar columnar cell dysplasia--genetic analysis of a novel preneoplastic lesion of peripheral lung. Virchows Archiv. 2003, 442: 429-436.
Brieger J, Jacob R, Riazimand HS, Essig E, Heinrich UR, Bittinger F, Mann WJ: Chromosomal aberrations in premalignant and malignant squamous epithelium. Cancer Genet Cytogenet. 2003, 144: 148-155.
Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, Williams CF, Jeffrey SS, Botstein D, Brown PO: Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nature Genet. 1999, 23: 41-46.
Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 2002, 99: 12963-12968.
Lisitsyn N, Wigler M: Cloning the differences between two complex genomes. Science. 1993, 259: 946-951.
Lucito R, Healy J, Alexander J, Reiner A, Esposito D, Chi M, Rodgers L, Brady A, Sebat J, Troge J, West JA, Rostan S, Nguyen KC, Powers S, Ye KQ, Olshen A, Venkatraman E, Norton L, Wigler M: Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res. 2003, 13: 2291-2305.
Ishkanian AS, Malloff CA, Watson SK, DeLeeuw RJ, Chi B, Coe BP, Snijders A, Albertson DG, Pinkel D, Marra MA, Ling V, MacAulay C, Lam WL: A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet. 2004, 36: 299-303.
Albertson DG, Pinkel D: Genomic microarrays in human genetic disease and cancer. Hum Mol Genet. 2003, 12 Spec No 2: R145-52.
Albertson DG, Ylstra B, Segraves R, Collins C, Dairkee SH, Kowbel D, Kuo WL, Gray JW, Pinkel D: Quantitative mapping of amplicon structure by array CGH identifies CYP24 as a candidate oncogene. Nat Genet. 2000, 25: 144-146.
Garnis C, Coe BP, Zhang L, Rosin MP, Lam WL: Overexpression of LRP12, a gene contained within an 8q22 amplicon identified by high-resolution array CGH analysis of oral squamous cell carcinomas. Oncogene. 2004, 23: 2582-2586-
Garnis C, MacAulay C, Lam S, Lam WL: Genetic Alteration On 8q Distinct From MYC In Bronchial Carcinoma In Situ Lesions. Lung Cancer. in press:
Bruder CE, Hirvela C, Tapia-Paez I, Fransson I, Segraves R, Hamilton G, Zhang XX, Evans DG, Wallace AJ, Baser ME, Zucman-Rossi J, Hergersberg M, Boltshauser E, Papi L, Rouleau GA, Poptodorov G, Jordanova A, Rask-Andersen H, Kluwe L, Mautner V, Sainio M, Hung G, Mathiesen T, Moller C, Pulst SM, Harder H, Heiberg A, Honda M, Niimura M, Sahlen S, Blennow E, Albertson DG, Pinkel D, Dumanski JP: High resolution deletion analysis of constitutional DNA from neurofibromatosis type 2 (NF2) patients using microarray-CGH. Hum Mol Genet. 2001, 10: 271-282.
Monni O, Hyman E, Mousses S, Barlund M, Kallioniemi A, Kallioniemi OP: From chromosomal alterations to target genes for therapy: integrating cytogenetic and functional genomic views of the breast cancer genome. Sem Cancer Biol. 2001, 11: 395-401. 10.1006/scbi.2001.0395.
Massion PP, Kuo WL, Stokoe D, Olshen AB, Treseler PA, Chin K, Chen C, Polikoff D, Jain AN, Pinkel D, Albertson DG, Jablons DM, Gray JW: Genomic copy number analysis of non-small cell lung cancer using array comparative genomic hybridization: implications of the phosphatidylinositol 3-kinase pathway. Cancer Res. 2002, 62: 3636-3640.
Buckley PG, Mantripragada KK, Benetkiewicz M, Tapia-Paez I, Diaz De Stahl T, Rosenquist M, Ali H, Jarbo C, De Bustos C, Hirvela C, Sinder Wilen B, Fransson I, Thyr C, Johnsson BI, Bruder CE, Menzel U, Hergersberg M, Mandahl N, Blennow E, Wedell A, Beare DM, Collins JE, Dunham I, Albertson D, Pinkel D, Bastian BC, Faruqi AF, Lasken RS, Ichimura K, Collins VP, Dumanski JP: A full-coverage, high-resolution human chromosome 22 genomic microarray for clinical and research applications. Hum Mol Genet. 2002, 11: 3221-3229.
Garnis C, Baldwin C, Zhang L, Rosin MP, Lam WL: Use of complete coverage array comparative genomic hybridization to define copy number alterations on chromosome 3p in oral squamous cell carcinomas. Cancer Res. 2003, 63: 8582-8585.
Garnis C, Campbell J, Zhang L, Rosin MP, Lam WL: OCGR Array, an oral cancer genomic regional array for comparative genomic hybridization analysis. Oral Oncol. :
Snijders AM, Nowak N, Segraves R, Blackwood S, Brown N, Conroy J, Hamilton G, Hindle AK, Huey B, Kimura K, Law S, Myambo K, Palmer J, Ylstra B, Yue JP, Gray JW, Jain AN, Pinkel D, Albertson DG: Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet. 2001, 29: 263-264.
Chung YJ, Jonkers J, Kitson H, Fiegler H, Humphray S, Scott C, Hunt S, Yu Y, Nishijima I, Velds A, Holstege H, Carter N, Bradley A: A whole-genome mouse BAC microarray with 1-Mb resolution for analysis of DNA copy number changes by array comparative genomic hybridization. Genome Res. 2004, 14: 188-196.
Greshock J, Naylor TL, Margolin A, Diskin S, Cleaver SH, Futreal PA, deJong PJ, Zhao S, Liebman M, Weber BL: 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis. Genome Res. 2004, 14: 179-187.
Wilhelm M, Veltman JA, Olshen AB, Jain AN, Moore DH, Presti J. C., Jr., Kovacs G, Waldman FM: Array-based comparative genomic hybridization for the differential diagnosis of renal cell cancer. Cancer Res. 2002, 62: 957-960.
