Success with molecular-based targeted drugs in the treatment of cancer has ignited extensive research efforts within the field of personalized therapeutics. However, successful application of such therapies is dependent on the presence or absence of mutations within the patient's tumor that can confer clinical efficacy or drug resistance. Building on these findings, we developed a high-throughput mutation panel for the identification of frequently occurring and clinically relevant mutations in melanoma. An extensive literature search and interrogation of the Catalogue of Somatic Mutations in Cancer database identified more than 1,000 melanoma mutations. Applying a filtering strategy to focus on mutations amenable to the development of targeted drugs, we initially screened 120 known mutations in 271 samples using the Sequenom MassARRAY system. A total of 252 mutations were detected in 17 genes, the highest frequency occurred in BRAF (n = 154, 57%), NRAS (n = 55, 20%), CDK4 (n = 8, 3%), PTK2B (n = 7, 2.5%), and ERBB4 (n = 5, 2%). Based on this initial discovery screen, a total of 46 assays interrogating 39 mutations in 20 genes were designed to develop a melanoma-specific panel. These assays were distributed in multiplexes over 8 wells using strict assay design parameters optimized for sensitive mutation detection. The final melanoma-specific mutation panel is a cost effective, sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular-based therapeutics for the treatment of melanoma. When used in a clinical research setting, the panel may rapidly and accurately identify potentially effective treatment strategies using novel or existing molecularly targeted drugs. Mol Cancer Ther; 11(4); 888–97. ©2012 AACR.

This article is featured in Highlights of This Issue, p. 793

Melanoma is a highly aggressive malignancy and accounts for the majority of all skin cancer-related deaths (1). The high mortality rate for advanced stage metastatic melanoma is largely due to the ineffectiveness of currently approved systemic treatment strategies (2). For example, use of traditional chemotherapeutic approaches has shown poor response rates; less than 10% of patients demonstrate a clinical response to standard treatment with dacarbazine and less than 5% patients survive beyond 5 years (3). Recently, novel effective molecular-targeted therapies for the treatment of metastatic melanoma have emerged.

One class of molecular-based drugs of current interest for treating metastatic melanoma is RAF inhibitors. RAF mutations represent highly desirable targets because approximately 60% of melanoma patients have constitutively active mitogen-activated protein kinase (MAPK) signaling due to mutations in BRAF (4). Although initial investigations using the RAF inhibitor sorafenib showed no significant increase in survival compared with chemotherapy (5), the recent development of more selective BRAF inhibitors has reignited research into this class of drug (6). Recently, a phase 3 randomized clinical trial of PLX4032 (vemurafenib) showed improved rates of overall and progression-free survival in patients with BRAF V600E mutations when compared with dacarbazine (7).

Interestingly, the use of PLX4032 in tumors not harboring the V600E mutation has shown adverse biological end points including increased cell division and proliferation both in vitro and in vivo (8–10). Furthermore, mutations occurring in MAPK kinase (MEK), a downstream effector of BRAF in the MAPK signaling pathway, have been shown to confer resistance to PLX4032 treatment (11, 12). In addition, durable responses have yet to be achieved with PLX4032, suggesting that a number of mechanisms can lead to acquired drug resistance (11–15). Thus, screening for multiple mutations will be necessary for the development of effective treatment strategies.

Targeted approaches treating melanoma patients harboring aberrations other than BRAF mutations are also being investigated. One approach was highlighted in a phase II trial of imatinib mesylate in which a clinical response was observed in a melanoma patient whose tumor harbored a KIT mutation (16). A number of case reports have recently highlighted the efficacy of imatinib treatment in KIT-mutated melanoma patients, and more recently, significant clinical responses in a subset of patients in a phase II clinical trial (17, 18).

Due to the recent successes of a number of targeted drugs in the treatment of metastatic melanoma, we sought to develop a melanoma-specific mutation panel to facilitate rapid identification of somatic mutation events relevant to targeted treatments. An extensive literature search was conducted, including interrogation of the Catalogue of Somatic Mutations in Cancer (COSMIC) database, to identify oncogenic mutations in melanoma. A confirmation panel of 120 highly ranked mutations was screened for prevalence in 271 melanomas before a final melanoma-specific mutation panel of 39 amino acid alterations was derived. Mutations selected for the final panel were “clinically relevant” in regards to having either documented evidence or potential clinical significance for targeted therapeutics.

Cell line and tumor samples

The first cohort of samples consisted of 61 cutaneous or nodal melanoma metastases previously described (19, 20) as well as a panel of 43 stage III (American Joint Committee on Cancer) melanoma early passage cell lines established at the Queensland Institute of Medical Research (unpublished data). In addition to this, 16 matched tumor samples were included to assess the similarity of mutation profiles with the associated cell line. Eleven previously described uveal melanoma cell lines (provided by Dr. Jerry Nierderkorn, UT Southwestern Medical Centre, Dallas, Texas), were included to identify mutations in this subtype of melanoma (21). A second cohort comprised 98 fresh-frozen stage III and IV metastases, from the Melanoma Institute Australia (22), and 30 melanoma cell lines (of which 9 represented additional clones) established as previously described (23). Cell lines with corresponding matched tumors were profiled to confirm authenticity using an AmpFISTR Profiler Plus PCR amplification kit (Applied Biosystems) and analyzed on a 3100 Genetic Analyzer (Applied Biosystems).

Cell culture and DNA extraction

The panel of 43 stage III melanoma cell lines was cultured in filter-sterilized RPMI1640 supplemented with 10% heat inactivated fetal calf serum (55°C for 30 minutes), 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C in 5% CO2. Genomic DNA was extracted by QIAGEN QIAamp Blood Maxi Kits according to the manufacturer's instructions (QIAGEN Pty Ltd). Tumor DNA was extracted from 20 to 30 mg of fresh-frozen tumor using QIAamp DNA Mini Kits (QIAGEN) with on-column RNAse digestion. Briefly, tissue was pulverized in liquid nitrogen then incubated with ATL buffer (QIAGEN) and proteinase K for 96 hours at 56°C.

Mutation detection

Mutation detection was carried out with the Sequenom MassARRAY system following standard protocols (Sequenom). In brief, 20 ng of genomic DNA were used in a PCR reaction and cleaned postamplification with shrimp alkaline phosphatase. A single base pair extension reaction using iPLEX Pro chemistry was carried out, resin treated to remove contaminants and spotted onto SpectroCHIP II arrays. Mutant and wild-type alleles were then discriminated via mass spectrometry using the Sequenom MassARRAY Analyser 4 platform. Mutations were detected by a minimum 10% threshold of the mutant allele peak and were all manually reviewed. The panel of stage III melanoma cell lines was also analyzed using the Sequenom OncoCarta v1.0 panel, which consists of 238 mutations covering 17 oncogenes (24).

