The stable traits of melanoma genetics: an alternate approach to target discovery
- Tara L Spivey1, 2, 3,
- Valeria De Giorgi1, 15Email author,
- Yingdong Zhao4,
- Davide Bedognetti1, 5, 6,
- Zoltan Pos7,
- Qiuzhen Liu1,
- Sara Tomei1, 8,
- Maria Libera Ascierto1, 5, 9,
- Lorenzo Uccellini1, 12,
- Jennifer Reinboth1, 13, 14,
- Lotfi Chouchane10,
- David F Stroncek11,
- Ena Wang1 and
- Francesco M Marincola1, 15Email author
© Spivey et al; licensee BioMed Central Ltd. 2012
Received: 12 October 2011
Accepted: 26 April 2012
Published: 26 April 2012
The weight that gene copy number plays in transcription remains controversial; although in specific cases gene expression correlates with copy number, the relationship cannot be inferred at the global level. We hypothesized that genes steadily expressed by 15 melanoma cell lines (CMs) and their parental tissues (TMs) should be critical for oncogenesis and their expression most frequently influenced by their respective copy number.
Functional interpretation of 3,030 transcripts concordantly expressed (Pearson's correlation coefficient p-value < 0.05) by CMs and TMs confirmed an enrichment of functions crucial to oncogenesis. Among them, 968 were expressed according to the transcriptional efficiency predicted by copy number analysis (Pearson's correlation coefficient p-value < 0.05). We named these genes, "genomic delegates" as they represent at the transcriptional level the genetic footprint of individual cancers. We then tested whether the genes could categorize 112 melanoma metastases. Two divergent phenotypes were observed: one with prevalent expression of cancer testis antigens, enhanced cyclin activity, WNT signaling, and a Th17 immune phenotype (Class A). This phenotype expressed, therefore, transcripts previously associated to more aggressive cancer. The second class (B) prevalently expressed genes associated with melanoma signaling including MITF, melanoma differentiation antigens, and displayed a Th1 immune phenotype associated with better prognosis and likelihood to respond to immunotherapy. An intermediate third class (C) was further identified. The three phenotypes were confirmed by unsupervised principal component analysis.
This study suggests that clinically relevant phenotypes of melanoma can be retraced to stable oncogenic properties of cancer cells linked to their genetic back bone, and offers a roadmap for uncovering novel targets for tailored anti-cancer therapy.
KeywordsMelanoma Melanoma genetics Cancer Tumor microenvironment
Advanced melanoma remains one of the cancers with the poorest prognosis [1, 2] as patients can expect to live less than 8 months on average once their disease metastasizes . In fact, metastatic melanoma's genetic instability poses a major challenge for the development of targeted therapies. This is evidenced by the poor long term outcomes observed when individual pathways are targeted as alternate oncogenic mechanisms rapidly develop and prevail [1, 4, 5]. Immunotherapy is also hampered by unstable cancer cell phenotypes that rapidly evolve under the selective pressure of immune effector mechanisms [6, 7]. Whole-genome studies have improved our understanding of melanoma biology, but much more needs to be discovered. For instance, a decade ago global transcriptional profiling suggested that over-expression of WNT5A denoted a highly aggressive melanoma phenotype associated with enhanced cellular motility . Moreover, the poor prognosis phenotype was associated with a more undifferentiated status with no expression of the melanoma differentiation antigen MelanA/Mart-1; yet, this important functional insight failed to yield a useful clinical application and a global understanding of genetic determinants responsible for the two phenotypes remains elusive.
Chromosomal aberrations are a common feature of human cancers, are more pronounced in solid tumors than hematologic cancers and occur with consistency in malignant melanomas [9–12]. However the debate over the role that chromosomal aneuploidy plays in cancer is ongoing [9, 13–15] and the relationship between alterations in gene copy number and respective gene expression is not clear-cut [16–19]. The transcriptional repercussions of chromosomal copy number imbalances relies on their influence on gene expression, but model systems, such as cancer cell lines suggest a limited relationship . Cancer cell lines provide a non-invasive tool for studying fundamental aspects of human cancer biology and are easily accessible for research . However, cell lines, while providing information about stable features of cancer genetics, do not inform about salient aspects of their biology in the interactive tumor microenvironment and about potential selection in vitro of non-representative sub-clones. This study, therefore, was aimed at the identification of consistent correlates between cell lines and parental tissues that define stable principles of cancer biology valid in vitro and in vivo. This may constitute an alternate roadmap to the identification of relevant therapeutic targets.