Sanchez-Izquierdo D, Buchonnet G, Siebert R, Gascoyne RD, Climent J, Karran L, Marin M, Blesa D, Horsman D, Rosenwald A, Staudt LM, Albertson DG, Du MQ, Ye H, Marynen P, Garcia-Conde J, Pinkel D, Dyer MJ, Martinez-Climent JA: MALT1 is deregulated by both chromosomal translocation and amplification in B-cell non-Hodgkin lymphoma. Blood. 2003, 101: 4539-4546.
Veltman JA, Fridlyand J, Pejavar S, Olshen AB, Korkola JE, DeVries S, Carroll P, Kuo WL, Pinkel D, Albertson D, Cordon-Cardo C, Jain AN, Waldman FM: Array-based Comparative Genomic Hybridization for Genome-Wide Screening of DNA Copy Number in Bladder Tumors. Cancer Res. 2003, 63: 2872-2880.
Albertson DG: Profiling breast cancer by array CGH. Breast Cancer Res Treat. 2003, 78: 289-298.
Paris PL, Albertson DG, Alers JC, Andaya A, Carroll P, Fridlyand J, Jain AN, Kamkar S, Kowbel D, Krijtenburg PJ, Pinkel D, Schroder FH, Vissers KJ, Watson VJ, Wildhagen MF, Collins C, Van Dekken H: High-resolution analysis of paraffin-embedded and formalin-fixed prostate tumors using comparative genomic hybridization to genomic microarrays. Am J Pathol. 2003, 162: 763-770.
Kraus J, Pantel K, Pinkel D, Albertson DG, Speicher MR: High-resolution genomic profiling of occult micrometastatic tumor cells. Genes, Chromosomes Cancer. 2003, 36: 159-166.
Watson SK, Deleeuw RJ, Ishkanian AS, Malloff CA, Lam WL: Methods for high throughput validation of amplified fragment pools of BAC DNA for constructing high resolution CGH arrays. BMC Genomics. 2004, 5: 6-
Chi B, deLeeuw RJ, Coe BP, MacAulay C, Lam WL: SeeGH - A Software Tool for Visualization of Whole Genome Array Comparative Genomic Hybridization Data. BMC Bioinformatics. 2004, 5 (13):
Wang TL, Maierhofer C, Speicher MR, Lengauer C, Vogelstein B, Kinzler KW, Velculescu VE: Digital karyotyping. Proc Natl Acad Sci U S A. 2002, 99: 16156-16161.
Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene expression. Science. 1995, 270: 484-487.
DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM: Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet. 1996, 14: 457-460.
Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP: Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci USA. 1994, 91: 5022-5026.
Yeatman TJ: The future of cancer management: translating the genome, transcriptome, and proteome. Ann Surg Oncol. 2003, 10: 7-14.
Relogio A, Schwager C, Richter A, Ansorge W, Valcarcel J: Optimization of oligonucleotide-based DNA microarrays. Nucleic Acids Res. 2002, 30: e51-
Quackenbush J: Microarray data normalization and transformation. Nature Genet. 2002, 32: 496-501.
Mohr S, Leikauf GD, Keith G, Rihn BH: Microarrays as cancer keys: an array of possibilities. J Clin Oncol. 2002, 20: 3165-3175.
Alizadeh A, Eisen M, Davis RE, Ma C, Sabet H, Tran T, Powell JI, Yang L, Marti GE, Moore DT, Hudson J. R., Jr., Chan WC, Greiner T, Weisenburger D, Armitage JO, Lossos I, Levy R, Botstein D, Brown PO, Staudt LM: The lymphochip: a specialized cDNA microarray for the genomic-scale analysis of gene expression in normal and malignant lymphocytes. Cold Spring Harb Symp Quant Biol. 1999, 64: 71-78.
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537.
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J., Jr., Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Staudt LM, : Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000, 403: 503-511.
Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark EJ, Lander ES, Wong W, Johnson BE, Golub TR, Sugarbaker DJ, Meyerson M: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001, 98: 13790-13795.
Garber ME, Troyanskaya OG, Schluens K, Petersen S, Thaesler Z, Pacyna-Gengelbach M, van de Rijn M, Rosen GD, Perou CM, Whyte RI, Altman RB, Brown PO, Botstein D, Petersen I: Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA. 2001, 98: 13784-13789.
Wigle DA, Jurisica I, Radulovich N, Pintilie M, Rossant J, Liu N, Lu C, Woodgett J, Seiden I, Johnston M, Keshavjee S, Darling G, Winton T, Breitkreutz BJ, Jorgenson P, Tyers M, Shepherd FA, Tsao MS: Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res. 2002, 62: 3005-3008.
Hedenfalk IA, Ringner M, Trent JM, Borg A: Gene expression in inherited breast cancer. Adv Cancer Res. 2002, 84: 1-34.
van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415: 530-536.
Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS, Ray TS, Koval MA, Last KW, Norton A, Lister TA, Mesirov J, Neuberg DS, Lander ES, Aster JC, Golub TR: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002, 8: 68-74.
Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, McQuary P, Payette T, Pistone M, Stecker K, Zhang BM, Zhou YX, Varnholt H, Smith B, Gadd M, Chatfield E, Kessler J, Baer TM, Erlander MG, Sgroi DC: Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA. 2003, 100: 5974-5979.
Wang E, Miller LD, Ohnmacht GA, Liu ET, Marincola FM: High-fidelity mRNA amplification for gene profiling. Nat Biotechnol. 2000, 18: 457-459.
Zhao H, Hastie T, Whitfield ML, Borresen-Dale AL, Jeffrey SS: Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis. BMC Genomics. 2002, 3: 31-
Hermeking H: Serial analysis of gene expression and cancer. Curr Opin Oncol. 2003, 15: 44-49.
Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome. Nat Biotechnol. 2002, 20: 508-512.
Datson NA, van der Perk-de Jong J, van den Berg MP, de Kloet ER, Vreugdenhil E: MicroSAGE: a modified procedure for serial analysis of gene expression in limited amounts of tissue. Nucleic Acids Res. 1999, 27: 1300-1307.
