Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Genomic imbalances in 5918 malignant epithelial tumors: an explorative meta-analysis of chromosomal CGH data

BMC Cancer20077:226

DOI: 10.1186/1471-2407-7-226

Received: 26 April 2007

Accepted: 18 December 2007

Published: 18 December 2007

Abstract

Background

Chromosomal abnormalities have been associated with most human malignancies, with gains and losses on some genomic regions associated with particular entities.

Methods

Of the 15429 cases collected for the Progenetix molecular-cytogenetic database, 5918 malignant epithelial neoplasias analyzed by chromosomal Comparative Genomic Hybridization (CGH) were selected for further evaluation. For the 22 clinico-pathological entities with more than 50 cases, summary profiles for genomic imbalances were generated from case specific data and analyzed.

Results

With large variation in overall genomic instability, recurring genomic gains and losses were prominent. Most entities showed frequent gains involving 8q2, while gains on 20q, 1q, 3q, 5p, 7q and 17q were frequent in different entities. Loss "hot spots" included 3p, 4q, 13q, 17p and 18q among others. Related average imbalance patterns were found for clinically distinct entities, e.g. hepatocellular carcinomas (ca.) and ductal breast ca., as well as for histologically related entities (squamous cell ca. of different sites).

Conclusion

Although considerable case-by-case variation of genomic profiles can be found by CGH in epithelial malignancies, a limited set of variously combined chromosomal imbalances may be typical for carcinogenesis. Focus on the respective regions should aid in target gene detection and pathway deduction.

Background

The analysis of genomic abnormalities in malignant cell clones has been performed for decades. Introduced in the 1960s, the evaluation of stained metaphase preparations from tumor cells [1, 2] has been widely employed in basic research as well as in clinical practice. For analysis of complex karyotypes, direct chromosomal inspection was recently enhanced through multicolour in-situ hybridization techniques: combinatorial multi-fluor FISH (M-FISH [3]) and multicolour spectral karyotyping (SKY [4]).

In contrast to traditional or enhanced cytogenetic analysis, molecular-cytogenetic methods rely on tumor DNA as starting material for the exploration of genomic abnormalities. While fluorescent in-situ hybridization (FISH [5, 6]) allows for the detection of single or few numerical and structural genomic features in non-dividing cells, Comparative Genomic Hybridization (CGH [7, 8]) permits the screening of the whole tumor genome for regional imbalances in the DNA content. Since CGH is based on the hybridization of tumor and reference DNA to standardized normal metaphase spreads, the spatial resolution of this technique is limited to several megabases [9]. Also, the involvement of presumptive target genes can only be inferred from the positional comparison of the genomic ratio profiles to the underlying chromosomal matrix. However, recent molecular-cytogenetic screening techniques (array-or matrix-CGH [1012]) have the potential for direct identification of oncogenetic target genes.

Based on the comparably easy access to dividing tumor cells, and the early reognition of specific cytogenetic aberrations in some entities [1315], metaphase analysis has been especially successfull in acute leukemias (review e.g. in [16]) and other hematologic malignancies. This has been reflected in the content of the Mitelman Database of Chromosome Aberrations in Cancer [17], in which hematologic neoplasias account for 73% of the database content (approx. 40,000 cases), while epithelial tumors constitute only 12% (approx. 6,600 cases). In contrast, especially through it's application to the genomic screening of frozen and archival tissue, the potential of CGH for the analysis of solid tumors had been recognized early on [7, 18].

Some previous reviews of CGH data have either reported on specific types of aberrations [19, 20] or were focused on solid tumors [21] or hematologic malignancies [22]. Struski et al. [23] provided a census of 11,984 solid tumors and hematologic malignancies analyzed by CGH. Since in the majority of publications data is presented as summary information (e.g. percentage of frequently involved chromosomal regions), these data reviews were able to develop a granular overview of the major regions frequently imbalanced in different tumor entities. Recently, Myllykangas et al. [24] provided an overview of genomic gain/amplification patterns, thereby omitting genomic losses. The study covered a large panel of human neoplasias, based on more than 4500 single cases. The authors were able to show a general relation genomic amplification patterns to general histo-pathological features, but could not prove overall relation of hot-spot sites to some known genetic features.

The current study attempts a descriptive overview of genomic imbalances in epithelial neoplasias, based on published CGH data. Starting point for the data exploration is the Progenetix molecular-cytogenetic database [2527], which was initiated in December 2000 to collect all published cancer related CGH data and make it available for reference and research purposes. In contrast to other CGH data assembly attempts, but comparable to the Mitelman Database of Chromosome Aberrations in Cancer, complete experimental results are collected on a case-by-case basis. Publications describing (molecular-) cytogenetic analyses of tumor samples and established cell lines are mined for case specific annotation of the experimental results. Focus is the exhaustive collection of chromosomal CGH data, with occasional inclusion of results from Metaphase banding, SKY and M-FISH data, as well as the recent addition of array or matrix CGH [10, 28] results.

The detailed discussion of identified or suspected target genes in the different chromosomal regions is not part of this study. However, some examples of genes with oncogene and tumor suppressor function, respectively, will be mentioned.

Methods

As of 2006-12-04, 15429 cases from 609 publications had been included into the Progenetix database. Of those, 13818 had been analyzed by chromosomal CGH, either alone or in combination with Metaphase analyses techniques (355 cases). Minimum requirement for addition to the database was the availability of case specific data for the complete (molecular-) cytogenetic analysis result and clinical diagnosis. In most cases, locus information was available or could be inferred. Additional data (grading, staging/TNM, age, gender, follow-up parameters) was recorded if accessible. As main disease descriptors, diagnosis and locus information was recoded to ICD-O-3 standard [29], based on the available information.

For the purpose of this article, cases ascribed to epithelial origin (ICD-O 8010/x-8780/x) were selected for further analysis. This base dataset comprised 6899 cases, collected from 254 publications. The oldest included data was from 1994 [30], the latest from 2006 [31]. The largest number of cases was found in a report about skin neoplasias (169 cases; [32]), followed by an extensive comparison of BRCA1 and BRCA2 mutated breast carcinomas (132 cases; [33]). Only 8 articles were single case reports.

To limit the bias in disease entities frequently analyzed in pre-malignant stages, only the 5918 clearly malignant epithelial neoplasias were selected (ICD-O-3 xxxx/2 and xxxx/3). For comparative purposes, common clinico-pathological disease categories were generated through a combination of diagnostic and locus codes (Table 1). Only the 22 entities with more than 50 cases were analyzed separately, while 377 cases were not assigned to one of these entities.
Table 1

Distribution of 5918 malignant epithelial tumors by clinico-pathological entities, sorted by frequency of occurrence.

Diagnosis

ICD-O-3 codes(1)

Locus code(s)

No. of cases

Breast carcinoma

81xx ... 86xx

C50

667

Prostate carcinoma

814x

C61

600

Gastric carcinoma

81xx ... 86xx

C16

529

Ovarian carcinoma

81xx ... 86xx

C56

449

Colorectal adenocarcinoma (CRC)

8140, 8480

C17, C18

430

Hepatocellular adenocarcinoma (HCC)

817x

C22

371

Head-neck squamous cell carcinoma (HNSCC)

8070

C01, C09, C06, C10, C12, C13, C14, C30, C32

339

Thyroid carcinoma

81xx ... 87xx

C73

314

Non-small cell lung carcinoma (NSCLC)

81xx ... 86xx, excl. 8041, 8045, 824x

C34

254

Cervical carcinoma

81xx ... 86xx

C53

226

Esophagus carcinoma (ES)

81xx ... 86xx

C15

209

Renal carcinoma (RCC)

81xx ... 86xx

C64

195

Nasopharynx carcinoma (NPC)

8010, 8070

C11

177

Bladder carcinoma

81xx ... 86xx

C67

169

Neuroendocrine ca. and carcinoid (NE)

824x

(all)

138

Melanocytic (MEL)

872x ... 877x

(all)

99

Pancreas adenocarcinoma (PAC)

814x

C25

88

Cholangio carcinomas

816x

C221, C24

63

Small cell lung carcinoma (SCLC)

8041, 8045

C34

63

Endometrial carcinoma

81xx ... 86xx

C55

56

Vulva carcinoma

81xx ... 86xx

C51

53

Squamous malignancies of the skin (SQS)

807x

C44

52

377 tumors from entities with <50 cases were omitted from this list. (1) ICD codes and locus codes are listed as used for automatic assignment of cases to the disease groups; groups do not necessarily contain cases assigned to the whole range of ICD codes.