We hypothesized that genes concordantly expressed by parental tissues and their cell line progeny may embody necessary elements for the maintenance of oncogenesis. The concordance of expression may gradually decline according to causality from transcripts driving (i.e. signaling and cell cycle regulating molecules), to those associated with oncogenesis (i.e. cancer testis antigens), and to those related to the ontogeny of melanoma (i.e. melanoma differentiation antigens). We also reasoned that, if such hierarchy existed, transcripts with highest concordance of expression between tissues and cell lines should also be most likely to be affected by genetic factors driving the oncogenic process including aneuploidy. Thus, we tested the degree with which transcripts stably expressed by cancer cells in vivo and in vitro matched in expression the prediction suggested by the corresponding amplification or deletion at the respective gene. Having identified a set of genes that matched this requirement we explored whether their expression in 112 melanoma metastases could be related to previous taxonomic classification of melanoma . Two divergent phenotypes of melanoma were observed. The first phenotype was characterized by prevalent expression of cancer testis antigens, WNT5A and a Th17 immune phenotype; those characteristics have all been ascribed to a more aggressive behavior of cancer (Class A) [8, 20–22]. A second phenotype (Class B) was characterized by prevalent expression of melanoma differentiation antigens and a Th1 immune phenotype; both characteristics associated with better prognosis. A third category sitting astride the two polar groups was also identified (Class C). Thus, this study links clinically relevant transcriptional signatures of melanoma to stable oncogenic properties of cancer cells and offers a road map for uncovering novel targets of therapy.
Genetic characterization of the 15 melanoma cell lines
Functional genomics correlates between parental tissues and derived cell lines: definition of cancer-specific transcripts
As a measure of comparison, 3,000 genes that were not correlated between TMs and CMs (Pearson's y < 0.1) were randomly selected and analyzed via IPA. The top network pathways in this cohort did not include any cancer-related pathway. The top biological function included genetic disorder, hematologic disease, connective tissue disorders, immunological disease, and inflammatory disease (p < 0.01 for all pathways) (Data not shown).
Correlation between gene copy number and transcription: definition of "genomic delegates"
Of the 3,030 genes stably expressed between TMs and CMs, only 968 (32%) were concordant (significance cutoff p < 0.05) with their respective genomic imbalance (Figure 3B, gene list provided in Additional file 2: Table S1) confirming previous estimates ; we refer to them as "genomic delegates" as they represent in expression the genetic footprint of individual cancers. IPA revealed that these genes are tightly related to oncogenesis (Figure 3C). The location of the delegate genes spanned the entire genome and included copy number gains (34%) and deletions (14%), while approximately half of the stably expressed genes (52%) belonged to genomic regions with no copy number change (Figure 3D). We then tested whether the expression of the delegate genes could segregate autologous TM/CM pairs in harmony (Figure 3E). Although the set of delegate genes was derived from the lower stringency 3,030 gene pool, which could not pair CMs with TMs as well as the higher stringency pool of 1,000 genes (Figure 3E), hierarchical clustering of the 968 delegate genes (based on concordance with genetic imbalances) yielded results similar to the higher stringency cluster analysis revealing that 11 CMs paired with their parental TM. The frequency of putative housekeeping genes was 2.2% and 0% according to the two respective references [28, 30] confirming that no enrichment for endogenous genes related to basic cell metabolism resulted from this strategy.
Functional relevance of delegate genes
Genetic basis determining TARA's classification
Analyses of the genetic differences among the three classes of melanoma or among the respective cell lines are being undertaken to identify regions of potential interest for the identification of novel oncogenes or tumor suppressor genes. A preliminary analysis did not identify striking differences between the two (class A vs class B) suggesting that the distinct phenotypes cannot simply be attributed to different levels of chromosomal instability and consequent aneuploidy but to more specific alterations of the genomic/transcriptomic axis that will require extensive evaluation. In particular, there were no specific differences in expression of microtubule depolymerases such as Kif2, MCAK or other regulatory components of the kinetochore  among the 15 cell lines ranked according to the segregation of their parental tumors into the different TARA's classes; similarly, sequencing of c-KIT, BRAF, KRAS, HRAS and NRAS did not identified specific polymorphisms or mutations that could explain the two phenotypes, nor could the analysis of the individual gene copy number (Additional file 5: Table S3).
