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domains of BP230, most frequently with C-terminal domain, by immunoblot analyses using bacterial recombinant proteins covering the entire molecule. Exp Dermatol 10:256–63
Clinical Implications • Epitope spreading is the sequential development of new antibodies against seemingly less accessible regions of target proteins in autoimmunity. • The identification of mechanisms of epitope spreading in the immunobullous diseases may lead to novel therapies that limit the process of spreading. • Because of accessibility, the analysis of epitope spreading in skin disease may provide insight into pathogenic mechanisms in systemic autoimmune diseases and transplantation immunity.
Perspectives
Although Di Zenzo et al. (2010) demonstrated convincingly that intramolecular epitope spreading occurs in BPAG2, many questions remain. The first is why patients with bullous pemphigoid preferentially develop IgG autoantibodies to epitopes on the NC16a domain of BPAG2. Second, why do autoantibodies in bullous pemphigoid react with epitopes in the NC16a domain of BPAG2, whereas autoantibodies in anti-BP180–type mucous membrane pemphigoid react with epitopes in the C-terminal domain? More important, how do the antibodies directed against these distinct domains of BPAG2 result in different clinical features (i.e., large, tense skin blisters in bullous pemphigoid and predominant erosive mucosal lesions in anti-BP180-type mucous membrane pemphigoid)? Why do IgA antibodies in lamina lucida–type linear IgA bullous dermatosis react with specific epitopes in 120- and 97-kDa linear IgA bullous dermatosis (LAD)-1 antigens produced from 180-kDa intact BPAG2 by proteolytic processing (Nie et al., 2000)? Future studies should unravel the mechanisms by which the hidden epitope in intact 180-kDa mol ecule (intact BPAG2) is exposed in linear IgA bullous dermatosis to autoantibodies against the 120- and 97-kDa LAD-1 antigens. Finally, and perhaps most important, we do not know yet why the development of antibodies against ICD epitopes in human BPAG2 correlated with skingraft loss. The relevance of this phenomenon to autoimmune bullous diseases remains to be determined.
References
Chan LS, Vanderlugt CJ, Cooper KD et al. (1998) Epitope spreading: lessons from autoimmune skin diseases. J Invest Dermatol 110:103–9 Chan PT, Ohyama B, Nishifuji K et al. Immune response towards amino-terminus of desmoglein 1 prevails across different activity stages in nonendemic pemphigus foliaceus. Br J Dermatol (in press) Di Zenzo G, Calabresi V, Olasz EB et al. (2010) Sequential intramolecular epitope spreading of humoral responses to human BPAG2 in a transgenic model. J Invest Dermatol 130:1040–7 Futei Y, Amagai M, Hashimoto T et al. (2003) Conformational epitope mapping and IgG subclass distribution of desmoglein 3 in paraneoplastic pemphigus. J Am Acad Dermatol 49:1023–8 Hamada T, Nagata Y, Tomita M et al. (2001) Bullous pemphigoid sera react specifically with various
Ishii N, Yoshida M, Hisamatsu Y et al. (2004) Epidermolysis bullosa acquisita sera react with distinct epitopes on the NC1 and NC2 domains of type VII collagen: study using immunoblotting of domain-specific recombinant proteins and postembedding immunoelectron microscopy. Br J Dermatol 150:843–51 Matsumura K, Amagai M, Nishikawa T et al. (1996) The majority of bullous pemphigoid and herpes gestationis serum samples react with NC16a domain of the 180-kDa bullous pemphigoid antigen. Arch Dermatol Res 288:507–9 Nagata Y, Karashima T, Watt FM et al. (2001) Paraneoplastic pemphigus sera react strongly with multiple epitopes on the various regions of envoplakin and periplakin, except for C-terminal homologous domain of periplakin. J Invest Dermatol 116:556–63 Nie Z, Hashimoto T (1999) IgA antibodies of cicatricial pemphigoid sera specifically react with C-terminus of BP180. J Invest Dermatol 112:254–5 Nie Z, Nagata Y, Joubeh S et al. (2000) IgA antibodies of linear IgA bullous dermatosis recognize the 15th collagenous domain of BP180. J Invest Dermatol 115:1164–6 Ohata Y, Amagai M, Ishii K et al. (2001) Immunoreactivity against intracellular domains of desmogleins in pemphigus. J Dermatol Sci 25:64–71
See related article on pg 1126
More or Less: Copy Number Alterations in Mycosis Fungoides William M. Lin1 and Michael Girardi1 Mycosis fungoides (MF) is the most common form of cutaneous T-cell lymphoma (CTCL), a heterogeneous group of non-Hodgkin’s lymphomas of skin-homing T cells. MF may vary from limited patchy skin disease to extensive cutaneous plaque and tumor involvement to extracutaneous compartments of blood, lymph nodes, and viscera. Advances in genomic technologies have enabled the increasing characterization of genetic alterations in this malignancy; using this technology, investigators hope to understand MF’s variable behavior and pathogenesis. In this issue, Salgado et al. identify regions of genomic DNA alterations from 41 MF samples and report associations with prognosis. Journal of Investigative Dermatology (2010) 130, 926–928. doi:10.1038/jid.2009.370
In recognition that cancer is fundamentally dependent on genetic
alterations (Vogelstein and Kinzler, 2004), the number of genomic
Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, USA
1
CONFLICT OF INTEREST
The authors state no conflict of interest.