Assay design parameters

As the confirmation panel's purpose was to identify frequently occurring mutation events from a large list of mutations, standard iplex parameters (Assay designer 4.0, Sequenom) were used with a maximum multiplexing level of 24 assays per well for cost-effective, high-throughput screening. The final melanoma-specific mutation panel, in contrast, used strict assay design parameters optimized for sensitive mutant allele peak detection. A maximum multiplexing level of 12 assays per well with increased penalty scores for hairpin, self-dimerization, heterodimerization, and false priming of the extension primer were applied. In addition, if possible, mutant allele peaks were designed as the first detected allele peak of an assay to reduce potential noise from salt adducts. Further details of the confirmation panel are provided in Supplementary Table S1. The final melanoma-specific mutation panel (MelaCARTA v1) is available from Sequenom Inc.

Statistical analysis

To assess the accuracy and sensitivity of the final melanoma-specific mutation panel compared with the OncoCarta panel, the mutant allele frequencies obtained with each panel were compared. A 2-tailed paired Student t test was used to determine whether any differences were statistically significant.

Mutation detection with the OncoCarta mutation panel v1.0

To identify frequently occurring mutations to be included in the melanoma-specific mutation panel, 43 stage III melanoma cell lines were screened with the Sequenom OncoCarta panel. Mutations were detected in 80% of cell lines (34 of 43). BRAF and NRAS mutations were mutually exclusive, with mutations occurring in approximately 60% (25 of 43) and 20% (9 of 43) of cell lines, respectively. Four BRAF-mutated samples had an additional mutation in CDK4, PIK3CA, or MET. Table 1 lists mutations and allelic ratios in the stage III melanomas. Ten mutations in 5 genes from the OncoCarta panel were included in the final melanoma-specific mutation panel, thus eliminating excessive and potentially unnecessary genotyping not relevant for melanoma.

Table 1.

Comparison between mutations identified with the OncoCarta and melanoma-specific mutation panels

OncoCartaMelanoma specific
SampleGeneMutationWT%Mut%WT%Mut%
C001 NRAS Q61K 0.467 0.533 0.316 0.684 
C002 NRAS Q61K 0.259 0.741 0.000 1.000 
C004 BRAF V600E 0.528 0.472 0.540 0.460 
C006 NRAS Q61L 0.000 1.000 
C011 BRAF V600E 0.444 0.556 0.436 0.564 
C012 BRAF V600E 0.739 0.261 0.726 0.274 
C013 NRAS Q61L 0.000 1.000 
C017 BRAF V600E 0.449 0.551 0.459 0.541 
C021 — — — — — — 
C022 — — — — — — 
C025 — — — — — — 
C027 NRAS Q61K 0.349 0.651 0.156 0.844 
C028 BRAF V600E 0.686 0.314 0.692 0.308 
C037 — — — — — — 
C038 BRAF V600E 0.673 0.327 0.666 0.334 
C042 BRAF V600E 0.541 0.459 0.546 0.454 
C044 BRAF V600E 0.671 0.329 0.664 0.336 
C045 BRAF V600E 0.553 0.447 0.532 0.468 
C052 — — — — — — 
C054 NRAS Q61K 0.593 0.407 0.463 0.537 
C057 BRAF V600E 0.727 0.273 0.735 0.265 
C057 CDK4 R24C 0.000 1.000 
C058 BRAF L597S 0.317 0.683 0.292 0.708 
C060 BRAF V600E 0.741 0.259 0.694 0.306 
C062 BRAF V600E 0.552 0.448 0.565 0.435 
C065 BRAF V600E 0.114 0.886 0.200 0.800 
C067 — — — — — — 
C071 BRAF V600E 0.688 0.312 0.691 0.309 
C071 CDK4 R24C 0.466 0.534 0.466 0.534 
C074 BRAF V600E 0.571 0.429 0.525 0.475 
C077 — — — — — — 
C078 BRAF V600E 0.842 0.158 0.844 0.156 
C081 BRAF V600K 0.513 0.487 0.605 0.395 
C083 NRAS Q61L 0.419 0.581 0.383 0.617 
C084 — — — — — — 
C086 — — — — — — 
C088 BRAF V600K 0.273 0.727 0.332 0.668 
C089 BRAF V600E 0.539 0.461 0.483 0.517 
C089 MET T1010I 0.467 0.533 0.508 0.492 
C091 BRAF V600E 0.592 0.408 0.585 0.415 
C094 BRAF V600E 0.574 0.426 0.537 0.463 
C096 NRAS Q61R 0.444 0.556 0.440 0.560 
C097 BRAF V600E 0.63 0.37 0.541 0.459 
C100 BRAF G469R 0.702 0.298 0.681 0.319 
C100 PIK3CA R88Q 0.447 0.553 0.460 0.540 
C106 NRAS Q61L 0.329 0.671 0.360 0.640 
C108 BRAF V600K 0.219 0.781 0.293 0.707 
OncoCartaMelanoma specific
SampleGeneMutationWT%Mut%WT%Mut%
C001 NRAS Q61K 0.467 0.533 0.316 0.684 
C002 NRAS Q61K 0.259 0.741 0.000 1.000 
C004 BRAF V600E 0.528 0.472 0.540 0.460 
C006 NRAS Q61L 0.000 1.000 
C011 BRAF V600E 0.444 0.556 0.436 0.564 
C012 BRAF V600E 0.739 0.261 0.726 0.274 
C013 NRAS Q61L 0.000 1.000 
C017 BRAF V600E 0.449 0.551 0.459 0.541 
C021 — — — — — — 
C022 — — — — — — 
C025 — — — — — — 
C027 NRAS Q61K 0.349 0.651 0.156 0.844 
C028 BRAF V600E 0.686 0.314 0.692 0.308 
C037 — — — — — — 
C038 BRAF V600E 0.673 0.327 0.666 0.334 
C042 BRAF V600E 0.541 0.459 0.546 0.454 
C044 BRAF V600E 0.671 0.329 0.664 0.336 
C045 BRAF V600E 0.553 0.447 0.532 0.468 
C052 — — — — — — 
C054 NRAS Q61K 0.593 0.407 0.463 0.537 
C057 BRAF V600E 0.727 0.273 0.735 0.265 
C057 CDK4 R24C 0.000 1.000 
C058 BRAF L597S 0.317 0.683 0.292 0.708 
C060 BRAF V600E 0.741 0.259 0.694 0.306 
C062 BRAF V600E 0.552 0.448 0.565 0.435 
C065 BRAF V600E 0.114 0.886 0.200 0.800 
C067 — — — — — — 
C071 BRAF V600E 0.688 0.312 0.691 0.309 
C071 CDK4 R24C 0.466 0.534 0.466 0.534 
C074 BRAF V600E 0.571 0.429 0.525 0.475 
C077 — — — — — — 
C078 BRAF V600E 0.842 0.158 0.844 0.156 
C081 BRAF V600K 0.513 0.487 0.605 0.395 
C083 NRAS Q61L 0.419 0.581 0.383 0.617 
C084 — — — — — — 
C086 — — — — — — 
C088 BRAF V600K 0.273 0.727 0.332 0.668 
C089 BRAF V600E 0.539 0.461 0.483 0.517 
C089 MET T1010I 0.467 0.533 0.508 0.492 
C091 BRAF V600E 0.592 0.408 0.585 0.415 
C094 BRAF V600E 0.574 0.426 0.537 0.463 
C096 NRAS Q61R 0.444 0.556 0.440 0.560 
C097 BRAF V600E 0.63 0.37 0.541 0.459 
C100 BRAF G469R 0.702 0.298 0.681 0.319 
C100 PIK3CA R88Q 0.447 0.553 0.460 0.540 
C106 NRAS Q61L 0.329 0.671 0.360 0.640 
C108 BRAF V600K 0.219 0.781 0.293 0.707 