It has been suggested that gene copy number bears causation in oncogenesis [14, 15] by directly or indirectly influencing the transcriptional activity of individual genes such as B-Raf [39–41]. It has also been suggested that gene copy number can affect the global transcriptional pattern of cancers; Pollack et al. observed  that breast cancers could be equally segregated into subclasses either according to the pattern of genomic imbalances or the expression of genes resident in the areas of imbalances. However, the same study did not evaluate whether identical classification could be obtained by using genes not included in the genomic imbalances as a basis for re-clustering. When this was tested by a subsequent study, it was observed that autologous cell lines segregated separately from heterologous ones whether copy number changes were used for re-clustering or whether the expression of resident or non-resident genes was considered for re-clustering. This observation questioned whether genomic imbalances influence transcription at the global transcriptional level . Thus, it remains unclear to what extent genetic imbalances affect transcription. Although at first glance it may seem intuitive that chromosomal gains should result in increased expression and vice versa for chromosomal depletions (loss of heterozygosity, homozygous deletion), on second thought, it should not be surprising that this linear relationship may be overwhelmed by the complexity of gene regulation. Amplification may result in the over expression of a transcription factor, which may in turn affect the expression of hundred of genes in other chromosomes with or without imbalances, therefore, obscuring direct from indirect effects. Moreover, structural analyses do not take into account mutations in the genome that may affect protein expression and function, nor the role that transcriptional regulators expressed in balanced genomic areas may play on genes included in regions of genomic imbalances.
Tumor cell lines are commonly employed to study properties of human cancer believed to be clinically relevant. Although cell lines are not perfect because they do not account for the influence of the tumor microenvironment, matching in vivo and in vitro information provides a powerful approach to describe highly conserved characteristics that can be relevant to the oncogenic process; yet, genome-wide comparisons between parental tumors and cell line progeny are limited . In this study, we had the opportunity to compare the transcriptional profile of melanoma cell lines with that of their parental tissue identifying transcripts consistently expressed; there are several reasons for transcriptional patterns to be discordant between cell lines and parental tissues; transcripts expressed by normal cell infiltrates are obviously missing; moreover, cancer cell transcription in vitro is unaffected by the crosstalk with other cells through paracrine secretion or cell to cell contact; furthermore, as cultured cell expand in vitro, cancer cell clones present at low frequency in the parental tumor may take over in culture; in particular, this in vitro natural selection may favor the expansion of stem cell-like subcomponents of different autologous tumors. Finally, the genetic drift due to the instability of cancer may incrementally diverge transcriptional patterns with subsequent in vitro passages. However, it is possible that properties driving the oncogenic process may be insensitive to surrounding influences or to time as they represent requirements for growth. Thus, transcription of some genes may remain steady because the neoplastic process depends upon them. Moreover, gene expression may coincide in vivo and in vitro because it is cancer-restricted though not causative as in the expression of cancer testis antigens , melanoma differentiation antigens [26, 43] or kidney-specific transcripts . This study identified about 3,000 stably expressed genes (Figure 2C) and the top 1,000 defined a tumor-specific finger print that accurately matched CMs with their respective TMs. Functional interpretation demonstrated that these genes were almost exclusively associated with the oncogenic process while most cancer testis antigens and melanoma differentiation antigens ranked lower in the correlation scale (data not shown).
We then quantified the weight of genetic imbalances on the stably expressed genes. One could suspect that a gene stably expressed in vivo and in vitro and relevant to oncogenesis may be more likely be expressed in concordance with the corresponding genomic imbalance than an irrelevant gene produced by infiltrating normal cells such as interferon-γ whose expression is likely dependent upon environmental factors. Expanding stochastically on this premise, one would predict a gradual decrease in concordance between copy number and transcription with decreasing stability of gene expression between CMs and TMs. This was exactly what we observed (Figure 3A). To our knowledge, this is the first compelling evidence that genetic imbalances significantly influence the global expression of the respective genes. Interestingly, this influence is limited: the percent of genes expressed in concordance with their copy number reached a plateau of 32% at the minimal cutoff of significance (Pearson's correlation coefficient p-value < 0.05) and did not change with increasing level of concordance between CMs and TMs. Thus, this model allowed the detection and quantification of a genome/transcriptome axis representative of stable properties of cancer cells inclusive of 968 transcripts that we named "genomic delegates" as they represent at the transcriptional level the genetic footprint of individual cancers.