Correspondence: Michael Girardi, Department of Dermatology, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06520, USA. E-mail:
[email protected]
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cutaneous T-cell lymphoma (CTCL) studies has rapidly increased, in the hope of shedding light on its poorly understood genetic basis (Karenko et al., 2007). Knowledge of the critical chromosomal abnormalities and genetic mutations involved in mycosis fungoides (MF) may provide insight into its pathogenesis, and a finer molecular classification may lead to the identification of novel therapeutic targets. Advances in genomic techno logy have facilitated CTCL characterization, and the resulting studies have begun to identify some of the major genes that may be involved in CTCL development and progression (Mao et al., 2002; Fischer et al., 2004; Prochazkova et al., 2007; Carbone et al., 2008; van Doorn et al., 2009; Vermeer et al., 2009). The study by Salgado and colleagues (2010, this issue) enhances our understanding of MF at the genomic level. First, the investigators identified regions of DNA copy number gains and losses in 41 skin biopsies of tumor–stage MF with Agilent 60-mer 44K oligonucleotide array comparative genomic hybridization (“array CGH”). Second, they used the number of DNA alterations, DNA breaks, and homozygous deletions/high-level amplifications as an indicator of genomic instability and observed decreased survival in patients with high genomic instability. Third, they determined three specific regions of DNA alteration—9p21.3, 8q24.21, and 10q26qter—as genetic modifiers of prognosis. Finally, and perhaps most important, they have submitted to the field the largest and highest-resolution data set for MF to date. Copy number landscape
Comparative genomic hybridization (CGH) enables the assessment of genomic changes in DNA copy number by comparing the degree of hybridization of a labeled probe in cancerous tissues against normal samples. Advances on this basic concept such as array CGH have allowed a markedly increased resolution in DNA copy number. Thus, generating CGH copy number data from tumor
Clinical Implications • Interpreting the role of acquired genetic mutations requires a full clinical characterization of patients with CTCL, a task for astute clinicians. • Instability in the integrity of DNA is an important characteristic of the malignant cells in CTCL. • We are reaching the point at which the identification of genetic mutations in malignant T cells may predict the outcome of patients with CTCL.
samples provides a whole-genome view of a cancer while allowing the focused identification of commonly amplified or deleted regions harboring potential oncogenes or tumor suppressors, respectively. Because large regions of chromosomes can often be gained or lost in a particular sample, it is often through increased sample size that a “minimal common region” of overlap is defined, thereby narrowing a region to its true gene target. Finer resolution of genomic data is also important, and increases in genomic coverage have improved detection from chromosomal or arm-level gains and losses down to cytoband changes. Together with improved algorithms, these analyses may allow investigators to narrow regions of genetic alteration down to single genes. The clinical heterogeneity of CTCL patients and the relative infrequency of the disease have often been cited as impediments to conducting genomic studies in this malignancy (Karenko et al., 2007). These factors, compounded by challenges in isolating and culturing tumor cells, have slowed investigators’ ability to define recurrent mutations that may be critical in CTCL onco genesis relative to other hematological malignancies or melanoma (Lin et al., 2008). The data set of Salgado et al. (2010) represents an admirably large group of similar patient samples. By comparing these data with those of other published reports, consensus minimal common regions of DNA copy gain and loss in MF can be elucidated, although even larger and higher-resolution studies will be necessary to further focus analyses and define the primary genes in CTCL.