NOTE. Dashes identify samples with no mutations.

Abbreviations: WT%, percentage wild type allele; Mut%, percentage mutant allele.

Mutations identified from the COSMIC database and detailed literature search

The COSMIC database is a comprehensive resource of somatic mutations occurring in cancer, curated from data in scientific literature, and large-scale tumor genomic resequencing efforts (25). Using this resource, a comprehensive list of mutations occurring in metastatic melanoma was compiled along with associated population mutation frequency data (Supplementary Table S2 lists mutations and references).

At the time of database accession, the majority of mutations in this list derived from a resequencing study of 518 kinases in 210 diverse cancers including 6 melanomas (26). Most mutations in these samples were found in MZ7-mel and CP66-mel (476 and 258 mutations, respectively, out of 900 mutations), which were deemed to have a “mutator phenotype” and were thus excluded from our analysis as most of the mutations are likely “passenger” events. After removing duplicate and synonymous changes, 87 mutations from the remaining 4 samples were included for consideration in the confirmation stage. An additional 76 documented mutations in COSMIC, not related to these 4 samples, were also considered.

For comprehensive coverage of mutations in melanoma, an extensive literature search was carried out with PubMed to identify mutations not included in COSMIC. Mutations in ERBB4, MITF, GNAQ, GNA11, MEK, and the MMP family were identified. The combination of approaches identified 314 mutations that were considered for inclusion in the confirmation phase (Supplementary Table S2).

Filtering strategy to reduce mutations screened in the confirmation panel

To reduce the number of mutations genotyped in the confirmation phase, mutations were ranked according to criteria based on technology platform capabilities, clinical significance, and translational value. High-priority mutations include those that occur frequently in the population, occur frequently at single amino acid positions, are relevant to targeted drugs currently available or in clinical development, or are responsible for tumorigenesis through the disruption of important biological pathways. For this reason, mutations that had a reported mutation frequency of more than 1%, occurred in genes with kinase domains, introduced charge changes in the amino acid sequence and that did not occur in genes mutated in a typical fashion associated with inactivation of tumor suppressor genes were preferentially selected. This strategy reduced the list from 314 mutations to 110 highly ranked mutations in 42 genes. Figure 1 shows a summary of the mutation filtering process. The confirmation panel was used to determine the significance of these mutations in a large number of melanoma cell lines and tumors.

Figure 1.

Filtering process for selecting mutations of the melanoma-specific panel. From a list of approximately 1,000 mutations, the panel was narrowed down to 39 mutations to allow for cost-effective, high-throughput screening. Mutations that occurred at high frequency and with clinical significance were prioritized for inclusion in the final melanoma-specific mutation panel.

Figure 1.

Filtering process for selecting mutations of the melanoma-specific panel. From a list of approximately 1,000 mutations, the panel was narrowed down to 39 mutations to allow for cost-effective, high-throughput screening. Mutations that occurred at high frequency and with clinical significance were prioritized for inclusion in the final melanoma-specific mutation panel.

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Mutations identified in the confirmation panel

The confirmation panel detected 247 mutations in 271 samples (Table 2). The highest frequency of mutation was in BRAF (n = 154 or 57%), NRAS (n = 55 or 20%), CDK4 (n = 8 or 3%), PTK2B (n = 7 or 2.5%), and ERBB4 (n = 5 or 2%). BRAF mutations were mutually exclusive to NRAS mutations and the majority occurred at codon 600, mostly resulting in a valine to glutamate substitution, that is, V600E (137 of 271 or 50.5%). The most frequently occurring NRAS mutation (52 of 271, 19%) was at amino acid 61 (2 × Q61H, 21 × Q61K, 9 × Q61L, and 20 × Q61R).

Table 2.

Summary of mutations detected with the melanoma-specific mutation panel

GENE# SamplesPercentage (%)
BRAF 154 56.82 
NRAS 55 20.29 
CDK4 2.95 
PTK2B 2.58 
ERBB4 1.84 
MET 1.48 
MEK 1.10 
GNAQ 1.10 
AKT3 0.74 
GNA11 0.74 
CTNNB1 0.74 
KRAS 0.74 
KIT 0.37 
NEK10 0.37 
PDGFRA 0.37 
PIK3CA 0.37 
EPHA10 0.37 
Wild type 48 17.71 
GENE# SamplesPercentage (%)
BRAF 154 56.82 
NRAS 55 20.29 
CDK4 2.95 
PTK2B 2.58 
ERBB4 1.84 
MET 1.48 
MEK 1.10 
GNAQ 1.10 
AKT3 0.74 
GNA11 0.74 
CTNNB1 0.74 
KRAS 0.74 
KIT 0.37 
NEK10 0.37 
PDGFRA 0.37 
PIK3CA 0.37 
EPHA10 0.37 
Wild type 48 17.71 

Twenty-seven samples had more than 1 mutation; the majority were a BRAF mutation in association with another mutation (23 of 27, 85.1%). Samples with ERBB4 mutations did not segregate with BRAF (2 of 5) or NRAS mutations (1 of 5). Interestingly, of the 3 samples with mutations in MEK, one (D28) also had a BRAF V600K mutation.