Vilain C, Libert F, Venet D, Costagliola S, Vassart G: Small amplified RNA-SAGE: an alternative approach to study transcriptome from limiting amount of mRNA. Nucleic Acids Res. 2003, 31: e24.-
Lash AE, Tolstoshev CM, Wagner L, Schuler GD, Strausberg RL, Riggins GJ, Altschul SF: SAGEmap: a public gene expression resource. Genome Res. 2000, 10: 1051-1060.
Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, Morin PJ, Buetow KH, Strausberg RL, De Souza SJ, Riggins GJ: An anatomy of normal and malignant gene expression. Proc Natl Acad Sci USA. 2002, 99: 11287-11292.
Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW: Gene expression profiles in normal and cancer cells. Science. 1997, 276: 1268-1272.
Hibi K, Liu Q, Beaudry GA, Madden SL, Westra WH, Wehage SL, Yang SC, Heitmiller RF, Bertelsen AH, Sidransky D, Jen J: Serial analysis of gene expression in non-small cell lung cancer. Cancer Res. 1998, 58: 5690-5694.
Ferguson AT, Evron E, Umbricht CB, Pandita TK, Chan TA, Hermeking H, Marks JR, Lambers AR, Futreal PA, Stampfer MR, Sukumar S: High frequency of hypermethylation at the 14-3-3 sigma locus leads to gene silencing in breast cancer. Proc Natl Acad Sci USA. 2000, 97: 6049-6054.
St Croix B, Rago C, Velculescu V, Traverso G, Romans KE, Montgomery E, Lal A, Riggins GJ, Lengauer C, Vogelstein B, Kinzler KW: Genes expressed in human tumor endothelium. Science. 2000, 289: 1197-1202.
Krop IE, Sgroi D, Porter DA, Lunetta KL, LeVangie R, Seth P, Kaelin CM, Rhei E, Bosenberg M, Schnitt S, Marks JR, Pagon Z, Belina D, Razumovic J, Polyak K: HIN-1, a putative cytokine highly expressed in normal but not cancerous mammary epithelial cells. Proc Natl Acad Sci USA. 2001, 98: 9796-9801.
Porter DA, Krop IE, Nasser S, Sgroi D, Kaelin CM, Marks JR, Riggins G, Polyak K: A SAGE (serial analysis of gene expression) view of breast tumor progression. Cancer Res. 2001, 61: 5697-5702.
Argani P, Iacobuzio-Donahue C, Ryu B, Rosty C, Goggins M, Wilentz RE, Murugesan SR, Leach SD, Jaffee E, Yeo CJ, Cameron JL, Kern SE, Hruban RH: Mesothelin is overexpressed in the vast majority of ductal adenocarcinomas of the pancreas: identification of a new pancreatic cancer marker by serial analysis of gene expression (SAGE). Clin Cancer Res. 2001, 7: 3862-3868.
Argani P, Rosty C, Reiter RE, Wilentz RE, Murugesan SR, Leach SD, Ryu B, Skinner HG, Goggins M, Jaffee EM, Yeo CJ, Cameron JL, Kern SE, Hruban RH: Discovery of new markers of cancer through serial analysis of gene expression: prostate stem cell antigen is overexpressed in pancreatic adenocarcinoma. Cancer Res. 2001, 61: 4320-4324.
Nacht M, Dracheva T, Gao Y, Fujii T, Chen Y, Player A, Akmaev V, Cook B, Dufault M, Zhang M, Zhang W, Guo M, Curran J, Han S, Sidransky D, Buetow K, Madden SL, Jen J: Molecular characteristics of non-small cell lung cancer. Proc Natl Acad Sci USA. 2001, 98: 15203-15208.
Buckhaults P, Rago C, St Croix B, Romans KE, Saha S, Zhang L, Vogelstein B, Kinzler KW: Secreted and cell surface genes expressed in benign and malignant colorectal tumors. Cancer Res. 2001, 61: 6996-7001.
Saha S, Bardelli A, Buckhaults P, Velculescu VE, Rago C, St Croix B, Romans KE, Choti MA, Lengauer C, Kinzler KW, Vogelstein B: A phosphatase associated with metastasis of colorectal cancer. Science. 2001, 294: 1343-1346.
Waghray A, Schober M, Feroze F, Yao F, Virgin J, Chen YQ: Identification of differentially expressed genes by serial analysis of gene expression in human prostate cancer. Cancer Res. 2001, 61: 4283-4286.
Yamashita T, Kaneko S, Hashimoto S, Sato T, Nagai S, Toyoda N, Suzuki T, Kobayashi K, Matsushima K: Serial analysis of gene expression in chronic hepatitis C and hepatocellular carcinoma. Biochem Biophys Res Commun. 2001, 282: 647-654.
Caldwell MC, Hough C, Furer S, Linehan WM, Morin PJ, Gorospe M: Serial analysis of gene expression in renal carcinoma cells reveals VHL- dependent sensitivity to TNFalpha cytotoxicity. Oncogene. 2002, 21: 929-936.
Iacobuzio-Donahue CA, Maitra A, Shen-Ong GL, van Heek T, Ashfaq R, Meyer R, Walter K, Berg K, Hollingsworth MA, Cameron JL, Yeo CJ, Kern SE, Goggins M, Hruban RH: Discovery of novel tumor markers of pancreatic cancer using global gene expression technology. Am J Pathol. 2002, 160: 1239-1249.
Untergasser G, Koch HB, Menssen A, Hermeking H: Characterization of epithelial senescence by serial analysis of gene expression: identification of genes potentially involved in prostate cancer. Cancer Res. 2002, 62: 6255-6262.
Lee JY, Eom EM, Kim DS, Ha-Lee YM, Lee DH: Analysis of gene expression profiles of gastric normal and cancer tissues by SAGE. Genomics. 2003, 82: 78-85.
