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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Commun. Author manuscript; available in PMC 2012 December 26.
Published in final edited form as:
PMCID: PMC3530385

Melanoma whole exome sequencing identifies V600EB-RAF amplification-mediated acquired B-RAF inhibitor resistance


The development of acquired drug resistance hampers the long-term success of B-RAF inhibitor (B-RAFi) therapy for melanoma patients. Here we show V600EB-RAF copy number gain as a mechanism of acquired B-RAFi resistance in four out of twenty (20%) patients treated with B-RAFi. In cell lines, V600EB-RAF over-expression and knockdown conferred B-RAFi resistance and sensitivity, respectively. In V600EB-RAF amplification-driven (vs. mutant N-RAS-driven) B-RAFi resistance, ERK reactivation is saturable, with higher doses of vemurafenib down-regulating pERK and re-sensitizing melanoma cells to B-RAFi. These two mechanisms of ERK reactivation are sensitive to the MEK1/2 inhibitor AZD6244/selumetinib or its combination with the B-RAFi vemurafenib. In contrast to mutant N-RAS-mediated V600EB-RAF bypass, which is sensitive to C-RAF knockdown, V600EB-RAF amplification-mediated resistance functions largely independently of C-RAF. Thus, alternative clinical strategies may potentially overcome distinct modes of ERK reactivation underlying acquired B-RAFi resistance in melanoma.


Activating B-RAF V600 kinase mutations occur in ~50% of melanomas1, and the ATP-competitive type I RAF inhibitors, PLX4032/vemurafenib and GSK2118436, display remarkable activity leading to overall survival advantage in patients with V600B-RAF mutant melanomas26. Acquisition of drug resistance leading to clinical relapse, however, develops in virtually all patients treated with B-RAF inhibitors (B-RAFi)4,5. Heterogeneous mechanisms of acquired B-RAFi resistance hitherto uncovered fall into general MAPK-redundant, AKT-dependent7,8 or MAPK-reactivating9,10 pathways, indicating specific translatable therapeutic strategies to prevent or overcome resistance. Contrary to expectation, V600EB-RAF secondary mutations have not been found to account for acquired B-RAFi resistance10, suggesting V600EB-RAF-bypass mechanisms as the principal means to ERK reactivation.

Here we observed an alteration in V600EB-RAF, namely genomic copy number gain, in tumors of melanoma patients whose cancer progressed after initial responses to B-RAF inhibitors. We demonstrated that this V600EB-RAF amplification results in V600EB-RAF over-expression, which is necessary and sufficient for acquired resistance to B-RAF inhibitor. This finding, along with a recent study reporting N-terminal truncation of V600EB-RAF causing acquired B-RAFi resistance in melanoma11, underscores key molecular alterations in the drug target itself. We further suggest that V600EB-RAF-instrinsic (amplification, truncation) vs. V600EB-RAF-bypass (N-RAS mutations) mechanisms, both reactivating the MAPK pathway, may offer insights into distinct therapeutic strategies to overcome acquired B-RAFi resistance in melanoma.


Whole exome sequencing identifies V600EB-RAF amplification

We assembled twenty sets of patient-matched baseline (prior to B-RAFi therapy) and disease progression (DP) (i.e., acquired B-RAFi resistance) melanoma tissues and analyzed them to identify the proposed mechanisms of acquired B-RAFi resistance in melanoma. These reported mechanisms include N-RAS10 and MEK112 mutations, alternative-spliced V600EB-RAF variants11, and over-expression of RTKs (PDGFRβ7,10, IGF1-R8) and COT9 (Tables 1 and Supplementary Table S1; Supplementary Fig. S1). For DP samples negative for these mechanisms and where there was sufficient frozen and patient-matched normal tissues (from patients #4, 5, 8, 14, 16, 17 & 18), we subjected triads of genomic DNAs (gDNAs) from normal, baseline, and DP tissues to whole exome sequencing. In two available data sets, we searched for somatic DP-specific non-synonymous single nucleotide variants (nsSNVs) and small insertion-deletion (indels), which were exceedingly few in number or absent, respectively, using our bioinformatic workflow (Supplementary Tables S2 and S3). We also analyzed for DP-specific copy number variations (CNVs) from the exome sequence data (Supplementary Table S2). This identified V600EB-RAF copy number gains in these two patients’ DP tissues (2.2 and 12.8 fold in patients #5 and 8, respectively) relative to their respective baseline tissues (Fig. 1a; Table 1). Gain in V600EB-RAF copy number was reflected in corresponding increased gene expression at the protein level (Fig. 1b).