Eleven melanoma cell lines of uveal origin were screened. Consistent with previous reports (27, 28), mutations in GNAQ (3 of 11, 27%) and GNA11 (2 of 11, 18%) were mutually exclusive. With one exception, TB1, a cutaneous melanoma metastasis, mutations of GNAQ and GNA11 were identified only in tumors of uveal origin. Three uveal melanomas (27%) had mutations in BRAF, including cell line OCM1, which has been documented (29, 30). No mutations were detected in approximately 18% of all samples (48 of 271). A complete list of results is provided in Supplementary Table S3.

All mutations identified in the confirmation panel were included in the final version of the melanoma-specific mutation panel (Table 3). Forty-six assays interrogating 39 mutations in 20 genes were designed into 8 wells with strict assay design parameters optimized for sensitive mutation allele detection. The final version of the melanoma-specific mutation panel was retested on all samples and all mutations identified in the confirmation panel were reidentified.

Table 3.

Mutations included in final version of the melanoma-specific mutation panel (39 in total)

MutationMutation
Gene(AA)Gene(AA)
AKT3 E17K KIT K642E 
BRAF G466* KIT L576P 
BRAF G469* KIT V559A 
BRAF K601E KIT W557R 
BRAF L397* KRAS G12* 
BRAF V600* KRAS Q61* 
CDK4 R24C MEK P124L 
CDK4 R24H MEK Q56P 
CTNNB1 S45* MET R1170Q 
CXCR4 V160I MET T992I 
EPHA10 E124K NEK10 E379K 
EPHB6 G404S NRAS G12* 
EPHB6 R679Q NRAS G13* 
ERBB4 E452K NRAS Q61* 
ERBB4 R393W PDGFRA E996K 
GNA11 Q209* PIK3CA R88Q 
GNA11 R183C PTK2B R429C 
GNAQ Q209* PTK2B R918Q 
GNAQ R183Q ROR2 A793S 
KIT D820Y   
MutationMutation
Gene(AA)Gene(AA)
AKT3 E17K KIT K642E 
BRAF G466* KIT L576P 
BRAF G469* KIT V559A 
BRAF K601E KIT W557R 
BRAF L397* KRAS G12* 
BRAF V600* KRAS Q61* 
CDK4 R24C MEK P124L 
CDK4 R24H MEK Q56P 
CTNNB1 S45* MET R1170Q 
CXCR4 V160I MET T992I 
EPHA10 E124K NEK10 E379K 
EPHB6 G404S NRAS G12* 
EPHB6 R679Q NRAS G13* 
ERBB4 E452K NRAS Q61* 
ERBB4 R393W PDGFRA E996K 
GNA11 Q209* PIK3CA R88Q 
GNA11 R183C PTK2B R429C 
GNAQ Q209* PTK2B R918Q 
GNAQ R183Q ROR2 A793S 
KIT D820Y   

NOTE: Asterisks indicate multiple amino acid changes detected.

Replication and concordance between panels/mutant to normal allele peak ratios

To assess the accuracy of the final melanoma-specific mutation panel compared with the OncoCarta panel, the mutant allele frequencies obtained with each panel were compared. There was complete concordance between the mutations identified, and overall, the mutant allele ratios did not significantly differ (P = 0.118, Student paired t test; Table 1). A wide range of mutant allele peaks was detected in the complete cohort of samples, ranging from 5% to 100%. Tumor samples generally had lower mutant allele peaks than cell lines (Fig. 2). This effect is likely due to the presence of contaminating stromal cells in the tumor specimens.

Figure 2.

Mutant allele frequencies detected between cell line and tumor samples. The mutant allele frequency of samples is grouped on the x-axis in 20% increments, whereas the y-axis shows the overall percentage of samples.

Figure 2.

Mutant allele frequencies detected between cell line and tumor samples. The mutant allele frequency of samples is grouped on the x-axis in 20% increments, whereas the y-axis shows the overall percentage of samples.

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Several cell lines in the series tested had either additional clones or matched tumor samples that were also screened with the melanoma-specific mutation panel. Cell lines with duplicate clones had 100% concordance in mutations identified (n = 21). All 16 cell lines had comparable mutation profiles when compared with their matched tumor samples, although 2 cell lines differed in mutant to normal allele frequency ratios from their parent tumors (Supplementary Table S4).

Recent success in molecular-based targeted therapies has provided new avenues for the treatment of a wide variety of cancers based on the mutational profiles of the tumor. This approach is exemplified by the clinical efficacy observed in recent trials using RAF and tyrosine kinase inhibitors (7, 17, 18). The success of this strategy relies heavily on the stratification of patients based on the spectrum of mutations harbored within their tumor. For effective use in a clinical research setting, the identification of mutations that confer drug sensitivity and absence of those that confer drug resistance in an accurate, high-throughput and timely manner is highly desirable. Here, we describe the development of a melanoma-specific mutation panel that can rapidly and accurately identify clinically relevant mutations in metastatic melanoma.

A very high concordance between the melanoma-specific panel and OncoCarta panel was observed when comparing mutation and allele frequencies, indicating the remultiplexing process required to generate the melanoma-specific panel did not affect its reproducibility. High concordance was also observed between matched tumor and cell line samples; only 2 of 16 samples had slight differences in mutation profiles, possibly indicating clonal selection during cell line generation. Alternatively, the disparity may result from stromal contamination in the tumor samples leading to an observed difference in mutant normal allele ratios. One benefit of using the Sequenom MassARRAY system for mutation analysis is the quantification of mutant to wild-type allele ratios, a key feature for estimating mutant subpopulations of cells. The platform allowed detection of samples with mutant allele frequencies at 10%. However, one mutation was reliably detected as low as 5% (MM595-Tumor) with the inclusion of a number of duplicates. A skewing toward lower mutant allele peaks was observed in excised human tumors when compared with cell lines, consistent with stromal contamination. Sensitive mutation detection is an important feature in a clinical setting as a high degree of admixture of cell populations within a tumor has the potential to disguise mutation events critical to choice of therapy.