Genetic modifiers of prognosis
Much of the difficulty in translating genomic data to biologic insight lies in the lack of sufficient phenotypic data, and it is thus informative that Salgado et al. (2010) collected survival data. Together, DNA changes that correlate with decreased survival suggest that the genes within a given region are, at least, associated with oncogenesis, but they may also be implicated in disease progression. Salgado et al. identify three regions associated with a poorer prognosis. van Doorn et al. (2009) reported that the deletion of 9p21 predicted decreased survival, and Salgado et al. narrow this region to 9p21.3, which includes the known tumor suppressor genes CDKN2A, CDKN2B, and MTAP. Given past reports of CDKN2A being hypermethylated (Navas et al., 2002), the current CGH data support the notion that CDKN2A functions as a tumor suppressor in MF. Salgado and colleagues similarly confirm the association of 8q amplification with poorer prognosis, as first reported by Fischer et al. (2004). Instead of 8q24.3, as reported by van Doorn et al. (2009), however, Salgado et al. found that gain of 8q24.21, which includes the oncogene MYC, correlates with decreased survival. With a high-level amplification of the region including MYC in two patients (Salgado et al., 2010) and an independent report that MYC is also one of the most commonly amplified genes in Sézary syndrome (Vermeer et al., 2008), these findings lend support to the importance of MYC and suggest its primary oncogenic role in CTCL progression. www.jidonline.org 927
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Finally, Salgado et al. identify the loss of 10q26qter, which narrows the region from 10q, as reported by Fischer et al. (2004). Included within this deletion are two tumor suppressors, MGMT and EBF3, nominated by Salgado et al. as candidates. Particularly with reports of MGMT methylation-mediated inactivation in a subset of CTCL (Gallardo et al., 2004), further studies must validate and dissect its function in MF. Additional genetic markers reported to decrease prognosis include gain of 1q21–1q22 (van Doorn et al., 2009). Also, Fischer et al. (2004) determined that the loss of 6q and loss of 13q correlated with decreased survival; however, it is important to note that other CTCL subtypes were included in that study. Although meta-analyses of combined genomic and survival data will help confirm these findings, along with the addition of new cohorts, currently, loss of 9p21.3, gain of 8q24.21, and loss of 10q26qter appear to be the three primary loci that predict poor survival in MF. The likely roles of CDKN2A, CDKN2B, MTAP, MYC, and MGMT in MF merit further study, and functional validation will better delineate their importance in this malignancy. Genomic instability
Genomic instability is typically divided into two classes: chromosomal instability and microsatellite instability (Aguilera and Gomez-Gonzalez, 2008). In assessing genomic instability, Salgado et al. (2010) define two groups of samples: high genomic instability and low genomic instability, based on: (i) number of DNA copy number changes (5), (ii) the presence of high-level copy number changes (presence vs. absence), and (iii) DNA breaks (10). The observation that patients with more DNA copy number changes (a gross marker of chromosomal instability) have poorer survival confirms a similar analysis done by Fischer et al. (2004) and raises the following questions: What are the regulators of genomic stability in CTCL and how might they be maintained? Moreover, is genomic instability an early or late genetic event? The authors note a correlation
between genomic instability and gain of 7q, yet it is unclear whether this is a cause or a consequence. Focused genomic analyses, particularly with other types of mutation data such as gene expression, of known regulator genes of genomic stability (compiled by Aguilera and Gomez-Gonzalez, 2008), coupled with functional studies, may help address these questions. Future directions
Evolutionary selection pressures in tumors manifest themselves in many mutations not limited to DNA copy number gains/losses, loss-ofheterozygosity, miRNA-related gene expression changes, epigenetic modifications, translocations, and point mutations. In this issue, Salgado et al. contribute to our understanding of DNA copy number changes in MF, narrowing in on and confirming many suspected gene targets. However, going forward, analyzing one type of mutation in the setting of multiple levels of cellular dysregulation impairs our ability to comprehend the full picture. Although we are still defining the key genes in CTCL, its primary biological pathways and how these mutations interact with one another to promote carcinogenesis remain relatively unexplored. These efforts will likely rely on modeling many kinds of mutations—because if a gene is critical for pathogenesis, a cancer cell will probably find some way to mutate it. Equally important to future analyses will be the collection of full clinical data on these patients. Together with functional experimentation, genomics integrated with statistical algorithms should enable the discovery of new therapeutic targets while elucidating the mechanisms that underlie transformation. Furthermore, an understanding of the genetic basis of CTCL may help explain its immunomodulatory nature. This is indeed an exciting time in CTCL genetics, with tremendous potential for a better understanding of T-cell malignancies and improved clinical care. CONFLICT OF INTEREST
WML has received funding from Affymetrix, Inc., under the Collaborations in Cancer Research Program.
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ACKNOWLEDGMENTS The authors acknowledge support by the Skin Cancer Foundation, American Skin Association, Yale School of Medicine Fellowship/Etta S. Chidsey Award, Affymetrix, Inc., and the Yale Comprehensive Cancer Center/Hull Fund.
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