When the genomic delegates were applied to a set of 112 consecutive melanoma metastases, two divergent phenotypes were observed with a third sitting astride; we termed them TARA's (transcriptional adjustments related to amplification/deletion) class A, B and C. Although these subclasses were "discovered" based on gene associated with copy number variation and steadily expressed in vivo and in vitro, it appears that they represent a natural phenotype of melanoma that segregated separately also by unsupervised testing adopting as a platform the complete genome-wide data set (Figure 4B). Moreover, functional analyses based on the selection of genes known to be relevant to melanoma biology segregated the three classes (with A and B representing the extremes): TARA's class A tumors prevalently expressed transcripts related to deregulation of WNT and g-protein coupled signaling and cyclins activity while class B aligned to a canonical activation of the MAP kinase pathway and classic melanoma signaling (Figure 4E). Furthermore, class A expressed transcripts that we previously observed to be expressed in melanoma with more invasive behavior such as WNT5A  or MAGEA genes [31, 32] while Class B was enriched with transcripts associated with better prognosis  and the expression of melanocytic lineage specific genes  denoting a higher status of differentiation (Figure 4F). Finally, TARA's class A metastases displayed a classic Th17 phenotype while class B a Th1; this finding is clinically relevant as the two immune phenotypes have distinct prognostic weight in cancer with the former being associated with poor prognosis  and the latter with good prognosis and likelihood to respond to immunotherapy [33–35, 45]. Analyses of the genetic differences among the three classes of melanoma or among the respective cell lines are being undertaken to identify regions of potential interest for the identification of novel oncogenes or tumor suppressor genes. Although the discovery through the genomic delegates of at least two classes of metastatic melanoma that differ on a broader spectrum not limited to the former; it is important to observe, how, such sub-classification stems, at least in part from the genetic backbone of individual cancers and, therefore, clinically relevant aspects of individual phenotypes may in the future be traced back to genetic alterations that have been mapped by this study.
The new classification of melanoma according to stably expressed genes provided new insights about of clinical relevance. It appears that TARA's class B represents a subtype of melanoma more closely linked to the melanocytic lineage while class A represents a more undifferentiated and less melanoma-specific subtype enriched by the co-ordinate activation of functions related to migration, tissue regeneration and paracrine and autocrine signaling, a phenomenon we previously described in an independent analysis of melanoma metastases . More broadly, this study provides evidence that clinically relevant phenotypes of melanoma can be retraced to the genetic back bone of individual cancer cells offering a tool for uncovering novel targets for tailored anti-cancer therapy.
Melanoma cell culture
Melanoma cell lines were derived from metastatic melanoma lesions from patients treated at the Surgery Branch, National Cancer Institute (NCI), Bethesda, MD kindly donated by Dr Steven A Rosenberg. The cells we received from Surgery Branch were after passage 3. Cells were cultured in bulk at 37°C, in CO2 5% with RPMI 1640 medium (Gibco) supplemented with 10% heat-inactivated Fetal Bovine Serum (FBS, Cellgro), 0.01% L-glutamine Pen-Strep Solution (GPS 100x, Gemini Bio-Products), 0.001% Ciprofloxacin (10 mg/mL) and 0.01% Fungizone Amphotericin B (250 μg/mL, Gibco). Confluent adhering cells were washed twice with cold Phosphate Buffered Saline 1X (PBS pH 7.4, Gibco) and detached by exposure to 0.2% Trypsin-EDTA (0.5%:0.53 mM Solution, Gemini Bio-Products). The obtained cell suspension was centrifuged to remove cell debris and suspended in fresh medium to a final concentration of 107cells/mL. Early-passage cultures (< 10) were used for all experiments and no clonal sub selection was performed.
Identity confirmation of cell lines and parental tissue by HLA phenotyping
Genomic DNA was extracted using QIAamp® DNA Mini Kit (Qiagen) according to the manufacture's protocol. DNA quality and quantity was estimated using Nanodrop (Thermo Scientific). The HLA Class I phenotype of all cell lines and from normal autologous lymphocytes from the same patients was tested by HLA Laboratory, Department of Transfusion Medicine, National Institutes of Health, Bethesda (MD). The HLA type of 15 cell lines out of the original 16 tested matched perfectly according to the original HLA type of the patients and therefore only 15 matched cell lines (CM) were studied and compared with their respective matched tumor samples (TM).