In an attempt to reduce cost while also maintaining high-throughput analysis, a confirmation panel of highly ranked mutations was initially screened in 271 melanoma samples to identify frequently occurring mutations. As the majority of mutations selected for the confirmation panel have only been identified in single tumors, screened in small numbers of samples, and not replicated in independent cohorts of samples, it is possible a number of these events may be patient-specific mutations or passenger mutations not responsible for melanoma development. This is shown by a high proportion of mutations identified in a large-scale sequencing screen of the protein tyrosine kinase gene family (31) that were not identified here in 271 melanoma samples during the confirmation stage (n = 50 or 85%).

The final melanoma-specific panel consisted of mutations identified within the confirmation stage or that have direct clinical significance to current or novel molecular-based targeted therapeutics. The clinical utility of these mutations is highlighted in Fig. 3. Not surprisingly, a high rate of BRAF V600E mutations was observed in patients for whom BRAF inhibitor treatment would represent an appropriate treatment strategy. Although not all mechanisms of RAF inhibitor resistance can be detected using the melanoma-specific mutation panel, such as upregulation of COT or receptor tyrosine kinases (13), a number of interesting mutations that could potentially confer resistance to inhibitors of the MAPK pathway were identified, as detailed below.

Figure 3.

Commonly affected genes and pathways in melanoma. A number of mutations in these genes are included in the melanoma-specific mutation panel and have therapeutic targets that have been or are currently under clinical investigation (denoted under gene name). Oval boxes represent oncogenes, whereas rectangular boxes represent tumor suppressor genes.

Figure 3.

Commonly affected genes and pathways in melanoma. A number of mutations in these genes are included in the melanoma-specific mutation panel and have therapeutic targets that have been or are currently under clinical investigation (denoted under gene name). Oval boxes represent oncogenes, whereas rectangular boxes represent tumor suppressor genes.

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An early report investigating mechanisms of resistance to inhibitors of the MAPK pathway identified a P124L MEK mutation from a resistant metastatic tumor in a patient treated with a MEK inhibitor, AZD6244, which was not present before treatment (11). Ex vivo functional analysis revealed the mutation was responsible for acquired resistance of AZD6244 treatment and likely explained drug resistance in this patient. Interestingly, the P124L mutation showed cross-resistance to PLX4720 in vitro, indicating MEK mutations may be clinically relevant to RAF inhibitor treatment. Screening for the MEK P124L mutation with the melanoma-specific mutation panel revealed mutations in 3 samples, one of which had an additional BRAF V600K mutation (D28). This suggests that MEK P124L mutations occur in a small proportion of patients with metastatic melanoma and possibly represents an alternative mechanism to the activation of the MAPK pathway apart from commonly observed BRAF and NRAS mutations.

Although some clinical trials of recent BRAF inhibitors have enrolled only melanoma patients with BRAF V600E mutations, there is evidence of positive drug response for V600K mutated tumors in vitro and in vivo (7, 32, 33). If V600K-mutated tumors are susceptible to modern BRAF inhibitors, as is suggested by results of an early phase clinical trial of GSK2118436 (34), screening for additional clinically relevant mutations within the tumor through mutation panels may be important for overall response to selected therapies. The sample with both BRAF V600K and MEK P124L is an interesting example in which screening only for the BRAF mutation would not have identified the ideal treatment strategy: a BRAF inhibitor would likely be ineffective due to the co-occurence of a MEK mutation.

Recently, mutations in GNAQ and GNA11 at amino acid residues R183 and Q209 have been identified in up to 83% of uveal melanomas (27, 28). Mutations in GNAQ and GNA11 occurred here only in cell lines of uveal origin except for a GNA11 R183C mutation identified in a cutaneous, superficial spreading melanoma with a nodular component (TB1). As far as we are aware, this is the first documented GNA11 mutation in a nonuveal melanoma. From a clinical aspect, initial reports indicate that tumors with mutations in GNAQ and GNA11 are likely to be responsible for the activation of the MAPK pathway and may potentially be susceptible to treatment with MEK inhibitors (27). However, GNAQ and GNA11 mutant uveal cells showed mild sensitivity to MEK inhibitors and complete resistance to BRAF inhibitor treatment in vitro (35).

An emerging treatment regimen in KIT-mutated melanomas is through use of tyrosine kinase inhibitors such as Imatinib (17). TB349 was the only sample harboring a KIT mutation (W557R) in our series. The low rate of KIT mutations observed in this data set is possibly due to insufficient coverage provided by the melanoma-specific mutation panel, compared with the wide variety of mutations reported to activate this gene (36). However, the most frequently documented mutations identified in KIT in melanoma were included in the final panel. Acral and mucosal melanomas are the subtypes most likely to harbor mutations in KIT (10%–15% of cases); however, the frequency of KIT mutations in melanomas occurring in chronically sun-exposed anatomic sites is probably much lower than reported in earlier studies (37). This interpretation is supported by our results because while our cohort contained only 2 acral and 1 mucosal melanomas, one third of the melanomas in the MIA subset arose from primary melanomas with high sun damage scores or from chronically sun-exposed body sites.

Platelet-derived growth factor receptor A (PDGFRA) mutations occur in a mutually exclusive pattern to KIT in 5% to 10% of gastrointestinal stromal tumors (GIST) and is a therapeutic target for imatinib (38). The most frequent mutation of PDGFRA in GIST occurs at D842V and shows both in vitro and in vivo resistance to imatinib; however, it is important to note that alternative mutations in PDGFRA are imatinib responsive and represent a viable treatment strategy (38–40). Although early reports failed to identify PDGFRA mutations in melanoma (41, 42), recent studies indicate mutations in PDGFRA do occur but represent rare events (31, 43, 44). The melanoma-specific mutation panel revealed a PDGFRA E996K mutation in MM648 that had been previously detected in another sample and may represent a mutation hotspot (43). Although a rare event in melanoma, the identification of PDGFRA mutations warrants further investigation because of its likely prediction of imatinib sensitivity in this subset of patients.

Screening of the protein tyrosine kinase gene family in cutaneous metastatic melanoma revealed that novel mutations of ERBB4 occurred in 19% of patients and led to increased kinase activity in vitro (42). Although a majority of these mutations were tested in the confirmation panel (12 of 20 mutations), R393W and E452K were the only mutations in ERBB4 identified in the series of samples. This suggests that a majority of ERBB4 mutations may be rare or patient-specific mutations, or alternatively occur throughout the entire gene instead of in localized regions. However, the identification of E452K in 3 samples in this study suggests that E452 is a likely mutation hotspot in ERRB4, occurring in a small proportion of melanomas. This has clinical significance as in vitro analysis revealed ERRB4 E452K-mutated cell lines had 10- to 250-fold enhanced sensitivity to lapatinib, a tyrosine kinase inhibitor of the HER2 growth receptor pathway (42). Although the significance of ERBB4 in the development and progression of melanoma has yet to be confirmed in vivo; ERBB4 mutations represent rational therapeutic targets for ERBB receptor inhibitors in a subset of patients.