Figure 1
Exome sequencing identifies V600EB-RAF amplification as a candidate mechanism for BRAFi resistance
Table 1
Clinical characteristics and acquired resistance mechanisms in patients with matched baseline and disease progression (DP) melanomas tissues.

V600EB-RAF amplification was validated by gDNA Q-PCR, producing consistent fold increases in DP-specific V600EB-RAF copy number gain (relative to baseline) (2.0 and 14 fold increase in patient #5 and 8 respectively) (Fig. 1c). We then expanded the analysis of V600EB-RAF amplification to all twenty paired melanoma tissues and detected V600EB-RAF copy number gains in DP samples from two additional patients (2.3 and 3 fold for DP2 of patient #9 & DP of patient #13, respectively) (Fig. 1c; Table 1). We note that these copy number fold increases are likely underestimates of the true changes due to non-tumor diploid cell contents and tumor heterogeneity, as most disease progressive tumors occur from stable residual tumors as a result of partial responses seen in the vast majority of patients treated with B-RAF inhibitors. An increase in the mutant B-RAF to WT B-RAF ratio was also noted in all four cases of DP harboring B-RAF copy number gain when compared to their respective baseline tissues (Fig. 1d), consistent with selection for V600EB-RAF (vs. the WT B-RAF allele) copy number gain during acquisition of B-RAFi resistance. V600EB-RAF amplification was largely mutually exclusive with N-RAS mutations (no enrichment in MEK1 exon 3 mutation was detected in DP vs. baseline tumors), RTK over-expression (no COT over-expression detected), as well as a novel mechanism involving V600EB-RAF alternative splicing11 (Table 1; Supplementary Fig. S1).

B-RAFi selects for V600EB-RAF gain and over-expression

We have derived vemurafenib/PLX4032-resistant (R) sub-lines by providing continuous vemurafenib exposure to seven human melanoma-derived V600EBRAF-positive parental (P) cell lines sensitive to vemurafenib-mediated growth inhibition. Four resistant sub-lines, including M229 R5 and M238 R17,10, over-expressed PDGFRβ compared to their parental counterpart. One sub-line (M249 R410) gained a mutation in N-RAS, and another (M397 R) an alternatively spliced variant of V600EB-RAF resulting in in-frame fusion of exons 1 and 11 (Supplementary Fig. S2). As in our tissue analysis, these mechanisms were identified in a mutually exclusive manner. Another vemurafenib-resistant sub-line, M395 R, was derived from a V600EB-RAF-homozygous parental line, M395 P (Supplementary Fig. S3a). Compared to M395 P, M395 R harbors increased copy numbers of V600EB-RAF gDNA and cDNA, consistent with a dramatic V600EB-RAF protein over-expression (Supplementary Fig. S3b, c, and d). M395 R displays growth highly resistant to vemurafenib treatment (Supplementary Fig. S4a), and titration of M395 R with vemurafenib (1 h) after a 24 h of drug withdrawal revealed pERK levels to be highly resistant to acute V600EB-RAF inhibition (Supplementary Fig. S4b). This pattern of MAPK reactivation was similar to that seen in a mutant N-RAS-driven, vemurafenib-resistant sub-line, M249 R4, and contrasted with that in the RTK-driven vemurafenib-resistant sub-line, M229 R5 (Supplementary Fig. S4b)7,10. Expectedly, the levels of p-AKT are unchanged (Fig. 2b) comparing M395 P vs. M395 R, consistent with a lack of RTK over-expression leading to MAPK-redundant, PI3K-AKT signaling7. Accordingly, M395 R does not over-express either PDGFRβ or IGF-1R, in contrast to M229 R5, which has been shown to over-express the RTK PDGFRβ (Supplementary Fig. S4c)7,8. Additionally, M395 R is WT for N-, H- and K-RAS and MEK1, harbors no secondary mutations in V600EB-RAF or an alternatively spliced variant of V600EB-RAF which results in a N-terminally truncated V600EB-RAF protein.