Total RNA from 15 cell line and autologous tumor pairs plus another 97 heterologous melanoma metastases (total 112 melanoma metastases) from patients treated at the Surgery Branch, NCI were extracted using miRNeasy minikit (Qiagen) according to the manufacture's protocol. RNA quality and quantity was estimated using Nanodrop (Thermo Scientific) and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). First- and second-strand cDNA were synthesized from 300 ng of total RNA according to manufacturer's instructions (Ambion WT Expression Kit). cDNAs were fragmented, biotinylated, and hybridized to the GeneChip Human Gene 1.0 ST Arrays (Affymetrix WT Terminal Labeling Kit). The arrays were washed and stained on a GeneChip Fluidics Station 450 (Affymetrix); scanning was carried out with the GeneChip Scanner 3000 and image analysis with the Affymetrix GeneChip Command Console Scan Control. Expression data were normalized, background-corrected, and summarized using the RMA algorithm, http://www.partek.com/. Data were log-transformed (base 2) for subsequent statistical analysis. Cluster analysis was performed using Partek software.
Array comparative genomic hybridization (CGH)
Human advanced melanoma cell lines were isolated and 1.5 μg genomic DNA extracted using QIAamp® DNA Mini Kit (Qiagen). For the healthy diploid reference, 1.5 μg genomic DNA was isolated from the PBMCs of a healthy female donor using QIAamp® DNA Mini Kit (Qiagen). DNA fragmented, labeled, purified, and hybridized to Agilent 2 × 105 K arrays according to the Agilent Oligonucleotide Array-Based CGH for Genomic DNA Analysis (version 6.2.1). Washing and scanning in Agilent BioScanner B took place immediately after hybridization. Data was extracted using Agilent's Feature Extraction Softward.
Copy Number Analysis was performed according to Partek suggested parameters. Copy number variations are measured by two-color data comparing melanoma cell lines to healthy diploid reference genomic DNA, and values are reported as intensity log2 ratios. Amplifications were defined as segments with log2 ratios greater than 0.15. Deletions were defined as segments with log2 ratios less than -0.3. Significantly different regions were determined using the Segmentation Model algorithm of the Partek Genomic Suite set to detect copy number states. Segments were defined as regions that differed from neighboring regions by at least 2 signal to noise ratios (SNRs) in at least 10 markers. Regions identified were annotated with gene symbols by importing the annotation file from the NCBI RefSeq genome browser (build Hg19).
All analyses were performed using Partek Genomic Suite, BRB Array tool , or R package. Congruency of gene expression among parental tissues and derivative cell lines was assessed by correlation analysis using the Pearson correlation coefficient. Pearson correlation between chromosomal copy number data and gene expression data was performed within Partek software using the "Biologic Integration/Correlating Gene Expression and Copy Number" function. DNA log2 ratio copy number variation data was correlated with mRNA gene expression log2 ratios for all 15 cell line samples. The threshold for Pearson correlation significance for concordant data in this study was uniformly defined by p-value < 0.05. Tests for expression differences between different classes were conducted for individual genes using two-sided t tests, considering P values of < 0.001 as significant, with adjustment for the batch effect. Principal component analysis (PCA) was applied for visualization when relevant based on the complete data set. Heat maps are presented based on Partek visualization programs. Gene interaction analyses were executed using Ingenuity Pathways Analysis (IPA) tools 3.0 http://www.ingenuity.com.
comparative genomic hybridization
matched cell lines
matched tumor samples
Hidden Markov Model
ingenuity pathway analysis
principal component analysis
transcriptional adjustments related to amplification/deletion.
Tara Spivey's research fellowship was made possible through the Clinical Research Training Program, a public-private partnership supported jointly by the NIH and Pfizer Inc. (via a grant to the foundation for NIH from Pfizer Inc.).