During database entry, CGH results were converted to standard "rev ish" ISCN 1995 format. This procedure contained software based syntax errors checking. The corrected "rev ish" data was processed by dedicated software [27] implemented in the Perl scripting language [34] using complex Regular Expression based parsing algorithms. Data matrices were generated, containing the imbalance status ("1" = gain, "-1" = loss, "2" = high level gain) for each of 862 chromosomal bands (UCSC Genome Bioinformatics [35], Golden Path mapping, May 2004 edition), or subsets thereof.

For statistical analysis, software packages available as part of the Bioconductor [36] project for the R programming language [37] were used. Visualization of genomic imbalance profiles was performed by custom Perl routines using the GD.pm interface [38] to the GD graphics library [39]. As a robust measure for genomic instability, the number of chromosomes per case with one or more imbalanced segments was determined.

Results and Discussion

In the base data set comprised of 5918 malignant cases analyzed by CGH, breast carcinomas and precursor lesions constituted the largest subgroup, followed by neoplasias of the prostate gland and stomach (Table 1). The median aberration number per case (number of chromosomes with at least one abnormal segment) showed a wide range (Figure 1), from 0 (squamous skin neoplasias, thyroid carcinomas) to 12 (small cell lung carcinomas; SCLC). For some of the entities at the low end of this spectrum, a certain bias through frequent biopsies at early stages can be suspected (e.g. tumors of skin, thyroid, prostate), although pre-maligant tumors had been excluded from the analysis.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig1_HTML.jpg
Figure 1

Number of imbalanced chromosomes for different tumor loci as indicator for overall genomic instability, in 5918 malignant epithelial tumors. The box plots indicate the median and distribution of chromosomes in each tumor karyotype, with total or partial genomic imbalances. Only malignant cases (ICD-code ####/2 or ####/3) were analyzed.

Imbalance hot spots in clinico-pathological entities

In the overall aberration profile, gains on chromosomal band 8q24 represented the most common imbalance, followed by gains involving regions on 20q, 1q, 3q, 17q, 7(q) and 5p, as well as other changes (Figure 2).

In the following overview, the most frequent regions involved in genomic imbalances are listed, with detailed locus and frequency information for regional hot-spots. Since overall aberration frequencies vary greatly between entities, no absolute cut-off values (e.g. 10% or 20%) were used when selecting these regions. Instead, genomic regions with clear separation from overall background in the imbalance histograms were selected for each entity and sorted in descending frequency of occurrence. Gains and losses were evaluated separately.

For each entity, few outstanding observations are discussed and selected literature is provided. Histologic subtypes are only mentioned in few entities. Because not all involved regions can be discussed here with respect to putative genetic targets, a selection of well characterized oncogenes and tumor suppressor genes with involvement in epithelial neoplasias is provided as part of Table 2. Detailed aberration profiles for each entity can be found in the Additional file 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig2_HTML.jpg
Figure 2

Overall imbalance pattern from all cases. For each chromosomal band (862 bands resolution) the percentage of cases with gains (green, upward) and losses (red, downward) is indicated.

Table 2

Occurrence of most common imbalances in different epithelial neoplasias by CGH

Regional imbalance

Most frequent*

Above background

Putative targets**

enh(1q)

occuring in most carcinoma types

Breast, ovary, HCC, cervix, NPC, MEL, endometrial, vulva

Gastric, CRC, HCC, HNSCC, NSCLC, ES, RCC, bladder, PAC, SCLC, SQS

ABL2, ETV3

dim(3p)

HNSCC, NSCLC, cervix, ES, RCC, NE, MEL (whole chr. 3), SCLC, vulva, SQS

Gastric, PAC

FHIT, MLH1

enh(3q)

maxima at 3q26q27 (except 3q25 in NPC)

Ovary, HNSCC, NSCLC, cervix, ES, NPC, SCLC, endometrial, vulva, SQS

Gastric, RCC, PAC

BCL6, PIK3CA

dim(4q)

frequently whole chromosome

Ovary, HCC, NSCLC, cervix, ES, bladder, cholangio, SCLC

Gastric, CRC, HNSCC, RCC, PAC,

PRDM5

enh(5p)

Thyroid, NSCLC, cervix,

Gastric, ovary, CRC, HCC, HNSCC, ES, bladder, NE, PAC, cholangio, vulva

CDH6, TERT

dim(5q)

Ovary, NSCLC,

Prostate, gastric, HNSCC, ES, bladder, cholangio,

APC, MCC

enh(6p)

HCC, MEL

Ovary, NSCLC, cervix, ES, bladder, cholangio, SCLC, vulva

E2F3, ID4

dim(6q)

maxima at 6q16q21 or 6q24q27

Prostate, RCC, MEL

Ovary, HCC, NSCLC, cervix, bladder, NE, PAC, cholangio, SCLC

CCNC

enh(7)

frequently whole 7, mostly max. on 7q

Prostate, thyroid, ES, RCC

Gastric, ovary, CRC, HCC, HNSCC, bladder, MEL, PAC (7p>7q), cholangio (7p>7q)

7p: EGFR 7q: ABCB1, MET

dim(8p)

Breast, prostate, CRC, HCC

Ovary, HNSCC, NSCLC, ES, RCC, bladder, PAC, SCLC, vulva

DLC1, MSR1, N33

enh(8q)

ubiquitously high (exception NE and thyroid)

Breast, prostate, ovary, CRC, HCC, HNSCC, NSCLC, ES, RCC, bladder, MEL, PAC, cholangio, endometrial, vulva

Cervix, NPC, SCLC, SQS

MYC

dim(9p)

9p or whole 9

HNSCC, bladder, PAC, SQS

Gastric, NSCLC, ES, RCC, NPC, MEL, SCLC

ARF, CDKN2A

enh(11q13)

frequently distinct (high-level) gain

HNSCC

Breast, gastric, ovary, NSCLC, ES, NPC, bladder, MEL, PAC, cholangio

CCND1, FGF3

dim(11q23qter)

NPC, NE, vulva

Breast, HNSCC, cervix, ES, MEL, SCLC

ATM (11q22), (LOH11CR2 A, TSG11)

enh(12p)

frequently whole 12; slight max. on 12p

NPC

Ovary, CRC, HNSCC, NSCLC, ES, RCC, PAC, vulva

12p: CDK2, CDK4, GLI, KRAS 12q: MDM2

dim(13q)

mostly 13q14q21

Prostate, HCC, thyroid, bladder, NE, PAC, SCLC, endometrial

Breast, gastric, ovary, HNSCC, NSCLC, cervix, ES, RCC, NPC (max. at 13q31), MEL, CRC, vulva

BRCA2, RB1, STARD13

dim(16q)

Breast, NPC

Prostate, gastric, HCC, SCLC

CDH1, ATBF1

dim(17p)

Breast, gastric, CRC, cholangio, SCLC

Ovary, HCC, NSCLC, cervix, ES, RCC, NPC, bladder, PAC, SQS

TP53

enh(17q)

Breast, gastric, bladder, NE, PAC, cholangio, SCLC, SQS

HCC, HNSCC, NSCLC, cervix, ES, renal, NPC

ERBB2

dim(18q)

Gastric, ovary, CRC, HNSCC, PAC, SQS

HCC, NSCLC, cervix, renal, bladder, cholangio

DCC, SMAD4

enh(19q)

NE, SCLC

Breast, CRC, PAC, vulva

AKT2, BAX

enh(20q)

Gastric, CRC, thyroid, bladder, NE, PAC, cholangio

Breast, HCC, cervix, ES, renal, MEL, SCLC, vulva

STK15/AuroraA

Please refer to table 1 for abbreviations. * "most frequent" lists entities in which the aberration belongs to the 3 most frequent imbalances of the specified quality (enh = gain, dim = loss; bold if most frequent change in entity). Entities are sorted according to the total number of included cases. Chromosomal regions 1p, 21, 22, X and Y were omitted due to differences in reporting (e.g. exclusion of regions prone to errors in chromosomal CGH analysis). ** Listed are some examples of genes with oncogene (for gain regions) or tumor suppressor (for loss regions) function. However, a large number of possible target genes as well as structural features may exist for each region.