Cyclin-dependent kinase 4 (CDK4) is an important kinase responsible for cell-cycle regulation and is a high penetrance melanoma predisposition gene mutated in a small number of families worldwide (45). In addition, a small number of CDK4 somatic mutations have been identified in melanoma tumors and cell lines; the majority of which result in a cysteine or histidine substitution of an arginine residue at position 24 (46). Mutations occurring at this position abrogate the binding of inhibitor p16 and lead to constitutive kinase activity of CDK4 and cell-cycle progression (47). The melanoma-specific mutation panel revealed 8 samples with either R24C or R24H mutations in CDK4 and was the most commonly mutated gene after BRAF and NRAS. Interestingly, all of these samples also harbored BRAF V600E mutations.

Although the CDK4 mutations identified here have not been determined as somatic events in the samples tested, the recurrence of these mutations suggests an important role in melanoma progression and possible clinical significance in a small number of patients. This subset of tumors may be susceptible to a CDK4 inhibitor strategy of which a number of drugs are currently under development, or in clinical trials. Evidence of this approach was observed in a phase I clinical trial of a CDK modulator UCN-01, which resulted in a partial response in a patient with metastatic melanoma (48). Although a larger phase II trial of UCN-01 in a cohort of 17 patients with metastatic melanoma did not reveal significant disease progression; the spectrum of mutations occurring within these patients was not investigated (49). It is interesting to speculate about the possibility of CDK4 inhibitor drugs such as UCN-01 being most effective in tumors harboring CDK4 mutations, although this has yet to be investigated experimentally. Lastly, the presence of CDK4 mutations in BRAF V600E–mutated cell lines did not confer resistance to BRAF inhibitors in vitro; however, one report investigating a combination approach of CDK4/MEK inhibitors in BRAF V600E/p16 negative cell lines showed a significant increase in apoptosis compared with a respective monotherapeutic strategy (50). Further investigation into the clinical significance of CDK4 mutations and pharmacologic treatment strategies in metastatic melanoma patients is warranted.

In conclusion, we describe here the development and validation of a melanoma-specific mutation panel that can rapidly and accurately determine the molecular profiles of tumors in a cost-effective and high-throughput manner. Using a mass spectrometry approach on the Sequenom MassARRAY system is not only advantageous in terms of minimizing price and maximizing speed but also provides a highly flexible format that will facilitate the addition of novel mutations influencing therapeutic patient response as they are identified. This will be of particular importance with the application of next generation sequencing in identifying mutations associated with the development of melanoma or drug-resistant mutations acquired during treatment. The melanoma-specific mutation panel also allows for sensitive allele detection important for the analysis of tumor samples in which contaminating stromal cells may affect the mutant wild-type allele ratio. Finally, the panel can quickly identify mutation profiles in patient samples and may be useful in a clinical setting to determine effective treatment strategies using molecular-based targeted drugs.

D. Irwin is employed by Sequenom Inc.

Conception and design: K. Dutton-Regester, D. Irwin, G.M. Pupo, N.K. Hayward

Development of methodology: K. Dutton-Regester, D. Irwin, C.W. Schmidt

Acquisition of data: K. Dutton-Regester, D. Irwin, P. Hunt, L. Aoude, V. Tembe, G.M. Pupo, C. Lanagan, C.D. Carter, L. O'Connor, M. O'Rourke, R.A. Scolyer, G.J. Mann, N.K. Hayward

Analysis and interpretation of data: K. Dutton-Regester, D. Irwin, G.M. Pupo, R.A. Scolyer, G.J. Mann, N.K. Hayward

Writing, review, and/or revision of the manuscript: D. Irwin, L. Aoude, V. Tembe, G.M. Pupo, R.A. Scolyer, G.J. Mann, C.W. Schmidt, A. Herington, N.K. Hayward

Administrative, technical, or material support: D. Irwin, P. Hunt, G.M. Pupo, C. Lanagan, A. Herington

Study supervision: D. Irwin, G.J. Mann, A. Herington, N.K. Hayward

The authors thank Drs. Mark Smithers and Andrew Barbour from the Princess Alexandra Hospital, and Prof. John Thompson, Director MIA, on behalf of all surgeons of the Institute, as well as the entire MIA Biospecimen Bank team for collection of specimens and tumors. The authors also thank Drs. Kavitha Gowrishankar and Helen Rizos for the derived cell line subclones.