- Ascierto PA, Streicher HZ, Sznol M: Melanoma: a model for testing new agents in combination therapies. J Transl Med. 2010, 8: 38-10.1186/1479-5876-8-38.PubMed CentralView ArticlePubMedGoogle Scholar
- Ascierto PA, De ME, Bertuzzi S, Palmieri G, Halaban R, Hendrix M, Kashani-Sabet M, Ferrone S, Wang E, Cochran A: Future perspectives in melanoma research. Meeting report from the "Melanoma Research: a bridge Naples-USA. Naples, December 6th-7th 2010". J Transl Med. 2011, 9: 32-10.1186/1479-5876-9-32.PubMed CentralView ArticlePubMedGoogle Scholar
- Garbe C, Eigentler TK, Keilholz U, Hauschild A, Kirkwood JM: Systematic review of medical treatment in melanoma: current status and future prospects. Oncologist. 2011, 16: 5-24. 10.1634/theoncologist.2010-0190.PubMed CentralView ArticlePubMedGoogle Scholar
- Xing F, Persaud Y, Pratilas CA, Taylor BS, Janakiraman M, She QB, Gallardo H, Liu C, Merghoub T, Hefter B: Concurrent loss of the PTEN and RB1 tumor suppressors attenuates RAF dependence in melanomas harboring (V600E)BRAF. Oncogene. 2012, 31: 446-457. 10.1038/onc.2011.250.PubMed CentralView ArticlePubMedGoogle Scholar
- Dienstmann R, Tabernero J: BRAF as a target for cancer therapy. Anticancer Agents Med Chem. 2011, 11: 285-295.View ArticlePubMedGoogle Scholar
- Marincola FM, Jaffe EM, Hicklin DJ, Ferrone S: Escape of human solid tumors from T cell recognition: molecular mechanisms and functional significance. Adv Immunol. 2000, 74: 181-273.View ArticlePubMedGoogle Scholar
- Marincola FM, Wang E, Herlyn M, Seliger B, Ferrone S: Tumors as elusive targets of T cell-based active immunotherapy. Trends Immunol. 2003, 24: 335-342.View ArticlePubMedGoogle Scholar
- Bittner M, Meltzer P, Chen Y, Jiang E, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A: Molecular classification of cutaneous malignant melanoma by gene expression: shifting from a countinuous spectrum to distinct biologic entities. Nature. 2000, 406: 536-840. 10.1038/35020115.View ArticlePubMedGoogle Scholar
- Roschke AV, Tonon G, Gehlhaus KS, McTyre N, Bussey KJ, Lababidi S, Scudiero DA, Weinstein JN, Kirsch IR: Karyotypic complexity of the NCI-60 drug-screening panel. Cancer Res. 2003, 63: 8634-8647.PubMedGoogle Scholar
- Thompson SL, Compton DA: Chromosomes and cancer cells. Chromosome Res. 2011, 19: 433-444. 10.1007/s10577-010-9179-y.PubMed CentralView ArticlePubMedGoogle Scholar
- Chinnaiyan AM, Palanisamy N: Chromosomal aberrations in solid tumors. Prog Mol Biol Transl Sci. 2010, 95: 55-94.View ArticlePubMedGoogle Scholar
- Bacolod MD, Barany F: Gene dysregulations driven by somatic copy number aberrations-biological and clinical implications in colon tumors: a paper from the 2009 William Beaumont Hospital Symposium on Molecular Pathology. J Mol Diagn. 2010, 12: 552-561. 10.2353/jmoldx.2010.100098.PubMed CentralView ArticlePubMedGoogle Scholar
- Jallepalli PV, Lengauer C: Chromosome segregation and cancer: cutting through the mystery. Nat Rev Cancer. 2001, 1: 109-117. 10.1038/35101065.View ArticlePubMedGoogle Scholar
- Weaver BA, Cleveland DW: Does aneuploidy cause cancer?. Curr Opin Cell Biol. 2006, 18: 658-667. 10.1016/j.ceb.2006.10.002.View ArticlePubMedGoogle Scholar
- Weaver BA, Cleveland DW: The role of aneuploidy in promoting and suppressing tumors. J Cell Biol. 2009, 185: 935-937. 10.1083/jcb.200905098.PubMed CentralView ArticlePubMedGoogle Scholar
- Squire JA, Bayani J, Luk C, Unwin L, Tokonaga J, MacMillan C, Irish J, Brown D, Gullane P, Kamel-Reid S: Molecular cytogenetic analysis of head and neck squamous cell carcinoma: by comparative genomic hybridization, spectral karyotyping and expression array analysis. Head Neck. 2002, 24: 874-887. 10.1002/hed.10122.View ArticlePubMedGoogle Scholar