Breast carcinoma (667 cases)

Gains: 1q31 (50.8%), 8q23 (47.3%), 17q24 (31,2%), 20q (30.9%), 16p, 11q13, 19, 3q

Losses: 16q (30%), 8p23 (26.1%), 17p13 (23.5%), 11q23 (23.1%), 13q21 (22.8%)

In breast carcinomas, gains on 1q and losses on 16q constitute the most frequent copy number changes of each quality. In the literature, a cytogenetic subtype with combination of these imbalances and few other genomic changes has been associated with histology of well differentiated DCIS [40] and a favorable prognosis [41]. Interestingly, gains on 11q13 and 12q24 were associated with higher metastasis-free survival in another study [42], which also associated multiple imbalances with a bad prognosis.

Although ERBB2 has been a known and clinically relevant [43] amplification target on 17q, distinct amplicons mapping telomeric to the ERBB2 locus have been described [44].

Prostate carcinoma (600 cases)

Gains: 8q24 (23%), 7, X

Losses: 8p21 (32.3%), 13q21 (26.5%), 6q16 (18.5%), 16q(23), 5q(21)

In contrast to other adenocarcinomas, and accounting for the overall low copy number variations, prostate ca. usually lack the 1q gains frequent in most carcinoma entities. While Mattfeldt et al. [45] showed a correlation of 8p loss to higher tumor stage, the negative prognostic impact of gains of 8q and chromosome 7 in was reported from bioptic samples [46] and early tumors [47]. A meta analysis of published copy number data recently was provided by Sun et al. [48].

Gastric carcinoma (529 cases)

Gains: 20q12 (36.1%), 8q23 (31.7%), 17q21 (21.2%), 7 (up to 20.8%), 13q22q31, 1q, 3q, 5p, 11q13, X

Losses: 17p13 (24.4%), 19p (18%), 18q (16.6%), 1p, 3p, 4, 5q, 9p, 12q, 16

For most chromosomes, an overall background of 4–10% of gains as well as losses can be found. In gastric carcinomas as well as in some other entities, 8q gains have shown their maximum at 8q23, proximal to the c-myc locus [49], implying a hitherto unidentified target.

Ovarian carcinoma (449 cases)

Gains: 8q24 (43.7%), 3q26 (38.1%), 1q32 (25.3%), 12p12 (19.2%), 2, 5p, 6p, 7q, 11(q13)

Losses: 5q14 (25.2%), 4q (26.3%), 18q21 (19.8%), 17p (19.6%), 8p (19.4%), 6q, 9q, 13q, 16q, X

Gains on 12p have been discussed as early event in ovarian carcinomas with frequent occurrence in borderline tumors [50], and several other changes have been linked to advanced carcinomas [51]. Overall, advanced stage tumors showed a higher grade of chromosomal instability.

Colorectal adenocarcinoma (430 cases)

Gains: 20q13 (53%), 13q (38.6%), 8q24 (37.2%), 7(p) (35.6%), X(q21), 1q, 5p, 12(p), 19

Losses: 18q(22) (47.4%), 8p(22) (37.9%), 17p12 (27%), 4 (up to 23.7%), 14, 15, 22

In colorectal carcinoma, gains on 8q23q24 [52] have been associated with lymph-node positivity. In a recent study, gains on 20q as well as KRAS mutations could bee shown to precede aneuploidy [53]. An overview is provided in [54].

Hepatocellular adenocarcinoma (HCC; 371 cases)

Gains: 1q23q31 (46.6%), 8q24 (44.8%), 6p21 (22.4%), 17q (21.8%), 5, 7, 20

Losses: 4q (up to 31.3%), 8p(21) (31.3%), 13q21 (28.3%), 16q(21) (25.9%), 17p13 (25.3%), 1p, 6q, 14, 18

Losses of 8p have been shown to distinguish HCC from other liver malignancies [55]. Also, losses on 4q and 13q were associated with poor differentiation [56]. An overview of genomic changes in HCC with discussion of putative target genes is provided in [57].

Head-neck squamous cell carcinoma, excluding nasopharyngeal ca. (HNSCC; 339 cases)

Gains: 3q26 (59.2%), 8q24 (40.8%), 11q13 (31.9%, many specific high-level), 5p (26.5%), Xq, 1q, 7q(21), 12p, 17

Losses: 3p (30.1%), 18q(22) (22.4%), 9p (22.4%), 11q24 (19.2%), 4, 5q, 8p, 13

Genomic imbalances in HNSCC are in line with other squamous cell carcinomas (-3p, +3q, +5p). The high rate of amplifications at 11p13 has been shown to involve the cyclin D1 (CCND1) locus and to be accompanied by high expression of the gene [58].

Thyroid carcinoma (314 cases)

Gains: 5(p), 7, 20(q)

Losses: 13q, 22, 1p

Although a number of highly aberrant cases was included (see outliers in box plot, Figure 1), thyroid carcinomas, including all variants, showed an overall the lowest level of chromosomal imbalances. The subset included a large study in post-Chernobyl tumors in children, for which imbalances could be found only in 30% of tumors [59]. Interestingly, anaplastic carcinomas had a low median number but high variability. Although not mapping to regions with frequent genomic gains in the the included cases, previous reports have shown e.g. amplification of PRKCE on 2p21 [60] and FGF3 [61].

Non-small cell lung carcinomas (NSCLC; 314 cases)

Gains: 5p (52.8%), 3q26 (39.4%), 8q24 (35.4%), 1q(21) (28.4%), 3, 6p, 11q13, 12p, 17q, 18p

Losses: 3p (31.9%), 4(q) (26.4%), 5q (26%), 13q21 (25.6%), 8p(21) (24%), 9 (up to 20.5%), 17p (16.5%), 1p, 6q, 10, 18q

NSCLC is a histologically heterogeneous group, consisting of squamous (SCC) and non-squamous (NSCC) cases. Imbalances have been shown to be partially shared between the groups, with differences in the frequency of some regional involvements [62].

Cervix carcinoma (226 cases)

Gains: 3q26qter (53.6%), 1q (up to 28.3%), 5p (27.4%), 8q24 (20.3%), 6p, 9(q), 17q, 20q, X

Losses: 2q36 (30.1%), 3p (up to 25.2%), 4 (up to 23.5%), 11q23q24 (23%), 6q, 13q, 17p, 18

The number of chromosomal changes may be influenced by occurrence and type of HPV infection. However, in a large study no correlation between single imbalances and clinical parameters could be identified [63]. Losses of the telomeric region of 2q were frequent and rarely found in other entities.

Esophagus carcinoma (209 cases)

Gains: 3q(26) (53.2%), 8q24 (49.8%), 7p (34%), 20q13 (32.6), 1q (29.7%), 7q21 (29.7%), 5p (28.7%), Xq (23.4%), 6p12 (22.9%), 12p (22.5%), 11q13 (22.1%), 17q21 (21.6%), 2(q), 9q

Losses: 3p (up to 37.3%), 18q (37.3%), 4 (up to 33%), 5q(21) (29.2%), 9p (26.3%), 8p (23.4%), 13q (up to 23%), 1p (22.5%), 19q (2%), 17p (20.1%), 10, 11, Xp

Esophagus carcinomas showed an overall high "background" of gains and losses, with the 2nd highest number of imbalances per case. This group consists of cases with squamous cell as well as adenocarcinoma or intestinal adeno-ca. histology. Besides specific de-regulation of oncogenes, an effect of the massive genomic changes on chromatin structure has been discussed previously [64].

Renal carcinoma (RCC, 195 cases)

Gains: 7 (up to 33.4%), 5q(31) (32.8%), 8q23q24 (19.5%), 20 (up to 18.5%), 17q (up to 17.4%), 1qter (13.1%), 3q, 12, 16

Losses: 3p (up to 43.1%), 14q (up to 26.2%), 6q (21%), 1p (up to 20%), 9 (20%), 8p (19.5%), 13q (19.5%), 17p (19%), 18q (18%), 2, 4, 10

Gains of 5q are a rare occurrence in other carcinomas, and have been identified in papillary as well as non-papillary cases [65]. An overview of accumulated karyotype data was given in 2004 [66], proposing different cytogenetic pathways and associating papillary RCC with a hyperdiploid karyotype pattern.