Figure 2
V600EB-RAF levels modulate melanoma sensitivity to vemurafenib

Modest V600EB-RAF over-expression leads to B-RAFi resistance

Three different but uniformly modest levels of V600EB-RAF over-expression were achieved by infecting M395 P with varying viral titers and subsequent puromycin selection. This resulted in relatively low (1.9 fold over empty vector virus control), medium (2.4 fold) and high (2.8 fold) levels of V600EB-RAF RNA/cDNA over-expression (Supplementary Fig. S5), with the corresponding protein over-expression levels shown in Figure 2a. In comparison, in two sets of tissues (from patients #8 and #13) where flash-frozen tissues were available, the RNA/cDNA levels of V600EB-RAF in the DP tumors were 9.5 and 1.4 fold relative to those in their patient-matched baseline tumors. Notably, the DP tumor from patient #13 was obtained by an intervention radiology-guided needle biopsy of a pelvic mass (Supplementary Table S1) and contained a high admixture of normal and tumor contents (latter indicated by S100), which likely contributed to an underestimation of the true change in the V600EB-RAF RNA/cDNA levels.

V600EB-RAF gain leads to drug-saturable resistance

The modest and incremental over-expression of V600EB-RAF at the RNA and protein levels in M395 P conferred similar degrees of vemurafenib resistance (Fig. 2b). Interestingly, further V600EB-RAF over-expression at a much greater level, as in the case of M395 R relative to M395 P (increase in RNA/cDNA level shown in Supplementary Fig. S3c and S5; increase in protein level shown in Fig. 2c) conferred enhanced drug resistance mainly at 1 μM vemurafenib but not 10 μM vemurafenib (Fig. 2d). Thus, a modest V600EB-RAF copy number gain and over-expression can confer vemurafenib resistance, and even high amplitude V600EB-RAF amplification and over-expression can be readily saturable by micromolar concentrations of vemurafenib.

Moreover, V600EB-RAF knockdown in M395 R confers vemurafenib sensitivity (Fig. 2c and d). Consistently, V600EB-RAF over-expression in M395 P (at a level titrated to be comparable to M395 R) and its knockdown in M395 R resulted in pERK resistance and sensitivity, respectively, to acute vemurafenib treatment after a 24 h drug withdrawal (Fig. 2e). We predicted that, regardless of the cellular genetic context, MAPK reactivation due to drug target (i.e., V600EB-RAF) over-expression would be saturable by higher doses of vemurafenib, in contrast to mutant N-RAS-mediated MAPK reactivation where V600EB-RAF may be bypassed by the alternative use of C-RAF13. Indeed, dosing of vemurafenib from 1 to 50 μM revealed a significant difference in drug sensitivity of M249 R4 (Q61KN-RAS) vs. M395 R (amplified V600EB-RAF) (Fig. 3a) (where the latter was highly sensitive to vemurafenib at this drug concentration range), suggesting a potential therapeutic opportunity. To rule out that these results were not due to a difference in genetic backgrounds, we artificially rendered the V600EB-RAF melanoma cell line, M229, vemurafenib-resistant by either Q61KN-RAS or V600EB-RAF viral transduction (Fig. 3b). Again, high dose vemurafenib treatment was more effective at overcoming drug resistance in V600EB-RAF-transduced M229 than in the same cell line transduced with Q61KN-RAS.