This work was supported by grants from the National Health and Medical Research Council (NHMRC), Cancer Council Queensland, Cancer Institute NSW, and the Australian Centre for Vaccine Development. Establishment of QIMR cell lines was supported by a NHMRC grant to the Australasian Biospecimen Network (Oncology).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Thompson
JF
,
Scolyer
RA
,
Kefford
RF
. 
Cutaneous melanoma
.
Lancet
2005
;
365
:
687
701
.
2.
Thompson
JF
,
Scolyer
RA
,
Kefford
RF
. 
Cutaneous melanoma in the era of molecular profiling
.
Lancet
2009
;
374
:
362
5
.
3.
Atkins
MB
,
Kunkel
L
,
Sznol
M
,
Rosenberg
SA
. 
High-dose recombinant interleukin-2 therapy in patients with metastatic melanoma: long-term survival update
.
Cancer J Sci Am
2000
;
6
Suppl 1
:
S11
4
.
4.
Davies
H
,
Bignell
GR
,
Cox
C
,
Stephens
P
,
Edkins
S
,
Clegg
S
, et al
Mutations of the BRAF gene in human cancer
.
Nature
2002
;
417
:
949
54
.
5.
Eisen
T
,
Ahmad
T
,
Flaherty
KT
,
Gore
M
,
Kaye
S
,
Marais
R
, et al
Sorafenib in advanced melanoma: a phase II randomised discontinuation trial analysis
.
Br J Cancer
2006
;
95
:
581
6
.
6.
Tsai
J
,
Lee
JT
,
Wang
W
,
Zhang
J
,
Cho
H
,
Mamo
S
, et al
Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity
.
Proc Natl Acad Sci U S A
2008
;
105
:
3041
6
.
7.
Chapman
PB
,
Hauschild
A
,
Robert
C
,
Haanen
JB
,
Ascierto
P
,
Larkin
J
, et al
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
.
N Engl J Med
2011
;
364
:
2507
16
.
8.
Poulikakos
PI
,
Zhang
C
,
Bollag
G
,
Shokat
KM
,
Rosen
N
. 
RAF inhibitors transactivate RAF dimers and ERK signalling in cells with wild-type BRAF
.
Nature
2010
;
464
:
427
30
.
9.
Halaban
R
,
Zhang
W
,
Bacchiocchi
A
,
Cheng
E
,
Parisi
F
,
Ariyan
S
, et al
PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF melanoma cells
.
Pigment Cell Melanoma Res
2010
;
23
:
190
200
.
10.
Hatzivassiliou
G
,
Song
K
,
Yen
I
,
Brandhuber
BJ
,
Anderson
DJ
,
Alvarado
R
, et al
RAF inhibitors prime wild-type RAF to activate the MAPK pathway and enhance growth
.
Nature
2010
;
464
:
431
5
.
11.
Emery
CM
,
Vijayendran
KG
,
Zipser
MC
,
Sawyer
AM
,
Niu
L
,
Kim
JJ
, et al
MEK1 mutations confer resistance to MEK and B-RAF inhibition
.
Proc Natl Acad Sci U S A
2009
;
106
:
20411
6
.
12.
Wagle
N
,
Emery
C
,
Berger
MF
,
Davis
MJ
,
Sawyer
A
,
Pochanard
P
, et al
Dissecting therapeutic resistance to RAF inhibition in melanoma by tumor genomic profiling
.
J Clin Oncol
2011
;
29
:
3085
96
.
13.
Nazarian
R
,
Shi
H
,
Wang
Q
,
Kong
X
,
Koya
RC
,
Lee
H
, et al
Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation
.
Nature
2010
;
468
:
973
7
.
14.
Jiang
CC
,
Lai
F
,
Thorne
RF
,
Yang
F
,
Liu
H
,
Hersey
P
, et al
MEK-independent survival of B-RAFV600E melanoma cells selected for resistance to apoptosis induced by the RAF inhibitor PLX4720
.
Clin Cancer Res
2011
;
17
:
721
30
.
15.
Villanueva
J
,
Vultur
A
,
Lee
JT
,
Somasundaram
R
,
Fukunaga-Kalabis
M
,
Cipolla
AK
, et al
Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K
.
Cancer Cell
2010
;
18
:
683
95
.
16.
Kim
KB
,
Eton
O
,
Davis
DW
,
Frazier
ML
,
McConkey
DJ
,
Diwan
AH
, et al
Phase II trial of imatinib mesylate in patients with metastatic melanoma
.
Br J Cancer
2008
;
99
:
734
40
.
17.
Guo
J
,
Si
L
,
Kong
Y
,
Flaherty
KT
,
Xu
X
,
Zhu
Y
, et al
Phase II, open-label, single-arm trial of imatinib mesylate in patients with metastatic melanoma harboring c-kit mutation or amplification
.
J Clin Oncol
2011
;
29
:
2904
9
.
18.
Yamaguchi
M
,
Harada
K
,
Ando
N
,
Kawamura
T
,
Shibagaki
N
,
Shimada
S
. 
Marked response to imatinib mesylate in metastatic acral lentiginous melanoma on the thumb
.
Clin Exp Dermatol
2011
;
36
:
174
7
.
19.
Pavey
S
,
Johansson
P
,
Packer
L
,
Taylor
J
,
Stark
M
,
Pollock
PM
, et al
Microarray expression profiling in melanoma reveals a BRAF mutation signature
.
Oncogene
2004
;
23
:
4060
7
.
20.
Castellano
M
,
Pollock
PM
,
Walters
MK
,
Sparrow
LE
,
Down
LM
,
Gabrielli
BG
, et al
CDKN2A/p16 is inactivated in most melanoma cell lines
.
Cancer Res
1997
;
57
:
4868
75
.
21.
Repp
AC
,
Mayhew
ES
,
Apte
S
,
Niederkorn
JY
. 
Human uveal melanoma cells produce macrophage migration-inhibitory factor to prevent lysis by NK cells
.
J Immunol
2000
;
165
:
710
5
.
22.
Carter
C
,
De Silva
C
,
Synnott
M
,
Thompson
J
,
Stretch
J
,
Spillane
A
, et al
Bio-Specimen banking: Melanoma Institute Australia
.
Pigment Cell Melanoma Res
2010
;
23
:
946
7
.
23.
Rizos
H
,
Darmanian
AP
,
Indsto
JO
,
Shannon
JA
,
Kefford
RF
,
Mann
GJ
. 
Multiple abnormalities of the p16INK4a-pRb regulatory pathway in cultured melanoma cells
.
Melanoma Res
1999
;
9
:
10
9
.
24.
Thomas
RK
,
Baker
AC
,
Debiasi
RM
,
Winckler
W
,
Laframboise
T
,
Lin
WM
, et al
High-throughput oncogene mutation profiling in human cancer
.
Nat Genet
2007
;
39
:
347
51
.
25.
Forbes
SA
,
Bhamra
G
,
Bamford
S
,
Dawson
E
,
Kok
C
,
Clements
J
, et al
The catalogue of somatic mutations in cancer (COSMIC)
.
Curr Protoc Hum Genet
2008
;
Chapter 10:Unit 10 11
.
26.
Greenman
C
,
Stephens
P
,
Smith
R
,
Dalgliesh
GL
,
Hunter
C
,
Bignell
G
, et al
Patterns of somatic mutation in human cancer genomes