- Clark J, Edwards S, Megan J, Flohr P, Gordon T, Maillard K, Giddings I, Brown C, Bagherzadeh A, Campbell C: Identification of amplified and expressed genes in breast cancer by comparative hybridization onto microarrays of randomly selected cDNA clones. Genes Chromosomes Cancer. 2002, 34: 104-114. 10.1002/gcc.10039.View ArticlePubMedGoogle Scholar
- 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. 10.1073/pnas.162471999.PubMed CentralView ArticlePubMedGoogle Scholar
- Sabatino M, Zhao Y, Voiculescu S, Monaco A, Robbins PF, Nickoloff BJ, Karai L, Selleri S, Maio M, Selleri S: Conservation of a core of genetic alterations over a decade of recurrent melanoma supports the melanoma stem cell hypothesis. Cancer Res. 2008, 68: 222-231.View ArticleGoogle Scholar
- Weeraratna AT, Jiang Y, Hostetter G, Rosenblatt K, Duray P, Bittner M, Trent JM: Wnt5a signaling directly affects cell motility and invasion of metastatic melanoma. Cancer Cell. 2002, 1: 279-288. 10.1016/S1535-6108(02)00045-4.View ArticlePubMedGoogle Scholar
- Dissanayake SK, Wade M, Johnson CE, O'Connell MP, Leotlela PD, French AD, Shah KV, Hewitt KJ, Rosenthal DT, Indig FE: The Wnt5A/protein kinase C pathway mediates motility in melanoma cells via the inhibition of metastasis suppressors and initiation of an epithelial to mesenchymal transition. J Biol Chem. 2007, 282: 17259-17271. 10.1074/jbc.M700075200.PubMed CentralView ArticlePubMedGoogle Scholar
- Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, Berger A, Bruneval P, Fridman WH, Pages F: Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Res. 2011, 71: 1263-1271. 10.1158/0008-5472.CAN-10-2907.View ArticlePubMedGoogle Scholar
- Gast A, Scherer D, Chen B, Bloethner S, Melchert S, Sucker A, Hemminki K, Schadendorf D, Kumar R: Somatic alterations in the melanoma genome: a high-resolution array-based comparative genomic hybridization study. Genes Chromosomes Cancer. 2010, 49: 733-745. 10.1002/gcc.20785.View ArticlePubMedGoogle Scholar
- Boon T, Coulie PG, van den Eynde BJ, Van Der BP: Human T cell responses against melanoma. Annu Rev Immunol. 2006, 24: 175-208. 10.1146/annurev.immunol.24.021605.090733.View ArticlePubMedGoogle Scholar
- Kawakami Y, Robbins P, Wang RF, Parkhurst MR, Kang X, Rosenberg SA: Tumor antigens recognized by T cells. The use of melanosomal proteins in the immunotherapy of melanoma. J Immunother. 1998, 21: 237-246. 10.1097/00002371-199807000-00001.View ArticlePubMedGoogle Scholar
- Wang E, Panelli MC, Zavaglia K, Mandruzzato S, Hu N, Taylor PR, Seliger B, Zanovello P, Freedman RS, Marincola FM: Melanoma-restricted genes. J Transl Med. 2004, 2: 34-10.1186/1479-5876-2-34.PubMed CentralView ArticlePubMedGoogle Scholar
- Basil CF, Zhao Y, Zavaglia K, Jin P, Panelli MC, Voiculescu S, Mandruzzato S, Lee HM, Seliger B, Freedman RS: Common cancer biomarkers. Cancer Res. 2006, 66: 2953-2961. 10.1158/0008-5472.CAN-05-3433.View ArticlePubMedGoogle Scholar
- Jin P, Zhao Y, Ngalame Y, Panelli MC, Nagorsen D, Monsurro' V, Smith K, Hu N, Su H, Taylor PR: Selection and validation of endogenous reference genes using a high throughput approach. BMC Genomics. 2004, 5: 55-10.1186/1471-2164-5-55.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhu J, He F, Song S, Wang J, Yu J: How many human genes can be defined as housekeeping with current expression data?. BMC Genomics. 2008, 9: 172-10.1186/1471-2164-9-172.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu YA, Shabahang S, Timiryasova TM, Zhang Q, Beltz R, Gentschev I, Goebel W, Szalay AA: Visualization of tumors and metastases in live animals with bacteria and vaccinia virus encoding light-emitting proteins. Nat Biotechnol. 2004, 22: 313-320. 10.1038/nbt937.View ArticlePubMedGoogle Scholar