Nasopharynx carcinoma (NPC; 177 cases)

Gains: 12p (up to 33.9%), 1q (24.3%), 3q (22%), 8q22q23 (19.5%), 18p (17%), 11q13 (12.4%), 2(q), 4(q), 6(q), 17q

Losses: 16q (29.9%), 14q24 (27.7%), 11q23 (24.3%), 1pter (23.7%; difficult region), 9, 13q31, 17p, 19p

In contrast to some other carcinoma entities, the maximum of gains mapped clearly proximal to the c-myc region. A meta-analysis of CGH data in NPC was recently provided by Li et al. [67]. In concordance with the low number of NPC cases with 8p deletion, the oncogenetically relevant DLC1 on 8p22 has been reported to be inactivated by methylation rather than copy number change [68].

Bladder carcinoma (169 cases)

Gains: 20q (21.3%), 8q (20.7%), 17q (20.7%), 11q13 (19.5%), 1q21 (17.8%), 5p, 6p, 7, 10p

Losses: 9 (26%), 13q21 (17.8%), 4q (up to 14.2%), 11p (14.2%), 5q (14.6%), 2q32, 8p, 6q, 18q, X, 17p

While some of the imbalances (e.g. 8q, 17q and 20q gain, 11q13 gain/amplification) were frequent in other entities, losses on chromosome 9 were exceptionally high in transitional cell carcinoma (115 cases). In contrast to other squamous cell carcinomas, gains on 3q were rare (3 of 40 bladder SCC). In one study included in the data, some differences between histological and etiological subtypes of bladder carcinomas were presented [69].

Neuroendocrine carcinoma and carcinoid (138 cases)

Gains: 19 (26.1%), 20 (up to 18.1%), 17q (up to 17.4%), 5p (15.9%)

Losses: 11q22q25 (20.3%), 13q21 (18.1%), 3p (up to 15.5%), 6q22 (13%), 10q25q26 (13%)

Possibly due to the heterogeneity of this group, a diffuse background of whole chromosome gains was observed. The genetics of neuroendocrine tumors was recently reviewed with consideration for inherited syndromes as well as molecular cytogenetic results [70].

Malignant melanocytic neoplasias (99 cases)

Gains: 8q(21) (36.3%), 6p (32.3%), 1q (22%), 7, 20q, 11q13

Losses: 6q(24) (29.3%), 10 (q22) (22.2%), 3 (up to 21.2%), 9p (21.2%), 13q, 1p, 11q23q24

In contrast to most epithelial malignancies, gains on 6p can be found in a large proportion of malignant melanomas. Above background losses on 10q are found in few other entities, too. Similar to other entities gains on 8q, 1q, 20q and chromosome 7 can be observed. A comprehensive overview about molecular-cytogenetic techniques in the analysis of melanocytic lesions was given by Bauer and Bastian [71].

Pancreas adenocarinoma (88 cases)

Gains: 20q (up to 30.7%), 8q(23) (26.2%), 17q (up to 21.6%), 12p11 (19.3%), 7p (18.2%), 3q (up to 17.1%), 5p, 11(q13), 14, 15qter, 16, 19q

Losses: 18q (35.2%), 9p(23) (29.6%), 13q (18.2%), 3p (up to 17.1%), 6q(21) (17.1%), 8p (17.1%), 17p13 (15.9%), 4, 15q, 10q, 12q

This subset only includes pancreas adenoocarcinomas, omitting endocrine tumors.

High level copy number amplifications have been shown e.g. for ERBB2 (17q12) and EGFR (7p12). Interestingly, the maximum for gains on 8q (8q22) is proximal to the c-myc region. This fact was also mentioned in a recent overview of genomic screening results in pancreas ca. [72].

Cholangio-carcinomas (intra- and extrahepatic; 63 cases)

Gains: 8q22 (42.9%), 17q (up to 39.7%), 20q (34.9%), 11q13 (23.8%), 15q (up to 23.8%), 3q(26) (20.7%), 7p (up to 20.7%), 13q (up to 20.7%), 5p (up to 19.1%), 1q, 6p, 12q23q24

Losses: 1p34p36 (up to 22.2%), 4q (up to 20.6%), 17p (19.1%), 5q(14), 6q, 13q, 18qter, X

Gains on 15q were reported in up to 36% (in a study of Korean intrahepatic cholangio-ca. [73]), but rarely seen in other entities.

Small-cell lung carcinoma (SCLC; 63 cases)

Gains: 19 (up to 44.4%), 3q (up to 41.3%), 17q23 (41.3%), 1p31p34 (38.2%), 8q(23) (36.5%), 20q (up to 30.2%), 14q31q32 (26%), 1q (up to 25.4%), 9q31q34, 6p, 13q32

Losses: 3p (84.1%), 4 (up to 74.1% on 4qter), 13q(14) (69.8%), 10(q) (41.3%), 17p13 (33.3%), 2(q23q24) (31.8%), 8p (25.4%), 9, 11, 15, 6q, 16q

SCLC had the highest number of chromosomal imbalances (median 12 involved chromosomes per case). Also, some of the imbalances constituted the overall highest fractional aberrations in all datasets (e.g. >80% losses on 3p), as well as very frequent losses on 4q, 13q and 10q. Although frequent gains on 7p had been reported from array CGH of SCLC cell lines [74], this region was rarely involved in the mostly clinical specimen analyzed by chromosomal CGH.

Endometrial carcinoma (56 cases)

Gains: 1q (50%), 8q(22) (39.3%), 3q26 (14.3%), 10 (up to 14.3%)

Losses: 10q (12.5%), 13q (10.7%), 14q, 9q

As in e.g. nasopharynx ca., SCLC and pancreas ca., the maximum of 8q gains was proximal of 8q24. Endometrial ca. show an exceptional high rate of gains on 1q, which were also the most frequent change in a series of 98 cases not available for inclusion here [75]. Interestingly, chromosome 10 was also frequently involved (both gains and losses).

Carcinomas of the vulva (53 cases)

Gains: 3q (45.7%), 8q(23) (34%), 1 (up to 28.3%), 5p(15) (26.4%), 20q (24.5%), 9q (20.8%), 6, 7, 12, 14, 19, X

Losses: 3p (22.6%), 4p (22.6%), 11q23q25 (20.8%), 13q14 (13.2%), 8p, 10, X

Vulvar carcinomas showed an imbalance pattern characteristic for squamous cell neoplasias, with a high rate of gains on 8q23 and especially 3q. As in cervix ca., the most frequent changes could not be related to HPV status [76].

Squamous cell carcinomas of the skin (52 cases)

Gains: 3q (11.5%), 17q (11.5%), Xq (9.6%), 1q, 8q, 14q

Lossses: 9p21 (19.2%), 3p (13.5%), 18q (13.5%), 17p (9.6%)

While containing many cases of borderline malignant behaviour, squamous skin neoplasias had the overall lowest aberration frequencies (median 0). This group did not include the pre-malignant keratoacanthomas, for which a frequent gain of 11q and cyclin D1 overexpression had been shown [77].

Disease-specific Involvement of most frequently aberrant chromosomes and relationship of overall aberration profiles

Several chromosomal hot spot regions showed a pronounced disease related variation. The most prominent regions scoring high in specific entities are listed in Table 2. To reduce sampling bias (e.g. entity specific inclusion of a high proportion of early stage cases) top-scoring aberrations were defined as the most frequent gains or losses in each entity. Some additional imbalances which occured only in few entities at high levels are listed in Table 3. In an attempt to identify the relation of single case aberration profiles, a cluster analysis of all 5043 informative cases was performed. For that purpose, a band specific matrix with 86 bands resolution was generated, of which the 55 intervals most frequently involved in imbalances were selected (top-scorers in one or more entities, ref. Tables 2 and 3; e.g. "8q2"). Hierarchical cluster analysis revealed the complexity of the case-specific aberration patterns, and was able to visualize concordance of imbalance patterns and disease categories in small groups of cases (Figure 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig3_HTML.jpg
Figure 3

Clustering of 5043 malignant epithelial neoplasias by the pattern of gains and losses, using regions previously defined as highly aberrant in one or several entities (ref. tables 2 + 3). For each case (x-axis), gains (green) and losses (red) are indicated for the corresponding chromosomal regions (55 selected bands; y-axis). The color bar codes on top indicate the cases' assignments to the different clinico-pathological entities and histological groups (color code is provided in the additional file 1).