Lonergan KM, Shadeo A, Kim JC, Chi B, LeRiche J, Jones S, Tsao MS, Marra MA, MacAulay C, Lam S, Lam WL: Gene Expression Profiles of Developmental Stages of Non-small Cell Lung Carcinoma [abstract]. Lung Cancer. 2003, 41: S123-
Porter D, Lahti-Domenici J, Keshaviah A, Bae YK, Argani P, Marks J, Richardson A, Cooper A, Strausberg R, Riggins GJ, Schnitt S, Gabrielson E, Gelman R, Polyak K: Molecular markers in ductal carcinoma in situ of the breast. Mol Cancer Res. 2003, 1: 362-375.
Zhou M, Chinnaiyan AM, Kleer CG, Lucas PC, Rubin MA: Alpha-Methylacyl-CoA racemase: a novel tumor marker over-expressed in several human cancers and their precursor lesions. Am J Surg Pathol. 2002, 26: 926-931.
Freeman WM, Walker SJ, Vrana KE: Quantitative RT-PCR: pitfalls and potential. Biotechniques. 1999, 26: 112-22, 124-5..
Higuchi R, Fockler C, Dollinger G, Watson R: Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology (N Y). 1993, 11: 1026-1030.
Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR. Genome Res. 1996, 6: 986-994.
Gibson UE, Heid CA, Williams PM: A novel method for real time quantitative RT-PCR. Genome Res. 1996, 6: 995-1001.
Bustin SA: Quantification of mRNA using real-time reverse transcription PCR (RT- PCR): trends and problems. J Mol Endocrinol. 2002, 29: 23-39.
Walker NJ: Tech.Sight. A technique whose time has come. Science. 2002, 296: 557-559.
Kononen J, Bubendorf L, Kallioniemi A, Barlund M, Schraml P, Leighton S, Torhorst J, Mihatsch MJ, Sauter G, Kallioniemi OP: Tissue microarrays for high-throughput molecular profiling of tumor specimens.[comment]. Nature Medicine. 1998, 4: 844-847.
Kallioniemi OP, Wagner U, Kononen J, Sauter G: Tissue microarray technology for high-throughput molecular profiling of cancer. Hum Mol Genet. 2001, 10: 657-662.
Simon R, Sauter G: Tissue microarrays for miniaturized high-throughput molecular profiling of tumors. Exp Hematol. 2002, 30: 1365-1372.
Mousses S, Kallioniemi A, Kauraniemi P, Elkahloun A, Kallioniemi OP: Clinical and functional target validation using tissue and cell microarrays. Curr Opin Chem Biol. 2002, 6: 97-101.
MacAulay C, Korbelik J, Matisic J: Cytology Microarrays [abstract]. International Conference on Applied Genomics Proceedings. 2003, 9th ESACP/16th ISDQP Meeting (ICAG-2003): O42-
Wulfkuhle JD, Liotta LA, Petricoin EF: Proteomic applications for the early detection of cancer. Nat Rev Cancer. 2003, 3: 267-275.
O'Farrell PH: High resolution two-dimensional electrophoresis of proteins. J Biol Chem. 1975, 250: 4007-4021.
Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol. 1999, 17: 994-999.
Patton WF, Schulenberg B, Steinberg TH: Two-dimensional gel electrophoresis; better than a poke in the ICAT?. Curr Opin Biotechnol. 2002, 13: 321-328.
Wiley WC, Mclaren IH: Time-of-fight mass spectrometer with improved resolution. Review of Scientific Instruments. 1955, 26: 1150-1157.
Guilhaus M, Selby D, Mlynski V: Orthogonal acceleration time-of-flight mass spectrometry. Mass Spectrom Rev. 2000, 19: 65-107.
Hutchens TW, Yip TT: New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun. Mass Spectrom. 1993, 7: 576-580.
Merchant M, Weinberger SR: Recent advancements in surface-enhanced laser desorption/ionization-time of flight-mass spectrometry. Electrophoresis. 2000, 21: 1164-1177.
de Wildt RM, Mundy CR, Gorick BD, Tomlinson IM: Antibody arrays for high-throughput screening of antibody-antigen interactions. Nat Biotechnol. 2000, 18: 989-994.
Smith GP: Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science. 1985, 228: 1315-1317.
Benhar I: Biotechnological applications of phage and cell display. Biotechnol Adv. 2001, 19: 1-33.
Giordano RJ, Cardo-Vila M, Lahdenranta J, Pasqualini R, Arap W: Biopanning and rapid analysis of selective interactive ligands. Nat Med. 2001, 7: 1249-1253.
Nephew KP, Huang TH: Epigenetic gene silencing in cancer initiation and progression. Cancer Lett. 2003, 190: 125-133.
Dunn BK: Hypomethylation: one side of a larger picture. Ann N Y Acad Sci. 2003, 983: 28-42.
Smiraglia DJ, Plass C: The development of CpG island methylation biomarkers using restriction landmark genomic scanning. Ann N Y Acad Sci. 2003, 983: 110-119.
Brown CJ, Greally JM: A stain upon the silence: genes escaping X inactivation. Trends Genet. 2003, 19: 432-438.
Hayashizaki Y, Hirotsune S, Okazaki Y, Hatada I, Shibata H, Kawai J, Hirose K, Watanabe S, Fushiki S, Wada S, : Restriction landmark genomic scanning method and its various applications. Electrophoresis. 1993, 14: 251-258.
Costello JF, Fruhwald MC, Smiraglia DJ, Rush LJ, Robertson GP, Gao X, Wright FA, Feramisco JD, Peltomaki P, Lang JC, Schuller DE, Yu L, Bloomfield CD, Caligiuri MA, Yates A, Nishikawa R, Su Huang H, Petrelli NJ, Zhang X, O'Dorisio MS, Held WA, Cavenee WK, Plass C: Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nat Genet. 2000, 24: 132-138.
Costello JF, Smiraglia DJ, Plass C: Restriction landmark genome scanning. Methods. 2002, 27: 144-149.
Zardo G, Tiirikainen MI, Hong C, Misra A, Feuerstein BG, Volik S, Collins CC, Lamborn KR, Bollen A, Pinkel D, Albertson DG, Costello JF: Integrated genomic and epigenomic analyses pinpoint biallelic gene inactivation in tumors. Nat Genet. 2002, 32: 453-458.
