Melanoma progression is a multi-step process involving an incompletely characterized sequence of generic events affecting multiple biological processes, including control of the cell cycle, apoptosis, enzymatic hydrolysis of the extracellular matrix, local immunosuppression and mechanisms of immunological escape, angiogenesis, and cell migration. In addition, there may be some similarities in biological mechanisms implicated in melanoma progression and those that play a role in development of drug resistance in malignant melanoma. For example, proteins involved in chemoresistance of malignant melanoma cells, such as elongation factor 1-delta, translationally controlled tumor protein, 60 kDa heat shock protein, and nucleophosmin [23
] were differentially expressed in metastatic melanoma.
In this study, we used genetically, very-closely-related cell lines with differing metastatic potential to minimize incidental genetic noise, i.e., incidental protein abundance changes unrelated to tumor progression resulting from impaired DNA repair and variations in chromosomal aberrations that affect gene expression. This goal appears to have been achieved, because when we integrated observed protein changes into cellular networks using the knowledge-based Ingenuity Pathway Analysis tools, the vast majority of identified proteins (97/110) mapped to a total of six, highly significant cellular networks. Importantly, the top functions associated with these six networks closely correlated with known important cancer progression mechanisms. This strongly suggests that a majority of the observed protein abundance changes are likely to contribute directly to the metastatic phenotype. Furthermore, there was a clear delineation between these six high-scoring networks and the next, best-scoring network, which had a score less than three (not significant). The fact that most observed protein changes map to only six highly significant cellular networks with no marginally significant networks is another indication that the greater depth of analysis for our 3-D protein profiling method and the genetically similar cell lines have resulted in identification of primarily those proteins directly implicated in melanoma progression. The highly comprehensive analysis of the current study is reflected further in the fact that approximately half of the proteins in the top networks were shown to change with metastatic potential in this study. In addition, these networks defined additional proteins that were not previously implicated in cancer metastasis.
The Ingenuity Pathway Analysis resource provides a useful method of integrating complex datasets discovered using global genomic or proteomic approaches, as illustrated above. Of course, an inherent weakness of this software and associated Knowledge database is its limitation to those proteins with assigned annotations or published relationships and, as a result, some annotations ultimately may prove to be inaccurate or incomplete. In addition, these networks do not directly define how the changes in levels of specific proteins will affect overall function. In this regard, it should be noted that in all identified networks, some proteins were elevated in metastatic cells while others showed reduced levels. Such opposite changes in a network are expected, as some proteins inhibit a pathway and others activate that function. Similarly, in a network, the stimulatory and inhibitory affects of different components are even more complex. Furthermore, as shown in , Western blots of total protein levels usually correlate with changes observed on 2-D gels, but there are exceptions such as IL-1β, which showed a substantial increase in the metastatic cells by Western blot (+5.7-fold), but a two-fold decrease for a 2-D gel spot identified as IL-1β (Supplemental Table 2
). The most likely explanation for this discrepancy is that the 2-D gel analysis probably identified a minor form of IL-1β as suggested by the observed higher than expected molecular weight (Supplemental Table 2
). In addition, the change in the major form of the protein indicated by the Western blot may have been missed by the 3-D DIGE method due to interference from a more abundant protein or other 2-D gel artifact. This illustrates that while 2-D gels are superior to most alternative proteomics methods in identifying some protein modifications, it often is ambiguous as to whether an observed change reflects the overall abundance of that protein or only a minor form of the protein. Furthermore, although the 3-D DIGE method detects far more proteins than conventional 2-D gels, some proteins such as membrane proteins and proteins larger than 100 kDa, are likely to be systematically missed because these groups of proteins are not well recovered from 2-D gels. Also, some relatively low-abundance proteins such as the MAGE proteins may be obscured by other more abundant proteins.
Despite the above caveats, examination of the identified networks shows that most of the results from the current study are both consistent with prior knowledge of tumor progression and, at the same time, substantially expand our knowledge. For example, in Network 1 (), interleukin-1β (IL-1β), which plays a critical role in melanoma progression [25
], is a central node and directly interacts with 12 other proteins in the network. IL-1, a family of two major agonistic proteins, IL-1α and IL-1β, is pleiotropic and affects mainly inflammation, immunity, and hemopoiesis. IL-1 is abundant at tumor sites, where it may affect the process of carcinogenesis, tumor growth and invasiveness, and also the patterns of tumor-host interactions. It has been proposed that membrane-associated IL-1α expressed on malignant cells stimulates anti-tumor immunity, while secretable IL-1β, derived from the microenvironment or the malignant cells, activates inflammation that promotes invasiveness and also induces tumor-mediated suppression [26
]. Consistent with this role, our Western blot analysis showed that the intracellular level of IL-1β is elevated in the metastatic cells, although it remains to be determined whether this results in increased levels of secreted IL-1β. The differential expression of IL-1β in melanoma cells may interact with JNK and influence its downstream targets as shown in . JNK, which is among the major subgroups of mitogen-activated protein kinase (MAPK), is activated primarily by inflammatory cytokines and environmental stress [27
]. A role for the JNK pathway in tumorigenesis is supported by the high levels of JNK activity found in several cancer cell lines [28