Table 3

Selected imbalances with high penetrance limited to one or few entities

Imbalance

Occurrence

Comment

dim(2q22q31)

SCLC (up to 31.8%)

Profile appears region-specific; however, SCLC cases have very rich imbalance pattern with more frequent changes

dim(2q33q37)

Cervix (up to 30.1%)

Specific terminal 2q deletions are else only found in low frequency in NSCLC and bladder lesions

enh(5q21q35)

RCC (up to 32.8%)

Specific regional 5q gains are near exclusive for renal carcinomas, and are here part of whole chromosomal changes (20%) or limited to the region

dim(10q)

SCLC (up to 41.3%), melanocytic, endometrial

Rare as specific changes above "background"

enh(13q)

CRC (up to 38.6%), gastric

13q gains are rare except in CRC, esp. compared to the frequent losses in the region

To reduce the complexity of the data and simplify the detection of similarities in the aberration patterns of different entities, region specific aberration frequencies of all entities were clustered. For each of the 55 selected intervals, the sum of gains – losses was calculated, resulting in a 22 × 55 matrix (entities × intervals). During hierarchical cluster analysis, values were normalized over each entity's intervals, to account for differences in genomic complexity. Figure 4 depicts the result of the cluster analysis, in which overall similarities and differences in the different entities become apparent.

Differences in aberration profiles in some instances will reflect the general histological type, which is substantiated by the close relation of overall profiles e.g. from clinico-pathological groups containing squamous cell carcinoma cases (group NSCLC/esophagus/HNSCC/cervical/vulva in Figure 4). To evaluate this effect, the most frequent histopathological entities (adenoca., squamous cell ca., hepatocellular ca., ductal breast ca., transitional cell ca., intestinal adenoca.) were selected. Additionally, adenocarcinomas of the prostate were put in a separate group, due to the previous observation of specificities in the imbalance profile (e.g. overall lack of 1q gains). Clustering was performed analogous to the method described above. Figure 5 visualizes the clustering of those histopathological entities.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig4_HTML.jpg
Figure 4

Clustering of carcinoma entities by their overall imbalance pattern. For each of the selected chromosomal regions, aberrations were summarized (percent gain – percent loss). After normalization of all regions over the respective entity, the color intensities represent the relative contribution of regional gains and losses to the overall aberration patterns.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig5_HTML.jpg
Figure 5

Clustering of different histologies in carcinomas by their overall imbalance pattern. Here, the most frequent histological types were automatically grouped for their overall imbalance profiles. Since considerable differences had been found for adenocarcinoma cases from the prostate (most notably lack of 1q gains), this group was separeted from the overall adenocarcinoma group.

In comparing the most frequent imbalances for their occurrence in different entities, some general observations stood out. Gains involving the terminal part of 8q were ubiquitously found, and often belonged to the most frequent imbalances in the respective entities. Hovever, they were rarely detected in neuroendocrine carcinomas and thyroid neoplasias. Interestingly, in some entities the maximum of detected abnormalities mapped proximal of 8q24 (e.g. 8q21 in melanocytic NPL and 8q22 in NPC and cholangio ca.). How much this reflects a true difference in regional involvement, and therefore may point to differential target gene involvement has to be left open here.

Gains on 1q with maxima on 1q2 were also frequent, and were the most frequent changes in breast, hepatocellular and endometrial carcinomas. However, 1q gains were comparatively infrequent in carcinomas of the prostate and bile ducts as well as in renal and colorectal ca. Overall gains on 3q26q27 combined with losses on 3p were characteristic for squamous cell carcinomas and SCLC, with 3q gains also being frequent in ovarial ca. and other entities.

While 5p gains were found in various entities, gains on 5q were frequent only in ca. of thyroid and kidney, as were gains on chromosome 12. Overall, 13q losses could be detected in most entities. In contrast, 13q gains were predominant in cholangio ca. and also frequent in colorectal and gastric carcinomas.

Region specific gains involving 11q13 were found in a number of entities (bladder ca., HNSCC, pancreas, prostate, skin, ovary, gastric, NSCLC). Interestingly, of the overall 692 cases with gain on 11q13, 18% had a loss of the telomer of 11q by CGH (Figure 6). Since a strong selective pressure for such a switch in aberration quality in linked regions can be suspected, this observation may point towards targeting of both genes with oncogene and tumor suppressor functions on 11q in these tumors.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-7-226/MediaObjects/12885_2007_Article_876_Fig6_HTML.jpg
Figure 6

Visualization of single case aberration patterns in 123 carcinomas with gain on 11q13 and concomitant proximal loss. Cases are clustered according to their imbalance patterns (gain/loss status, 320 bands resolution).

When comparing overall aberration patterns, entities containing squamous cell carcinoma cases (HNSCC, NSCLC, cervix and vulva ca., esophagus ca.) appeared related, with a common pattern offrequent losses on 3p, gains on 3q, 8q and varying gains on 1q and 5p. NPC showed a slightly diverging pattern, with predominant 12p gains and 14q losses in addition to slightly lower levels of those imbalances.

Interestingly, in both unselected renal carcinomas as thyroid neoplasias, the average imbalance profiles included frequent gains on chromosomes 5, 7 and 12, while lacking the dominance of gains 1q and 8q found in most other entities.

Apart from these observations, one should refer to the accompanying figures for a general comparison of patterns in different clinico-pathological groups. Also, the use of the Progenetix website tools is encouraged.

Conclusion

This study attempted an overview of chromosomal imbalance profiles in epithelial neoplasias, based on a large collection of case-specific chromosomal imbalance data from published chromosomal CGH experiments. The most striking observations were the high frequency of a limited set of changes (+1q, +3q, +7, +8q, -13q, -17p, -18q, +20q ...), of which different subsets occurred in most entities. The iterative involvement of certain genomic regions in either gains or losses is a strong argument for the non-random incorporation of these changes into the tumor cell genomes, with common as well as disease specific changes becoming apparent. One may speculate that the recurring combination of a limited set of imbalance hot-spots is concordant with the multistep process of cancer development [78], and that these regions point towards preferential oncogenetic targets. For a detailed discussion of the region-specific changes and patterns related to single disease entities, only examples could be given in the context of this article. The existing literature should be considered for additional information.

This descriptive analysis of local genomic imbalance frequencies cannot be able to address some important questions, e.g. regarding the biological significance of the observed changes. Although certain genomic copy number amplifications and segmental deletions have been shown to involve oncogenetically relevant genes in various malignancies (see Table 2 and for reference e.g. [7982]), some of the recurring genomic imbalances might be as well pure epi-phenomena of defects in the molecular maintenance of the genome. In respect to the limited spatial resolution and phenomenological nature of the data, the discussion and validation of specific target genes should be left to meticulously crafted molecular-biological experiments.

Cluster analysis of case specific chromosomal CGH data has been used previously to identify subsets of cases in single clinico-pathological entities [45, 83, 84]. Current methodology appears more suited for limited data sets, while new algorithms have to be developed for automatic subgroup detection from large, heterogeneous series of CGH data [85, 86]. This is especially challenging regarding the variant background of chromosomal copy number changes found in certain tumor types, which may be based on aberrant expression of regulatory genes [41]. Since genomic copy number changes constitute only one of many mechanisms leading to aberrant gene regulation, the deduction of oncogenetic pathways from CGH data will remain a challenging project. However, the combination of existing large-scale data collections and current high resolution screening techniques should provide additional pieces for these puzzles. For that purpose, the Progenetix data collection is open for inclusion into data mining projects.

Declarations

Acknowledgements

I am indebted to the authors of a number of articles, who provided the data for the database that otherwise would not have been accessible. Progenetix database contributors are listed on the project's website [25]. During a part of the project, the author has been supported through a grant from the Claussen-Simon-Stiftung.