Weinmann AS, Yan PS, Oberley MJ, Huang TH, Farnham PJ: Isolating human transcription factor targets by coupling chromatin immunoprecipitation and CpG island microarray analysis. Genes Dev. 2002, 16: 235-244.
Yan PS, Efferth T, Chen HL, Lin J, Rodel F, Fuzesi L, Huang TH: Use of CpG island microarrays to identify colorectal tumors with a high degree of concurrent methylation. Methods. 2002, 27: 162-169.
Chen CM, Chen HL, Hsiau TH, Hsiau AH, Shi H, Brock GJ, Wei SH, Caldwell CW, Yan PS, Huang TH: Methylation target array for rapid analysis of CpG island hypermethylation in multiple tissue genomes. Am J Pathol. 2003, 163: 37-45.
Ballestar E, Paz MF, Valle L, Wei S, Fraga MF, Espada J, Cigudosa JC, Huang TH, Esteller M: Methyl-CpG binding proteins identify novel sites of epigenetic inactivation in human cancer. EMBO J. 2003, 22: 6335-6345.
Grunau C, Clark SJ, Rosenthal A: Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res. 2001, 29: E65-5.
Belinsky SA, Nikula KJ, Palmisano WA, Michels R, Saccomanno G, Gabrielson E, Baylin SB, Herman JG: Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci USA. 1998, 95: 11891-11896.
Umbricht CB, Evron E, Gabrielson E, Ferguson A, Marks J, Sukumar S: Hypermethylation of 14-3-3 sigma (stratifin) is an early event in breast cancer. Oncogene. 2001, 20: 3348-3353.
Burbee DG, Forgacs E, Zochbauer-Muller S, Shivakumar L, Fong K, Gao B, Randle D, Kondo M, Virmani A, Bader S, Sekido Y, Latif F, Milchgrub S, Toyooka S, Gazdar AF, Lerman MI, Zabarovsky E, White M, Minna JD: Epigenetic inactivation of RASSF1A in lung and breast cancers and malignant phenotype suppression. J Natl Cancer Inst. 2001, 93: 691-699.
Young J, Biden KG, Simms LA, Huggard P, Karamatic R, Eyre HJ, Sutherland GR, Herath N, Barker M, Anderson GJ, Fitzpatrick DR, Ramm GA, Jass JR, Leggett BA: HPP1: a transmembrane protein-encoding gene commonly methylated in colorectal polyps and cancers. Proc Natl Acad Sci USA. 2001, 98: 265-270.
Lehmann U, Langer F, Feist H, Glockner S, Hasemeier B, Kreipe H: Quantitative assessment of promoter hypermethylation during breast cancer development. Am J Pathol. 2002, 160: 605-612.
Suzuki H, Gabrielson E, Chen W, Anbazhagan R, van Engeland M, Weijenberg MP, Herman JG, Baylin SB: A genomic screen for genes upregulated by demethylation and histone deacetylase inhibition in human colorectal cancer. Nat Genet. 2002, 31: 141-149.
Tijsterman M, Ketting RF, Plasterk RH: The genetics of RNA silencing. Annu Rev Genet. 2002, 36: 489-519.
Mousses S, Caplen NJ, Cornelison R, Weaver D, Basik M, Hautaniemi S, Elkahloun AG, Lotufo RA, Choudary A, Dougherty ER, Suh E, Kallioniemi O: RNAi Microarray Analysis in Cultured Mammalian Cells. Genome Res. 2003, 13: 2341-2347.
Kumar R, Conklin DS, Mittal V: High-Throughput Selection of Effective RNAi Probes for Gene Silencing. Genome Res. 2003, 13: 2333-2340.
Dieterich C, Cusack B, Wang H, Rateitschak K, Krause A, Vingron M: Annotating regulatory DNA based on man-mouse genomic comparison. Bioinformatics. 2002, 18 Suppl 2: S84-90.
Modrek B, Lee CJ: Alternative splicing in the human, mouse and rat genomes is associated with an increased frequency of exon creation and/or loss. Nat Genet. 2003, 34: 177-180.
Guigo R, Dermitzakis ET, Agarwal P, Ponting CP, Parra G, Reymond A, Abril JF, Keibler E, Lyle R, Ucla C, Antonarakis SE, Brent MR: Comparison of mouse and human genomes followed by experimental verification yields an estimated 1, 019 additional genes. Proc Natl Acad Sci USA. 2003, 100: 1140-1145.
Clark AG, Glanowski S, Nielsen R, Thomas PD, Kejariwal A, Todd MA, Tanenbaum DM, Civello D, Lu F, Murphy B, Ferriera S, Wang G, Zheng X, White TJ, Sninsky JJ, Adams MD, Cargill M: Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science. 2003, 302: 1960-1963.
Perkins AS: Functional genomics in the mouse. Funct Integr Genomics. 2002, 2: 81-91.
Hogan B, Beddington R, Constantini F, Lacy E: Manipulating the Mouse Embryo: A Laboratory Manual. 1994, 500-Cold Spring Harbor Laboratory Press, 2nd
Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O'Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES: Initial sequencing and comparative analysis of the mouse genome. Nature. 2002, 420: 520-562.
Capecchi MR: Altering the genome by homologous recombination. Science. 1989, 244: 1288-1292.
Macleod KF, Jacks T: Insights into cancer from transgenic mouse models. J Pathol. 1999, 187: 43-60.
Lakso M, Sauer B, Mosinger B, Jr., Lee EJ, Manning RW, Yu SH, Mulder KL, Westphal H: Targeted oncogene activation by site-specific recombination in transgenic mice. Proc Natl Acad Sci USA. 1992, 89: 6232-6236.
Sauer B: Inducible gene targeting in mice using the Cre/lox system. Methods. 1998, 14: 381-392.
Metzger D, Chambon P: Site- and time-specific gene targeting in the mouse. Methods. 2001, 24: 71-80.