.
Nature
2007
;
446
:
153
8
.
27.
Van Raamsdonk
CD
,
Bezrookove
V
,
Green
G
,
Bauer
J
,
Gaugler
L
,
O'Brien
JM
, et al
Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi
.
Nature
2009
;
457
:
599
602
.
28.
Van Raamsdonk
CD
,
Griewank
KG
,
Crosby
MB
,
Garrido
MC
,
Vemula
S
,
Wiesner
T
, et al
Mutations in GNA11 in uveal melanoma
.
N Engl J Med
2010
;
363
:
2191
9
.
29.
Zuidervaart
W
,
van Nieuwpoort
F
,
Stark
M
,
Dijkman
R
,
Packer
L
,
Borgstein
AM
, et al
Activation of the MAPK pathway is a common event in uveal melanomas although it rarely occurs through mutation of BRAF or RAS
.
Br J Cancer
2005
;
92
:
2032
8
.
30.
Calipel
A
,
Lefevre
G
,
Pouponnot
C
,
Mouriaux
F
,
Eychene
A
,
Mascarelli
F
. 
Mutation of B-Raf in human choroidal melanoma cells mediates cell proliferation and transformation through the MEK/ERK pathway
.
J Biol Chem
2003
;
278
:
42409
18
.
31.
Prickett
TD
,
grawal
NS
,
Wei
X
,
Yates
KE
,
Lin
JC
,
Wunderlich
JR
, et al
Analysis of the tyrosine kinome in melanoma reveals recurrent mutations in ERBB4
.
Nat Genet
2009
;
41
:
1127
32
.
32.
Rubinstein
JC
,
Sznol
M
,
Pavlick
AC
,
Ariyan
S
,
Cheng
E
,
Bacchiocchi
A
, et al
Incidence of the V600K mutation among melanoma patients with BRAF mutations, and potential therapeutic response to the specific BRAF inhibitor PLX4032
.
J Transl Med
2010
;
8
:
67
.
33.
Yang
H
,
Higgins
B
,
Kolinsky
K
,
Packman
K
,
Go
Z
,
Iyer
R
, et al
RG7204 (PLX4032), a selective BRAFV600E inhibitor, displays potent antitumor activity in preclinical melanoma models
.
Cancer Res
2010
;
70
:
5518
27
.
34.
Kefford
R
,
Arkenau
H
,
Brown
MP
,
Millward
M
,
Infante
JR
,
Long
GV
, et al
Phase I/II study of GSK2118436, a selective inhibitor of oncogenic mutant BRAF kinase, in patients with metastatic melanoma and other solid tumors
.
J Clin Oncol
2010
;
28
:
8503
.
35.
Mitsiades
N
,
Chew
SA
,
He
B
,
Riechardt
AI
,
Karadedou
T
,
Kotoula
V
, et al
Genotype-dependent sensitivity of uveal melanoma cell lines to inhibition of B-Raf, MEK and Akt kinases: rationale for personalized therapy
.
Invest Ophthalmol Vis Sci
2011
;
52
:
7245
55
36.
Curtin
JA
,
Busam
K
,
Pinkel
D
,
Bastian
BC
. 
Somatic activation of KIT in distinct subtypes of melanoma
.
J Clin Oncol
2006
;
24
:
4340
6
.
37.
Handolias
D
,
Salemi
R
,
Murray
W
,
Tan
A
,
Liu
W
,
Viros
A
, et al
Mutations in KIT occur at low frequency in melanomas arising from anatomical sites associated with chronic and intermittent sun exposure
.
Pigment Cell Melanoma Res
2010
;
23
:
210
5
.
38.
Corless
CL
,
Schroeder
A
,
Griffith
D
,
Town
A
,
McGreevey
L
,
Harrell
P
, et al
PDGFRA mutations in gastrointestinal stromal tumors: frequency, spectrum and in vitro sensitivity to imatinib
.
J Clin Oncol
2005
;
23
:
5357
64
.
39.
Heinrich
MC
,
Corless
CL
,
Demetri
GD
,
Blanke
CD
,
von Mehren
M
,
Joensuu
H
, et al
Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor
.
J Clin Oncol
2003
;
21
:
4342
9
.
40.
Weisberg
E
,
Wright
RD
,
Jiang
J
,
Ray
A
,
Moreno
D
,
Manley
PW
, et al
Effects of PKC412, nilotinib, and imatinib against GIST-associated PDGFRA mutants with differential imatinib sensitivity
.
Gastroenterology
2006
;
131
:
1734
42
.
41.
Terada
T
. 
Low incidence of KIT gene mutations and no PDGFRA gene mutations in primary cutaneous melanoma: an immunohistochemical and molecular genetic study of Japanese cases
.
Int J Clin Oncol
2010
;
15
:
453
6
.
42.
Curtin
JA
,
Pinkel
D
,
Bastian
BC
. 
Absence of PDGFRA mutations in primary melanoma
.
J Invest Dermatol
2008
;
128
:
488
9
.
43.
Forbes
SA
,
Bindal
N
,
Bamford
S
,
Cole
C
,
Kok
CY
,
Beare
D
, et al
COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer
.
Nucleic Acids Res
2011
;
39
:
D945
50
.
44.
Wallander
ML
,
Layfield
LJ
,
Emerson
LL
,
Mamalis
N
,
Davis
D
,
Tripp
SR
, et al
KIT mutations in ocular melanoma: frequency and anatomic distribution
.
Mod Pathol
2011
;
24
:
1031
5
45.
Zuo
L
,
Weger
J
,
Yang
Q
,
Goldstein
AM
,
Tucker
MA
,
Walker
GJ
, et al
Germline mutations in the p16INK4a binding domain of CDK4 in familial melanoma
.
Nat Genet
1996
;
12
:
97
9
.
46.
Wolfel
T
,
Hauer
M
,
Schneider
J
,
Serrano
M
,
Wolfel
C
,
Klehmann-Hieb
E
, et al
A p16INK4a-insensitive CDK4 mutant targeted by cytolytic T lymphocytes in a human melanoma
.
Science
1995
;
269
:
1281
4
.
47.
Coleman
KG
,
Wautlet
BS
,
Morrissey
D
,
Mulheron
J
,
Sedman
SA
,
Brinkley
P
, et al
Identification of CDK4 sequences involved in cyclin D1 and p16 binding
.
J Biol Chem
1997
;
272
:
18869
74
.
48.
Sausville
EA
,
Arbuck
SG
,
Messmann
R
,
Headlee
D
,
Bauer
KS
,
Lush
RM
, et al
Phase I trial of 72-hour continuous infusion UCN-01 in patients with refractory neoplasms
.
J Clin Oncol
2001
;
19
:
2319
33
.
49.
Li
T
,
Christensen
SD
,
Frankel
PH
,
Margolin
KA
,
Agarwala
SS
,
Luu
T
, et al
A phase II study of cell cycle inhibitor UCN-01 in patients with metastatic melanoma: a California Cancer Consortium trial
.
Invest New Drugs
2012
;
30
:
741
8
.
50.
Li
J
,
Xu
M
,
Yang
Z
,
Li
A
,
Dong
J
. 
Simultaneous inhibition of MEK and CDK4 leads to potent apoptosis in human melanoma cells
.
Cancer Invest
2010
;
28
:
350
6
.