- Kocher T, Zheng M, Bolli M, Simon R, Forster T, Schultz-Thater E, Remmel E, Noppen C, Schmid U, Ackermann D: Prognostic relevance of MAGE-A4 tumor antigen expression in transitional cell carcinoma of the urinary bladder: a tissue microarray study. Int J Cancer. 2002, 100: 702-705. 10.1002/ijc.10540.View ArticlePubMedGoogle Scholar
- Bolli M, Kocher T, Adamina M, Guller U, Dalquen P, Haas P, Mirlacher M, Gambazzi F, Harder F, Heberer M: Tissue microarray evaluation of Melanoma antigen E (MAGE) tumor-associated antigen expression: potential indications for specific immunotherapy and prognostic relevance in squamous cell lung carcinoma. Ann Surg. 2002, 236: 785-793. 10.1097/00000658-200212000-00011.PubMed CentralView ArticlePubMedGoogle Scholar
- Ascierto ML, De Giorgi V, Liu Q, Bedognetti D, Murtas D, Chouchane L, Wang E, Marincola FM: An immunologic portrait of cancer. J Transl Med. 2011, 9: 146-10.1186/1479-5876-9-146.PubMed CentralView ArticlePubMedGoogle Scholar
- Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, Tosolini M, Camus M, Berger A, Wind P: Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006, 313: 1960-1964. 10.1126/science.1129139.View ArticlePubMedGoogle Scholar
- Ascierto ML, Kmieciak M, Idowo MO, Manjili R, Zhao Y, Grimes M, Dumur C, Wang E, Ramakrishnan V, Wang X-Y: A signature of immune function genes associated with recurrence-free survival in breast cancer patients. Breast Cancer Res Treat. 2011,Google Scholar
- Wang E, Worschech A, Marincola FM: The immunologic constant of rejection. Trends Immunol. 2008, 29: 256-262. 10.1016/j.it.2008.03.002.View ArticlePubMedGoogle Scholar
- Wang E, Marincola FM: Immunologic signatures of rejection. 2010, New York, NY: SpringerGoogle Scholar
- Chen Z, O'Shea JJ: Regulation of IL-17 production in human lymphocytes. Cytokine. 2008, 41: 71-78. 10.1016/j.cyto.2007.09.009.View ArticlePubMedGoogle Scholar
- Little AS, Balmanno K, Sale MJ, Newman S, Dry JR, Hampson M, Edwards PA, Smith PD, Cook SJ: Amplification of the driving oncogene, KRAS or BRAF, underpins acquired resistance to MEK1/2 inhibitors in colorectal cancer cells. Sci Signal. 2011, 4: ra17-10.1126/scisignal.2001752.View ArticlePubMedGoogle Scholar
- Corcoran RB, as-Santagata D, Bergethon K, Iafrate AJ, Settleman J, Engelman JA: BRAF gene amplification can promote acquired resistance to MEK inhibitors in cancer cells harboring the BRAF V600E mutation. Sci Signal. 2010, 3: ra84-10.1126/scisignal.2001148.PubMed CentralView ArticlePubMedGoogle Scholar
- Dahl C, Guldberg P: The genome and epigenome of malignant melanoma. APMIS. 2007, 115: 1161-1176. 10.1111/j.1600-0463.2007.apm_855.xml.x.View ArticlePubMedGoogle Scholar
- Van Der BP, Zhang Y, Chaux P, Stroobant V, Panichelli C, Schultz ES, Chapiro J, van den Eynde BJ, Brasseur F, Boon T: Tumor-specific shared antigenic peptides recognized by human T cells. Immunol Rev. 2002, 188: 51-64. 10.1034/j.1600-065X.2002.18806.x.View ArticleGoogle Scholar
- Kawakami Y, Rosenberg SA: Immunobiology of human melanoma antigens MART-1 and gp100 and their use for immuno-gene therapy. Int Rev Immunol. 1997, 14: 173-192. 10.3109/08830189709116851.View ArticlePubMedGoogle Scholar
- Wang E, Lichtenfels R, Bukur J, Ngalame Y, Panelli MC, Seliger B, Marincola FM: Ontogeny and oncogenesis balance the transcriptional profile of renal cell cancer. Cancer Res. 2004, 64: 7279-7287. 10.1158/0008-5472.CAN-04-1597.View ArticlePubMedGoogle Scholar
- Pages F, Berger A, Camus M, Sanchez-Cabo F, Costes A, Molidor R, Mlecnik B, Kirilovsky A, Nilsson M, Damotte D: Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med. 2005, 353: 2654-2666. 10.1056/NEJMoa051424.View ArticlePubMedGoogle Scholar
- Simon R, Lam A, LI MC, Ngan M, Menenzes S, Zhao Y: Analysis of gene expression data using BRB-Array tools. Cancer Inform. 2007, 3: 11-17.PubMed CentralPubMedGoogle Scholar
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.