Popescu NC, Zimonjic DB: Chromosome and gene alterations in breast cancer as markers for diagnosis and prognosis as well as pathogenetic targets for therapy. Am J Med Genet. 2002, 115: 142-149.
Miyakis S, Spandidos DA: Allelic loss in breast cancer. Cancer Detect Prevent. 2002, 26: 426-434.
Huebner K, Hadaczek P, Siprashvili Z, Druck T, Croce CM: The FHIT gene, a multiple tumor suppressor gene encompassing the carcinogen sensitive chromosome fragile site, FRA3B. Biochim Biophys Acta. 1997, 1332: M65-70.
Porter PL: Molecular markers of tumor initiation and progression. Curr Opin Genet Dev. 2001, 11: 60-63.
Polyak K, Riggins GJ: Gene discovery using the serial analysis of gene expression technique: implications for cancer research. J Clin Oncol. 2001, 19: 2948-2958.
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumours. Nature. 2000, 406: 747-752.
Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, Wilfond B, Borg A, Trent J: Gene-expression profiles in hereditary breast cancer. N Engl J Med. 2001, 344: 539-548.
Chinnaiyan AM, Rubin MA: Gene-expression profiles in hereditary breast cancer. Adv Anat Pathol. 2002, 9: 1-6.
Domchek SM, Weber BL: Recent advances in breast cancer biology. Curr Opin Oncol. 2002, 14: 589-593.
Dillon DA: Molecular markers in the diagnosis and staging of breast cancer. Semin Radiat Oncol. 2002, 12: 305-318.
Wulfkuhle JD, Sgroi DC, Krutzsch H, McLean K, McGarvey K, Knowlton M, Chen S, Shu H, Sahin A, Kurek R, Wallwiener D, Merino MJ, Petricoin E. F., 3rd, Zhao Y, Steeg PS: Proteomics of human breast ductal carcinoma in situ. Cancer Res. 2002, 62: 6740-6749.
Yang X, Yan L, Davidson NE: DNA methylation in breast cancer. Endocrine-Related Cancer. 2001, 8: 115-127.
Evron E, Umbricht CB, Korz D, Raman V, Loeb DM, Niranjan B, Buluwela L, Weitzman SA, Marks J, Sukumar S: Loss of cyclin D2 expression in the majority of breast cancers is associated with promoter hypermethylation. Cancer Res. 2001, 61: 2782-2787.
Evron E, Dooley WC, Umbricht CB, Rosenthal D, Sacchi N, Gabrielson E, Soito AB, Hung DT, Ljung B, Davidson NE, Sukumar S: Detection of breast cancer cells in ductal lavage fluid by methylation-specific PCR. Lancet. 2001, 357: 1335-1336.
Lehmann U, Celikkaya G, Hasemeier B, Langer F, Kreipe H: Promoter hypermethylation of the death-associated protein kinase gene in breast cancer is associated with the invasive lobular subtype. Cancer Res. 2002, 62: 6634-6638.
Brothman AR: Cytogenetics and molecular genetics of cancer of the prostate. Am J Med Genet. 2002, 115: 150-156.
DeMarzo AM, Nelson WG, Isaacs WB, Epstein JI: Pathological and molecular aspects of prostate cancer. Lancet. 2003, 361: 955-964.
Nupponen N, Visakorpi T: Molecular biology of progression of prostate cancer. Eur Urol. 1999, 35: 351-354.
Lou W, Krill D, Dhir R, Becich MJ, Dong JT, Frierson H. F., Jr., Isaacs WB, Isaacs JT, Gao AC: Methylation of the CD44 metastasis suppressor gene in human prostate cancer. Cancer Res. 1999, 59: 2329-2331.
Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP, Rubin MA, Chinnaiyan AM: The polycomb group protein EZH2 is involved in progression of prostate cancer.[comment]. Nature. 2002, 419: 624-629.
Yamanaka M, Watanabe M, Yamada Y, Takagi A, Murata T, Takahashi H, Suzuki H, Ito H, Tsukino H, Katoh T, Sugimura Y, Shiraishi T: Altered methylation of multiple genes in carcinogenesis of the prostate. Int J Cancer. 2003, 106: 382-387.
Vanaja DK, Cheville JC, Iturria SJ, Young CY: Transcriptional silencing of zinc finger protein 185 identified by expression profiling is associated with prostate cancer progression. Cancer Res. 2003, 63: 3877-3882.
Dumur CI, Dechsukhum C, Ware JL, Cofield SS, Best AM, Wilkinson DS, Garrett CT, Ferreira-Gonzalez A: Genome-wide detection of LOH in prostate cancer using human SNP microarray technology. Genomics. 2003, 81: 260-269.
Karan D, Lin MF, Johansson SL, Batra SK: Current status of the molecular genetics of human prostatic adenocarcinomas. Int J Cancer. 2003, 103: 285-293.
Matsuyama H, Pan Y, Yoshihiro S, Kudren D, Naito K, Bergerheim US, Ekman P: Clinical significance of chromosome 8p, 10q, and 16q deletions in prostate cancer. Prostate. 2003, 54: 103-111.
Bubendorf L, Kolmer M, Kononen J, Koivisto P, Mousses S, Chen Y, Mahlamaki E, Schraml P, Moch H, Willi N, Elkahloun AG, Pretlow TG, Gasser TC, Mihatsch MJ, Sauter G, Kallioniemi OP: Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays. J Natl Cancer Inst. 1999, 91: 1758-1764.
Paweletz CP, Charboneau L, Bichsel VE, Simone NL, Chen T, Gillespie JW, Emmert-Buck MR, Roth MJ, Petricoin Iii Ef, Liotta LA: Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene. 2001, 20: 1981-1989.
Luo JH, Yu YP, Cieply K, Lin F, Deflavia P, Dhir R, Finkelstein S, Michalopoulos G, Becich M: Gene expression analysis of prostate cancers. Mol Carcinog. 2002, 33: 25-35.
Brooks JD: Microarray analysis in prostate cancer research. Curr Opin Urol. 2002, 12: 395-399.
Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell. 2002, 1: 203-209.
Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature. 2001, 412: 822-826.
Rhodes DR, Sanda MG, Otte AP, Chinnaiyan AM, Rubin MA: Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer. J Natl Cancer Inst. 2003, 95: 661-668.
Minna JD, Fong K, Zochbauer-Muller S, Gazdar AF: Molecular pathogenesis of lung cancer and potential translational applications. Cancer J. 2002, 8 Suppl 1: S41-6.
Girard L, Zochbauer-Muller S, Virmani AK, Gazdar AF, Minna JD: Genome-wide allelotyping of lung cancer identifies new regions of allelic loss, differences between small cell lung cancer and non-small cell lung cancer, and loci clustering. Cancer Res. 2000, 60: 4894-4906.
Luk C, Tsao MS, Bayani J, Shepherd F, Squire JA: Molecular cytogenetic analysis of non-small cell lung carcinoma by spectral karyotyping and comparative genomic hybridization. Cancer Genet Cytogenet. 2001, 125: 87-99.
Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, Lizyness ML, Kuick R, Hayasaka S, Taylor JM, Iannettoni MD, Orringer MB, Hanash S: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002, 8: 816-824.
Miura K, Bowman ED, Simon R, Peng AC, Robles AI, Jones RT, Katagiri T, He P, Mizukami H, Charboneau L, Kikuchi T, Liotta LA, Nakamura Y, Harris CC: Laser capture microdissection and microarray expression analysis of lung adenocarcinoma reveals tobacco smoking- and prognosis-related molecular profiles. Cancer Res. 2002, 62: 3244-3250.
Nakamura H, Saji H, Ogata A, Hosaka M, Hagiwara M, Saijo T, Kawasaki N, Kato H: cDNA microarray analysis of gene expression in pathologic Stage IA nonsmall cell lung carcinomas. Cancer. 2003, 97: 2798-2805.
Varella-Garcia M, Gemmill RM, Rabenhorst SH, Lotto A, Drabkin HA, Archer PA, Franklin WA: Chromosomal duplication accompanies allelic loss in non-small cell lung carcinoma. Cancer Res. 1998, 58: 4701-4707.
Petersen S, Aninat-Meyer M, Schluns K, Gellert K, Dietel M, Petersen I: Chromosomal alterations in the clonal evolution to the metastatic stage of squamous cell carcinomas of the lung. Br J Cancer. 2000, 82: 65-73.
Shibuya K, Fujisawa T, Hoshino H, Baba M, Saitoh Y, Iizasa T, Sekine Y, Suzuki M, Hiroshima K, Ohwada H: Increased telomerase activity and elevated hTERT mRNA expression during multistage carcinogenesis of squamous cell carcinoma of the lung. Cancer. 2001, 92: 849-855.
Tan DF, Huberman JA, Hyland A, Loewen GM, Brooks JS, Beck AF, Todorov IT, Bepler G: MCM2--a promising marker for premalignant lesions of the lung: a cohort study. BMC Cancer. 2001, 1: 6-
Osada H, Tatematsu Y, Yatabe Y, Nakagawa T, Konishi H, Harano T, Tezel E, Takada M, Takahashi T: Frequent and histological type-specific inactivation of 14-3-3sigma in human lung cancers. Oncogene. 2002, 21: 2418-2424.
Virmani AK, Tsou JA, Siegmund KD, Shen LY, Long TI, Laird PW, Gazdar AF, Laird-Offringa IA: Hierarchical clustering of lung cancer cell lines using DNA methylation markers. Cancer Epidemiol Biomarkers Prev. 2002, 11: 291-297.
Seki N, Takasu T, Mandai K, Nakata M, Saeki H, Heike Y, Takata I, Segawa Y, Hanafusa T, Eguchi K: Expression of eukaryotic initiation factor 4E in atypical adenomatous hyperplasia and adenocarcinoma of the human peripheral lung. Clin Cancer Res. 2002, 8: 3046-3053.
Zabarovsky ER, Lerman MI, Minna JD: Tumor suppressor genes on chromosome 3p involved in the pathogenesis of lung and other cancers. Oncogene. 2002, 21: 6915-6935.
Fujii T, Dracheva T, Player A, Chacko S, Clifford R, Strausberg RL, Buetow K, Azumi N, Travis WD, Jen J: A preliminary transcriptome map of non-small cell lung cancer. Cancer Res. 2002, 62: 3340-3346.
Tan D, Li Q, Deeb G, Ramnath N, Slocum HK, Brooks J, Cheney R, Wiseman S, Anderson T, Loewen G: Thyroid transcription factor-1 expression prevalence and its clinical implications in non-small cell lung cancer: A high-throughput tissue microarray and immunohistochemistry study. Hum Pathol. 2003, 34: 597-604.
Tan D, Kirley S, Li Q, Ramnath N, Slocum HK, Brooks JS, Wu CL, Zukerberg LR: Loss of cables protein expression in human non-small cell lung cancer: a tissue microarray study. Hum Pathol. 2003, 34: 143-149.
Santos Romeo M, Sokolova IA, Morrison LE, Zeng C, Baron AE, Hirsch FR, Miller YE, Franklin WA, Varella-Garcia M: Chromosomal abnormalities in non-small cell lung carcinomas and in bronchial epithelia of high-risk smokers detected by multi-target interphase fluorescence in situ hybridization. J Mol Diagn. 2003, 5: 103-112.
Yokota J, Nishioka M, Tani M, Kohno T: Genetic alterations responsible for metastatic phenotypes of lung cancer cells. Clin Exp Metastasis. 2003, 20: 189-193.
Sozzi G: Molecular biology of lung cancer. Eur J Cancer. 2001, 37 Suppl 7: S63-73.
Niklinski J, Niklinska W, Chyczewski L, Becker HD, Pluygers E: Molecular genetic abnormalities in premalignant lung lesions: biological and clinical implications. Eur J Cancer Prev. 2001, 10: 213-226.