] and we confirmed using Western blots that JNK is indeed elevated in the metastatic cells in this study. A further example of a role for JNK in tumorigenesis has been reported in the liver, where JNK was shown to promote chemically induced hepatocarcinogenesis [29
In Network 2, Myc represents a central biological theme of cancer as well as a central node of this network. Its ability to activate or repress, either directly or indirectly, core genes in the other networks demonstrates that Myc can regulate multiple subsets of genes to elicit specific regulatory programs. Myc plays an important role in regulating cell cycle, cell growth, differentiation, apoptosis, transformation, genomic instability, and angiogenesis, presumably through its ability to activate or repress transcription of target genes that mediate these various processes [30
]. Amplification of myc
genes has been found in a variety of tumor types including lung (c-myc
), colon (c-myc
), breast (c-myc
), and neuroblastoma (N-myc
]. In melanoma, high c-myc
expression has been found to be associated significantly with vertical growth phase, poor prognosis, and metastases [31
]. However, our Western blots indicate that the increase in C-Myc is marginal (). More interestingly, N-Myc (designated MYCN in Network 3, see Supplemental Figure 1
) amplification is associated with a variety of tumors, most notably neuroblastomas. Many differentially expressed proteins that are directly connected to the oncogene are translation factors and ribosomal proteins. The ribosomal proteins, including 40S ribosomal protein S12 and 60S acidic ribosomal protein P2, were increased in metastatic cells, which is consistent with previous results from gene expression profile analysis [32
]. Furthermore, proteins involved in translation, such as the eukaryotic translation initiation factor 5A and eukaryotic translation elongation factor 1 alpha and delta, were increased in metastatic melanoma, although eukaryotic initiation factor 4A-I was significantly decreased. These genes have been shown to respond to the N-myc
oncogene, suggesting that MYCN functions as a major regulator of protein synthesis [33
]. The possible involvement of the MYCN proteins in the etiology of common human cancers has raised exciting questions and is the subject of intense investigation by multiple laboratories. The network analyses performed here suggest a common mechanism involving regulatory networks may be involved in human cancers. Of possible significance is the fact that both neuroblastoma and melanoma tumors are derived from the same neural crest progenitor cells [34
]. The molecular dissection of how MYCN is involved in human melanoma is an exciting and challenging endeavor that should lead to a better understanding of melanoma metastasis and, perhaps, tumorigenesis.
As expected, proteins associated with tumor invasion and metastasis, including proteases and protease inhibitors, were more likely to be elevated than decreased in metastatic melanoma cells. Specifically, we found cathepsin B and D to be increased in metastatic melanoma by both the 3-D DIGE and Western blot analyses. Both proteins were apparently the processed mature forms when compared with observed and theoretical molecular masses (Supplemental Table 1
: F3_40 for cathepsin B and F3_39 for cathepsin D). Specific cathepsin proteins, including B and D, have been analyzed extensively and implicated in various cancers [35
]. Consistent with the observed increased levels of these proteases in the metastatic cells, multiple protease inhibitors exhibited decreases, including plasminogen activator inhibitor-2; serpin peptidase inhibitor), clade B (ovalbumin), member 2 (SERPINB2); serine (or cysteine) proteinase inhibitor; serpin peptidase inhibitor, clade H (heat shock protein 47), member 1; and SERPINH1. In particular, the urokinase plasminogen activator (uPA) system, including the serine protease (uPA), 2 serpin inhibitors (PAI-1 and PAI-2), and the membrane-linked receptor (uPAR), plays a key role in cancer invasion and metastasis [39
]. In addition to mediating invasion and metastasis as a result of extracellular matrix dissolution, uPA has been shown to enhance cell proliferation and migration and to modulate cell adhesion. Elevated PAI-2 has been shown to prevent invasion and metastasis of various cancer cells including melanoma [40
]. Thus, the observed decreased level of this and other proteinase inhibitors should enhance the proteolytic processes that promote tumor invasion and metastasis. The degradation of basement membranes by tumor cells involves secretion and activation of these proteinases and results from an imbalance between their inhibitors and activators, which are controlled by various growth factors or cytokines. Among them, the transforming growth factor-β (TGF-β) family, centered mainly on Network 4, is one of the most intriguing because it has been reported either to decrease or promote cancer progression. Thus, TGF-β1 triggered a large decrease of uPA and tPA, as well as a decrease of uPA and uPAR mRNAs, and also induced a strong increase of PAI-1 synthesis [42
]. TGF-β1 may inhibit melanoma tumor growth by specifically decreasing plasmin activity of tumor cells and play a protective role during the earliest stages of tumor progression.
On the other hand, proteins associated with apoptosis and tumor suppressors were decreased in metastatic melanoma (). Although tumor development involves many other processes, in almost all instances, deregulated cell proliferation and suppressed cell death provide the underlying platform for tumor progression. In most cancers, this ability to survive results in part from inhibition of the p53 pathway, either by activating mutations in p53 itself, perturbation of the signaling pathways that allow activation of p53 in response to stress, or defects in the downstream mediators of p53-induced apoptosis [43
]. In this study, we observed a decrease in metastatic melanoma of NAD(P)H dehydrogenase 1 (NQO1), which destabilizes p53 and contributes to increased tumorigenesis of melanoma [44
Further experiments will be required to unravel the most critical protein changes associated with alterative pathway preferences leading to invasion and metastasis in melanoma and to identify any other discrepancies between apparent changes in 2-D gel spots and the overall abundance of a given protein, analogous to IL-1β. Due to the limited availability of specific antibodies, as well as the limited quantitative accuracy of Western blots and their limited throughput, the next logical step is to use targeted multiple reaction monitoring mass spectrometry to quantitatively assess all proteins in the top-scoring networks defined in this study to further explore the relationships between these networks and the metastatic phenotype.