Authors’ Affiliations

(1)
Institute of Molecular Biology, University of Zurich

References

  1. Patau K: The identification of individual chromosomes, especially in man. Am J Hum Genet. 1960, 12: 250-276.PubMedPubMed CentralGoogle Scholar
  2. Crossen PE: Giemsa banding patterns of human chromosomes. Clin Genet. 1972, 3: 169-179.View ArticlePubMedGoogle Scholar
  3. Speicher MR, Gwyn Ballard S, Ward DC: Karyotyping human chromosomes by combinatorial multi-fluor FISH. Nat Genet. 1996, 12: 368-375. 10.1038/ng0496-368.View ArticlePubMedGoogle Scholar
  4. Veldman T, Vignon C, Schrock E, Rowley JD, Ried T: Hidden chromosome abnormalities in haematological malignancies detected by multicolour spectral karyotyping. Nat Genet. 1997, 15: 406-410. 10.1038/ng0497-406.View ArticlePubMedGoogle Scholar
  5. Cremer T, Lichter P, Borden J, Ward DC, Manuelidis L: Detection of chromosome aberrations in metaphase and interphase tumor cells by in situ hybridization using chromosome-specific library probes. Hum Genet. 1988, 80: 235-246. 10.1007/BF01790091.View ArticlePubMedGoogle Scholar
  6. Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC: Delineation of individual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet. 1988, 80: 224-234. 10.1007/BF01790090.View ArticlePubMedGoogle Scholar
  7. Kallioniemi A, Kallioniemi OP, Sudar D, Rutovitz D, Gray JW, Waldman F, Pinkel D: Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science. 1992, 5083: 818-821. 10.1126/science.1359641.View ArticleGoogle Scholar
  8. Joos S, Scherthan H, Speicher MR, Schlegel J, Cremer T, Lichter P: Detection of amplified DNA sequences by reverse chromosome painting using genomic tumor DNA as probe. Hum Genet. 1993, 90 (6): 584-589. 10.1007/BF00202475.View ArticlePubMedGoogle Scholar
  9. Bentz M, Plesch A, Stilgenbauer S, Dohner H, Lichter P: Minimal sizes of deletions detected by comparative genomic hybridization. Genes Chromosomes Cancer. 1998, 2: 172-175. 10.1002/(SICI)1098-2264(199802)21:2<172::AID-GCC14>3.0.CO;2-T.View ArticleGoogle Scholar
  10. Solinas-Toldo S, Lampel S, Stilgenbauer S, Nickolenko J, Benner A, Dohner H, Cremer T, Lichter P: Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer. 1997, 4: 399-407. 10.1002/(SICI)1098-2264(199712)20:4<399::AID-GCC12>3.0.CO;2-I.View ArticleGoogle Scholar
  11. 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, 2: 207-211. 10.1038/2524.View ArticleGoogle Scholar
  12. 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. Nat Genet. 1999, 1: 41-46.Google Scholar
  13. Nowell PC, Hungerford DA: A minute chromosome in chronic granulocytic leukemia. Science. 1960, 132: 1497-Google Scholar
  14. Rowley JD: Letter: A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining. Nature. 1973, 5405: 290-293. 10.1038/243290a0.View ArticleGoogle Scholar
  15. Manolov G, Manolova Y: Marker band in one chromosome 14 from Burkitt lymphomas. Nature. 1972, 237: 33-34. 10.1038/237033a0.View ArticlePubMedGoogle Scholar
  16. Mrozek K, Heerema NA, Bloomfield CD: Cytogenetics in acute leukemia. Blood Rev. 2004, 18: 115-136. 10.1016/S0268-960X(03)00040-7.View ArticlePubMedGoogle Scholar
  17. Mitelman Database of Chromosome Aberrations in Cancer. [http://cgap.nci.nih.gov/Chromosomes/Mitelman]
  18. Speicher MR, du Manoir S, Schrock E, Holtgreve-Grez H, Schoell B, Lengauer C, Cremer T, Ried T: Molecular cytogenetic analysis of formalin-fixed, paraffin-embedded solid tumors by comparative genomic hybridization after universal DNA-amplification. Hum Mol Genet. 1993, 11: 1907-1914. 10.1093/hmg/2.11.1907.View ArticleGoogle Scholar
  19. Knuutila S, Bjorkqvist AM, Autio K, Tarkkanen M, Wolf M, Monni O, Szymanska J, Larramendy ML, Tapper J, Pere H, El-Rifai W, Hemmer S, Wasenius VM, Vidgren V, Zhu Y: DNA copy number amplifications in human neoplasms: review of comparative genomic hybridization studies. Am J Pathol. 1998, 152: 1107-1123.PubMedPubMed CentralGoogle Scholar
  20. Knuutila S, Aalto Y, Autio K, Bjorkqvist AM, El-Rifai W, Hemmer S, Huhta T, Kettunen E, Kiuru-Kuhlefelt S, Larramendy ML, Lushnikova T, Monni O, Pere H, Tapper J, Tarkkanen M, Varis A, Wasenius VM, Wolf M, Zhu Y: DNA copy number losses in human neoplasms. Am J Pathol. 1999, 155: 683-694.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Gebhart E, Liehr T: Patterns of genomic imbalances in human solid tumors (Review). Int J Oncol. 2000, 16: 383-399.PubMedGoogle Scholar
  22. Gebhart E: Genomic imbalances in human leukemia and lymphoma detected by comparative genomic hybridization (Review). Int J Oncol. 2005, 27: 593-606.PubMedGoogle Scholar
  23. Struski S, Doco-Fenzy M, Cornillet-Lefebvre P: Compilation of published comparative genomic hybridization studies. Cancer Genet Cytogenet. 2002, 135: 63-90. 10.1016/S0165-4608(01)00624-0.View ArticlePubMedGoogle Scholar
  24. Myllykangas S, Himberg J, Bohling T, Nagy B, Hollmen J, Knuutila S: DNA copy number amplification profiling of human neoplasms. Oncogene. 2006, 25: 7324-7332. 10.1038/sj.onc.1209717.View ArticlePubMedGoogle Scholar
  25. Progenetix – cytogenetic abnormalities in human cancer. [http://www.progenetix.net]
  26. Baudis M, Cleary ML: Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics. 2001, 12: 1228-1229. 10.1093/bioinformatics/17.12.1228.View ArticleGoogle Scholar
  27. Baudis M: Online database and bioinformatics toolbox to support data mining in cancer cytogenetics. Biotechniques. 2006, 40 (3): 269-272.View ArticlePubMedGoogle Scholar
  28. Schwaenen C, Nessling M, Wessendorf S, Salvi T, Wrobel G, Radlwimmer B, Kestler HA, Haslinger C, Stilgenbauer S, Dohner H, Bentz M, Lichter P: Automated array-based genomic profiling in chronic lymphocytic leukemia: development of a clinical tool and discovery of recurrent genomic alterations. Proc Natl Acad Sci USA. 2004, 4: 1039-1044. 10.1073/pnas.0304717101.View ArticleGoogle Scholar
  29. International Classification of Diseases for Oncology (ICD-O), Edition. World Health Organization. Edited by: Fritz A, Percy C, Jack A, Sobin LH, Parkin MD. 2000, Geneva, Third
  30. Speicher MR, Schoell B, du Manoir S, Schrock E, Ried T, Cremer T, Storkel S, Kovacs A, Kovacs G: Specific loss of chromosomes 1, 2, 6, 10, 13, 17, and 21 in chromophobe renal cell carcinomas revealed by comparative genomic hybridization. Am J Pathol. 1994, 145: 356-364.PubMedPubMed CentralGoogle Scholar
  31. Wild P, Giedl J, Stoehr R, Junker K, Boehm S, van Oers J, Zwarthoff E, Blaszyk H, Fine S, Humphrey P, Dehner L, Amin M, Epstein J, Hartmann A: Genomic aberrations are rare in urothelial neoplasms of patients 19 years or younger. J Pathol. 2006Google Scholar
  32. Clausen OP, Aass HC, Beigi M, Purdie KJ, Proby CM, Brown VL, Mattingsdal M, Micci F, Kolvraa S, Bolund L, Deangelis PM: Are Keratoacanthomas Variants of Squamous Cell Carcinomas? A Comparison of Chromosomal Aberrations by Comparative Genomic Hybridization. J Invest Dermatol. 2006Google Scholar
  33. van Beers EH, van Welsem T, Wessels LF, Li Y, Oldenburg RA, Devilee P, Cornelisse CJ, Verhoef S, Hogervorst FB, van't Veer LJ, Nederlof PM: Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations. Cancer Res. 2005, 65: 822-827.PubMedGoogle Scholar