Mitsuuchi Y, Testa JR: Cytogenetics and molecular genetics of lung cancer. Am J Med Genet. 2002, 115: 183-188.
Osada H, Takahashi T: Genetic alterations of multiple tumor suppressors and oncogenes in the carcinogenesis and progression of lung cancer. Oncogene. 2002, 21: 7421-7434.
Zochbauer-Muller S, Gazdar AF, Minna JD: Molecular pathogenesis of lung cancer. Annu Rev Physiol. 2002, 64: 681-708.
Wistuba , II, Lam S, Behrens C, Virmani AK, Fong KM, LeRiche J, Samet JM, Srivastava S, Minna JD, Gazdar AF: Molecular damage in the bronchial epithelium of current and former smokers. J Natl Cancer Inst. 1997, 89: 1366-1373.
McWilliams A, MacAulay C, Gazdar AF, Lam S: Innovative molecular and imaging approaches for the detection of lung cancer and its precursor lesions. Oncogene. 2002, 21: 6949-6959.
Lam S, MacAulay C, leRiche JC, Palcic B: Detection and localization of early lung cancer by fluorescence bronchoscopy. Cancer. 2000, 89: 2468-2473.
Fujita Y, Fujikane T, Fujiuchi S, Nishigaki Y, Yamazaki Y, Nagase A, Shimizu T, Ohsaki Y, Kikuchi K: The diagnostic and prognostic relevance of human telomerase reverse transcriptase mRNA expression detected in situ in patients with nonsmall cell lung carcinoma. Cancer. 2003, 98: 1008-1013.
Houlston RS: What we could do now: molecular pathology of colorectal cancer. Mol Pathol. 2001, 54: 206-214.
Fearnhead NS, Wilding JL, Bodmer WF: Genetics of colorectal cancer: hereditary aspects and overview of colorectal tumorigenesis. Br Med Bull. 2002, 64: 27-43.
Kinzler KW, Vogelstein B: Lessons from hereditary colorectal cancer. Cell. 1996, 87: 159-170.
Komarova NL, Lengauer C, Vogelstein B, Nowak MA: Dynamics of genetic instability in sporadic and familial colorectal cancer. Cancer Biology & Therapy. 2002, 1: 685-692.
Rajagopalan H, Nowak MA, Vogelstein B, Lengauer C: Opinion: The significance of unstable chromosomes in colorectal cancer. Nat Rev Cancer. 2003, 3: 695-701.
Dunican DS, McWilliam P, Tighe O, Parle-McDermott A, Croke DT: Gene expression differences between the microsatellite instability (MIN) and chromosomal instability (CIN) phenotypes in colorectal cancer revealed by high-density cDNA array hybridization. Oncogene. 2002, 21: 3253-3257.
Kitahara O, Furukawa Y, Tanaka T, Kihara C, Ono K, Yanagawa R, Nita ME, Takagi T, Nakamura Y, Tsunoda T: Alterations of gene expression during colorectal carcinogenesis revealed by cDNA microarrays after laser-capture microdissection of tumor tissues and normal epithelia. Cancer Res. 2001, 61: 3544-3549.
Notterman DA, Alon U, Sierk AJ, Levine AJ: Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Res. 2001, 61: 3124-3130.
Hegde P, Qi R, Gaspard R, Abernathy K, Dharap S, Earle-Hughes J, Gay C, Nwokekeh NU, Chen T, Saeed AI, Sharov V, Lee NH, Yeatman TJ, Quackenbush J: Identification of tumor markers in models of human colorectal cancer using a 19, 200-element complementary DNA microarray. Cancer Res. 2001, 61: 7792-7797.
Lin YM, Furukawa Y, Tsunoda T, Yue CT, Yang KC, Nakamura Y: Molecular diagnosis of colorectal tumors by expression profiles of 50 genes expressed differentially in adenomas and carcinomas. Oncogene. 2002, 21: 4120-4128.
Zou TT, Selaru FM, Xu Y, Shustova V, Yin J, Mori Y, Shibata D, Sato F, Wang S, Olaru A, Deacu E, Liu TC, Abraham JM, Meltzer SJ: Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon. Oncogene. 2002, 21: 4855-4862.
Birkenkamp-Demtroder K, Christensen LL, Olesen SH, Frederiksen CM, Laiho P, Aaltonen LA, Laurberg S, Sorensen FB, Hagemann R, TF O. Rntoft: Gene expression in colorectal cancer. Cancer Res. 2002, 62: 4352-4363.
Stremmel C, Wein A, Hohenberger W, Reingruber B: DNA microarrays: a new diagnostic tool and its implications in colorectal cancer. Int J Colorectal Dis. 2002, 17: 131-136.
Yasui W, Oue N, Ono S, Mitani Y, Ito R, Nakayama H: Histone acetylation and gastrointestinal carcinogenesis. Ann N Y Acad Sci. 2003, 983: 220-231.
Etzioni R, Urban N, Ramsey S, McIntosh M, Schwartz S, Reid B, Radich J, Anderson G, Hartwell L: The case for early detection. Nat Rev Cancer. 2003, 3: 243-252.
We wish to acknowledge J. R. Vielkind, J. Fee, J. Squire, S. Lam, and K. Lonergan for providing pictures. We would also like to thank L. J. Henderson, J. J. Davies, B. P. Coe, and other members of the Lam Lab for useful discussion and critical evaluation of this manuscript. This work was supported by funds from the Canadian Institute of Health Research and National Cancer Institute of Canada. C. G. is supported by a scholarship from the Natural Sciences and Engineering Research Council of Canada.
C. G., the corresponding author, organized manuscript production. C. G. and T. P. H. B. contributed equally in literature review and the generation of early drafts. All authors participated in the development of concepts and framework for the review, the generation of figures, multiple rounds of text editing, and fact checking.
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Garnis, C., Buys, T.P. & Lam, W.L. Genetic alteration and gene expression modulation during cancer progression. Mol Cancer 3, 9 (2004). https://doi.org/10.1186/1476-4598-3-9
- cancer progression
- expression profiles
- high throughput technology