Figure 3
Differential B-RAFi/MEKi sensitivities and C-RAF dependency

MEK inhibition restores vemurafenib sensitivity

Since both N-RAS mutation and V600EB-RAF amplification-driven acquired resistance mechanisms would be anticipated to result in MEK reactivation, we tested the allosteric MEKi, AZD6244/selumetinib, on the Q61KN-RAS-driven M249 R4 and the V600EB-RAF amplification-driven M395 R sub-lines. MEKi treatment resulted in decreased proliferation in both cases, but the activity was noted at lower concentrations for the Q61KN-RAS-driven resistance mechanism (Fig. 3c). This differential pattern was reproducible by exposing AZD6244/selumetinib to V600EB-RAF melanoma cell lines M229 and M238 transduced with high levels of V600EB-RAF vs. a short-term culture, Pt55 R10, with Q61KN-RAS-driven acquired B-RAFi resistance (Fig. 3d). We also tested the combination of B-RAFi with MEKi, which is currently in clinical testing14, in three-day survival assays. A calculation of combination index (CI) values using equal ratios of vemurafenib and selumetinib was performed. The results were consistent with a highly synergistic effect of these two agents combined in overcoming both mutant N-RAS-driven (M249 R4) and V600EB-RAF amplification-driven B-RAFi resistance (M395 R) (Fig 3e and 3f), although the combination tended to be more potent against mutant N-RAS-driven acquired resistance to vemurafenib. This B-RAFi and MEKi combinatorial synergy was further corroborated in longer-term clonogenic assays (Fig. 3g).

Differential C-RAF dependency of ERK-reactivating mechanisms

We also predicted that MAPK reactivation due to V600EB-RAF over-expression would be C-RAF-independent, in contrast to mutant N-RAS-mediated MAPK reactivation where V600EB-RAF may be bypassed by the alternative use of C-RAF. Indeed, C-RAF knockdown by shRNA sensitized the mutant N-RAS sub-line, M249 R4, but not the V600EB-RAF amplified sub-line, M395 R, to vemurafenib in three-day survival assays (Fig. 3h). C-RAF knockdown restored vemurafenib sensitivity to M249 R4 (Q61KN-RAS/V600EB-RAF) even more strikingly in a longer-term clonogenic assays which afforded fresh drug replacement every two days (Fig. 3i). An independent C-RAF shRNA also restored vemurafenib sensitivity to M249 R4 (Supplementary Table S4). Additionally, B-RAFi and MEKi synergy and C-RAF-dependence in mutant N-RAS-driven acquired B-RAFi resistance was confirmed in a short-term culture derived from a tumor with clinical acquired vemurafenib resistance (Supplementary Fig. S6).


Identification of V600EB-RAF amplification as a mechanism of acquired resistance in B-RAFi treated patients provides evidence for alterations in the drug target causing clinical relapse. Based on these studies, therapeutic stratification of MAPK reactivation underlying B-RAFi resistance into drug-saturable or C-RAF-dependent pathways may be translatable into the design of next-generation clinical trials aimed at preventing or overcoming B-RAFi resistance (Fig. 4). These findings also provide pre-clinical rationale for dose escalation studies in selected patients with B-RAFi-resistant V600E/KB-RAF metastatic melanomas, particularly given the wide range of effective dosing and the fact that the maximum tolerated dose of GSK2118436 has not been determined. The combination of current B-RAF inhibitors (or next-generation RAF inhibitors that enhance B-RAF potency or feature pan-RAF inhibition) with MEK1/2 inhibitors may potentially broadly block MAPK reactivation.

Figure 4
MAPK-reactivating mechanisms of acquired B-RAFi resistance and therapeutic implications

Emerging evidence points to B-RAF mutant cancers of other tissue origin or lineage being less responsive to specific B-RAF inhibition than B-RAF mutant melanomas. Mechanisms of acquired B-RAF inhibitor resistance may turn out to be instructive for understanding primary resistance of B-RAF mutant cancer types to B-RAF inhibitors, as primary (de novo) and secondary (or acquired) drug resistance may be clinical manifestations from a spectrum of molecular alterations that are mechanistically linked. Thus, multiple modes (e.g., mutation, copy number gain) of up-regulating oncogene activity, which may pre-exist in the same tumor and/or patient, may help explain the range of heterogeneous responses of B-RAF mutant cancers to direct B-RAF, MEK or ERK inhibition.