  34. The Perl Directory at Perl.org. [http://www.perl.org]
  35. UCSC Genome Bioinformatics. [http://genome.ucsc.edu]
  36. Bioconductor – open source software for bioinformatics. [http://www.bioconductor.org]
  37. The R Project for Statistical Computing. [http://www.r-project.org]
  38. GD.pm Perl interface to GD graphics library. [http://stein.cshl.org/WWW/software/GD/]
  39. GD open source code library for the dynamic creation of images. [http://www.libgd.org]
  40. Vos CB, ter Haar NT, Rosenberg C, Peterse JL, Cleton-Jansen AM, Cornelisse CJ, van de Vijver MJ: Genetic alterations on chromosome 16 and 17 are important features of ductal carcinoma in situ of the breast and are associated with histologic type. Br J Cancer. 1999, 81: 1410-1418. 10.1038/sj.bjc.6693372.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Fridlyand J, Snijders AM, Ylstra B, Li H, Olshen A, Segraves R, Dairkee S, Tokuyasu T, Ljung BM, Jain AN, McLennan J, Ziegler J, Chin K, Devries S, Feiler H, Gray JW, Waldman F, Pinkel D, Albertson DG: Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer. 2006, 6: 96-10.1186/1471-2407-6-96.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Aubele M, Auer G, Braselmann H, Nahrig J, Zitzelsberger H, Quintanilla-Martinez L, Smida J, Walch A, Hofler H, Werner M: Chromosomal imbalances are associated with metastasis-free survival in breast cancer patients. Anal Cell Pathol. 2002, 24: 77-87.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, Fleming T, Eiermann W, Wolter J, Pegram M, Baselga J, Norton L: Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001, 344: 783-792. 10.1056/NEJM200103153441101.View ArticlePubMedGoogle Scholar
  44. Barlund M, Tirkkonen M, Forozan F, Tanner MM, Kallioniemi O, Kallioniemi A: Increased copy number at 17q22-q24 by CGH in breast cancer is due to high-level amplification of two separate regions. Genes Chromosomes Cancer. 1997, 20: 372-376. 10.1002/(SICI)1098-2264(199712)20:4<372::AID-GCC8>3.0.CO;2-Z.View ArticlePubMedGoogle Scholar
  45. Mattfeldt T, Wolter H, Kemmerling R, Gottfried HW, Kestler HA: Cluster analysis of comparative genomic hybridization (CGH) data using self-organizing maps: application to prostate carcinomas. Anal Cell Pathol. 2001, 23 (1): 29-37.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Ribeiro FR, Jeronimo C, Henrique R, Fonseca D, Oliveira J, Lothe RA, Teixeira MR: 8q gain is an independent predictor of poor survival in diagnostic needle biopsies from prostate cancer suspects. Clin Cancer Res. 2006, 12: 3961-3970. 10.1158/1078-0432.CCR-05-1977.View ArticlePubMedGoogle Scholar
  47. Alers JC, Krijtenburg PJ, Vis AN, Hoedemaeker RF, Wildhagen MF, Hop WC, van Der Kwast TT, Schroder FH, Tanke HJ, van Dekken H: Molecular cytogenetic analysis of prostatic adenocarcinomas from screening studies: early cancers may contain aggressive genetic features. Am J Pathol. 2001, 158: 399-406.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Sun J, Liu W, Adams TS, Sun J, Li X, Turner AR, Chang B, Kim JW, Zheng SL, Isaacs WB, Xu J: DNA copy number alterations in prostate cancers: a combined analysis of published CGH studies. Prostate. 2007, 67: 692-700. 10.1002/pros.20543.View ArticlePubMedGoogle Scholar
  49. van Grieken NC, Weiss MM, Meijer GA, Hermsen MA, Scholte GH, Lindeman J, Craanen ME, Bloemena E, Meuwissen SG, Baak JP, Kuipers EJ: Helicobacter pylori-related and -non-related gastric cancers do not differ with respect to chromosomal aberrations. J Pathol. 2000, 192: 301-306. 10.1002/1096-9896(2000)9999:9999<::AID-PATH697>3.0.CO;2-F.View ArticlePubMedGoogle Scholar
  50. Osterberg L, Akeson M, Levan K, Partheen K, Zetterqvist BM, Brannstrom M, Horvath G: Genetic alterations of serous borderline tumors of the ovary compared to stage I serous ovarian carcinomas. Cancer Genet Cytogenet. 2006, 167: 103-108. 10.1016/j.cancergencyto.2006.01.009.View ArticlePubMedGoogle Scholar
  51. Partheen K, Levan K, Osterberg L, Helou K, Horvath G: Analysis of cytogenetic alterations in stage III serous ovarian adenocarcinoma reveals a heterogeneous group regarding survival, surgical outcome, and substage. Genes Chromosomes Cancer. 2004, 40: 342-348. 10.1002/gcc.20053.View ArticlePubMedGoogle Scholar
  52. Ghadimi BM, Grade M, Liersch T, Langer C, Siemer A, Fuzesi L, Becker H: Gain of chromosome 8q23-24 is a predictive marker for lymph node positivity in colorectal cancer. Clin Cancer Res. 2003, 9: 1808-1814.PubMedGoogle Scholar
  53. De Angelis PM, Stokke T, Beigi M, Flatberg G, Enger M, Haug K, Aass HC, Schjolberg A, Andresen PA, Ariansen S, Bo AS, Mjaland O, Clausen OP: Chromosomal 20q gain in the DNA diploid component of aneuploid colorectal carcinomas. Int J Cancer. 2007Google Scholar
  54. Grade M, Becker H, Liersch T, Ried T, Ghadimi BM: Molecular cytogenetics: genomic imbalances in colorectal cancer and their clinical impact. Cell Oncol. 2006, 28: 71-84.PubMedPubMed CentralGoogle Scholar
  55. Terracciano L, Tornillo L: Cytogenetic alterations in liver cell tumors as detected by comparative genomic hybridization. Pathologica. 2003, 95: 71-82.PubMedGoogle Scholar
  56. Steinemann D, Skawran B, Becker T, Tauscher M, Weigmann A, Wingen L, Tauscher S, Hinrichsen T, Hertz S, Flemming P, Flik J, Wiese B, Kreipe H, Lichter P, Schlegelberger B, Wilkens L: Assessment of differentiation and progression of hepatic tumors using array-based comparative genomic hybridization. Clin Gastroenterol Hepatol. 2006, 4: 1283-1291. 10.1016/j.cgh.2006.07.010.View ArticlePubMedGoogle Scholar
  57. Lau SH, Guan XY: Cytogenetic and molecular genetic alterations in hepatocellular carcinoma. Acta Pharmacol Sin. 2005, 26: 659-665. 10.1111/j.1745-7254.2005.00126.x.View ArticlePubMedGoogle Scholar
  58. Jares P, Fernandez PL, Campo E, Nadal A, Bosch F, Aiza G, Nayach I, Traserra J, Cardesa A: PRAD-1/cyclin D1 gene amplification correlates with messenger RNA overexpression and tumor progression in human laryngeal carcinomas. Cancer Res. 1994, 54: 4813-4817.PubMedGoogle Scholar
  59. Richter H, Braselmann H, Hieber L, Thomas G, Bogdanova T, Tronko N, Zitzelsberger H: Chromosomal imbalances in post-chernobyl thyroid tumors. Thyroid. 2004, 14: 1061-1064. 10.1089/thy.2004.14.1061.View ArticlePubMedGoogle Scholar
  60. Chen X, Knauf JA, Gonsky R, Wang M, Lai EH, Chissoe S, Fagin JA, Korenberg JR: From amplification to gene in thyroid cancer: a high-resolution mapped bacterial-artificial-chromosome resource for cancer chromosome aberrations guides gene discovery after comparative genome hybridization. Am J Hum Genet. 1998, 63: 625-637. 10.1086/301973.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Schulte KM, Niederacher D, An HX, Staudt S, Simon D, Beckmann MW, Goretzki PE: INT-2 gene amplification in differentiated human thyroid cancer. Exp Clin Endocrinol Diabetes. 1996, 104 (Suppl 4): 101-104.View ArticlePubMedGoogle Scholar
  62. Yakut T, Schulten HJ, Demir A, Frank D, Danner B, Egeli U, Gebitekin C, Kahler E, Gunawan B, Urer N, Ozturk H, Fuzesi L: Assessment of molecular events in squamous and non-squamous cell lung carcinoma. Lung Cancer. 2006, 54: 293-301. 10.1016/j.lungcan.2006.08.011.View ArticlePubMedGoogle Scholar
  63. Rao PH, Arias-Pulido H, Lu XY, Harris CP, Vargas H, Zhang FF, Narayan G, Schneider A, Terry MB, Murty VV: Chromosomal amplifications, 3q gain and deletions of 2q33-q37 are the frequent genetic changes in cervical carcinoma. BMC Cancer. 2004, 4: 5-10.1186/1471-2407-4-5.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Koon N, Zaika A, Moskaluk CA, Frierson HF, Knuutila S, Powell SM, El-Rifai W: Clustering of molecular alterations in gastroesophageal carcinomas. Neoplasia. 2004, 6: 143-149. 10.1593/neo.03385.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Bentz M, Bergerheim US, Li C, Joos S, Werner CA, Baudis M, Gnarra J, Merino MJ, Zbar B, Linehan WM, Lichter P: Chromosome imbalances in papillary renal cell carcinoma and first cytogenetic data of familial cases analyzed by comparative genomic hybridization. Cytogenet Cell Genet. 1996, 75 (1): 17-21.View ArticlePubMedGoogle Scholar
  66. Hoglund M, Gisselsson D, Soller M, Hansen GB, Elfving P, Mitelman F: Dissecting karyotypic patterns in renal cell carcinoma: an analysis of the accumulated cytogenetic data. Cancer Genet Cytogenet. 2004, 153: 1-9. 10.1016/j.cancergencyto.2003.12.019.View ArticlePubMedGoogle Scholar
  67. Li X, Wang E, Zhao YD, Ren JQ, Jin P, Yao KT, Marincola FM: Chromosomal imbalances in nasopharyngeal carcinoma: a meta-analysis of comparative genomic hybridization results. J Transl Med. 2006, 4: 4-10.1186/1479-5876-4-4.View ArticlePubMedPubMed CentralGoogle Scholar
  68. Seng TJ, Low JS, Li H, Cui Y, Goh HK, Wong ML, Srivastava G, Sidransky D, Califano J, Steenbergen RD, Rha SY, Tan J, Hsieh WS, Ambinder RF, Lin X, Chan AT, Tao Q: The major 8p22 tumor suppressor DLC1 is frequently silenced by methylation in both endemic and sporadic nasopharyngeal, esophageal, and cervical carcinomas, and inhibits tumor cell colony formation. Oncogene. 2007, 26: 934-944. 10.1038/sj.onc.1209839.View ArticlePubMedGoogle Scholar
  69. El-Rifai W, Kamel D, Larramendy ML, Shoman S, Gad Y, Baithun S, El-Awady M, Eissa S, Khaled H, Soloneski S, Sheaff M, Knuutila S: DNA copy number changes in Schistosoma-associated and non-Schistosoma-associated bladder cancer. Am J Pathol. 2000, 156: 871-878.View ArticlePubMedPubMed CentralGoogle Scholar
  70. Duerr EM, Chung DC: Molecular genetics of neuroendocrine tumors. Best Pract Res Clin Endocrinol Metab. 2007, 21: 1-14. 10.1016/j.beem.2006.12.001.View ArticlePubMedGoogle Scholar
  71. Bauer J, Bastian BC: [DNA copy number changes in the diagnosis of melanocytic tumors]. Pathologe. 2007Google Scholar
  72. Karhu R, Mahlamaki E, Kallioniemi A: Pancreatic adenocarcinoma – genetic portrait from chromosomes to microarrays. Genes Chromosomes Cancer. 2006, 45: 721-730. 10.1002/gcc.20337.View ArticlePubMedGoogle Scholar
  73. Lee JY, Park YN, Uhm KO, Park SY, Park SH: Genetic alterations in intrahepatic cholangiocarcinoma as revealed by degenerate oligonucleotide primed PCR-comparative genomic hybridization. J Korean Med Sci. 2004, 19: 682-687.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Coe BP, Lee EH, Chi B, Girard L, Minna JD, Gazdar AF, Lam S, MacAulay C, Lam WL: Gain of a region on 7p22.3, containing MAD1L1, is the most frequent event in small-cell lung cancer cell lines. Genes Chromosomes Cancer. 2006, 45: 11-19. 10.1002/gcc.20260.View ArticlePubMedGoogle Scholar
  75. Levan K, Partheen K, Osterberg L, Helou K, Horvath G: Chromosomal alterations in 98 endometrioid adenocarcinomas analyzed with comparative genomic hybridization. Cytogenet Genome Res. 2006, 115: 16-22. 10.1159/000094796.View ArticlePubMedGoogle Scholar
  76. Bryndorf T, Kirchhoff M, Larsen J, Andreasson B, Bjerregaard B, Westh H, Rose H, Lundsteen C: The most common chromosome aberration detected by high-resolution comparative genomic hybridization in vulvar intraepithelial neoplasia is not seen in vulvar squamous cell carcinoma. Cytogenet Genome Res. 2004, 106: 43-48. 10.1159/000078559.View ArticlePubMedGoogle Scholar
  77. Burnworth B, Popp S, Stark HJ, Steinkraus V, Brocker EB, Hartschuh W, Birek C, Boukamp P: Gain of 11q/cyclin D1 overexpression is an essential early step in skin cancer development and causes abnormal tissue organization and differentiation. Oncogene. 2006, 25: 4399-4412. 10.1038/sj.onc.1209474.View ArticlePubMedGoogle Scholar
  78. Vogelstein B, Kinzler KW: The multistep nature of cancer. Trends Genet. 1993, 4: 138-141. 10.1016/0168-9525(93)90209-Z.View ArticleGoogle Scholar
  79. Seruca R, Suijkerbuijk RF, Gartner F, Criado B, Veiga I, Olde-Weghuis D, David L, Castedo S, Sobrinho-Simoes M: Increasing levels of MYC and MET co-amplification during tumor progression of a case of gastric cancer. Cancer Genet Cytogenet. 1995, 82: 140-145. 10.1016/0165-4608(95)00033-L.View ArticlePubMedGoogle Scholar
  80. Houldsworth J, Mathew S, Rao PH, Dyomina K, Louie DC, Parsa N, Offit K, Chaganti RS: REL proto-oncogene is frequently amplified in extranodal diffuse large cell lymphoma. Blood. 1996, 87: 25-29.PubMedGoogle Scholar
  81. Perry A, Nobori T, Ru N, Anderl K, Borell TJ, Mohapatra G, Feuerstein BG, Jenkins RB, Carson DA: Detection of p16 gene deletions in gliomas: a comparison of fluorescence in situ hybridization (FISH) versus quantitative PCR. J Neuropathol Exp Neurol. 1997, 56: 999-1008.View ArticlePubMedGoogle Scholar
  82. Nessling M, Solinas-Toldo S, Wilgenbus KK, Borchard F, Lichter P: Mapping of chromosomal imbalances in gastric adenocarcinoma revealed amplified protooncogenes MYCN, MET, WNT2, and ERBB2. Genes Chromosomes Cancer. 1998, 23: 307-316. 10.1002/(SICI)1098-2264(199812)23:4<307::AID-GCC5>3.0.CO;2-#.View ArticlePubMedGoogle Scholar
  83. Joos S, Menz CK, Wrobel G, Siebert R, Gesk S, Ohl S, Mechtersheimer G, Trumper L, Moller P, Lichter P, Barth TF: Classical Hodgkin lymphoma is characterized by recurrent copy number gains of the short arm of chromosome 2. Blood. 2002, 99: 1381-1387. 10.1182/blood.V99.4.1381.View ArticlePubMedGoogle Scholar
  84. Penzel R, Hoegel J, Schmitz W, Blaeker H, Morresi-Hauf A, Aulmann S, Hecker E, Mechtersheimer G, Otto HF, Rieker RJ: Clusters of chromosomal imbalances in thymic epithelial tumours are associated with the WHO classification and the staging system according to Masaoka. Int J Cancer. 2003, 105: 494-498. 10.1002/ijc.11101.View ArticlePubMedGoogle Scholar
  85. Liu J, Mohammed J, Carter J, Ranka S, Kahveci T, Baudis M: Distance-based clustering of CGH data. Bioinformatics. 2006, 22: 1971-1978. 10.1093/bioinformatics/btl185.View ArticlePubMedGoogle Scholar
  86. Liu J, Ranka S, Kahveci T: Markers improve clustering of CGH data. Bioinformatics. 2007, 23: 450-457. 10.1093/bioinformatics/btl624.View ArticlePubMedGoogle Scholar
  87. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/7/226/prepub

Copyright

© Baudis; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.