Cell culture experiments

Cells were maintained in DMEM with 10 or 20% fetal bovine serum and glutamine. shRNAs (Supplementary Table S4) for B-RAF and C-RAF were sub-cloned into the lentiviral vector pLL3.7; pBabe B-RAF (V600E) was purchase (plasmid 17544, Addgene); viral supernatants generated by co-transfection with three packaging plasmids into HEK293T cells; and infections carried out with protamine sulfate. Stocks and dilutions of PLX4032 (Plexxikon, Berkeley, CA) and AZD6244 (commercially available) were made in DMSO. Cells were quantified using CellTiter-GLO Luminescence (Promega) or crystal violet staining followed by NIH Image J quantification.

Whole exome sequencing

Human tissues were obtained with patient-informed consent under UCLA Institutional Review Board (#10-001089) approval. For each sample, 3ug of high molecular weight genomic DNA was used as the starting material to generate the sequencing library. Exome captures were performed using Agilent SureSelect Human All Exon 50mb and Agilent SureSelect Human All Exon 50mb XT for PT #5 and Pt #8, respectively, per manufacturers’ recommendation, to create a mean 200bp insert library. For Pt #5, sequencing was performed on Illumina GenomeAnalyzerII (GAII) as 76+76bp paired-end run. The normal sample was run on 1 flowcell lane and the tumor samples were run on 2 flowcell lanes each. For Pt #8, sequencing was performed on Illumina HiSeq2000 as 50+50bp paired-end run and 100+100bp paired-end run. The three samples (normal, baseline and DP) were initially mixed with 9 other samples and run across 5 flowcell lanes for the 50+50bp run. For the 100+100bp run, they were mixed with 3 other samples to be run across 5 flowcell lanes with barcoding of each individual genomic sample library.

For Pt #5, approximately 62 million, 137 million, 147 million reads were generated for normal tissue (skin), baseline melanoma and DP melanoma, respectively, with 75.2%, 78.1%, and 74.7% of the reads mapping to capture targets. Based on an analysis of reads that uniquely aligned to the reference genome and for which the potential PCR duplicates were removed, an average coverage of 52X, 88X, and 114X was achieved with 87%, 92% and 93% of the targeted bases being covered at 10X or greater read depth for normal, baseline and DP, respectively.

For Pt #8, approximately 198 million, 270 million, 256 million reads were generated for normal tissue (skin), baseline melanoma and DP melanoma, respectively with 43.2%, 44.1% and 42.3% of the reads mapping to capture targets. Based on an analysis of reads that uniquely aligned to the reference genome and for which the potential PCR duplicates were removed, an average read depth of 107X, 132X and 123X was achieved with 89%, 90% and 90% of the targeted bases being covered at 10X or greater for normal, baseline and DP, respectively.

Sequencing data analysis

For Pt #8 where the samples were indexed and pooled before the sequencing, Novobarcode from Novocraft was used to demultiplex the data. The sequence reads were aligned to the human reference genome using Novoalign V2.07.13 from Novocraft ( For Pt #5, hg18 downloaded from UCSC genome database was used and for Pt #8, b37 downloaded from GATK (Genome analysis toolkit) resources website was used for the reference genome. SAMtools v.0.1.1616 was used to sort and merge the data and Picard ( was used to mark PCR duplicates. To correct the misalignments due to the presence of indels, local realignment was performed using RealignerTargetCreator and IndelRealigner of GATK17. Indel calls in dbSNP132 were used as known indel input. Then, GATK CountCovariates and TableRecalibration were used to recalibrate the originally reported quality score by using the position of the nucleotide within the read and the preceding and current nucleotide information. Finally, to call the single nucleotide variants (SNVs), the GATK UnifiedGenotyper was used to the realigned and re-calibrated bam file while GATK IndelGenotyperV2 was used to call small insertion/deletions (Indels). To generate a list of somatic variants for DP tumor, the difference in allele distribution was calculated using one-sided Fisher’s exact test using normal sample or the baseline sample. Variants with p-value<0.05 were included in the “somatic variant list”. Low coverage (<10X) SNVs and SNVs with more than one variant allele in normal tissue and baseline melanoma were filtered out during the process. These somatic variants were further annotated with SeattleSeqSNPannotation ( For DP-specific, non-synonymous SNVs that result in missense mutations, we assessed the level of amino acid conservation using PhyloP score (provided in UCSC genome database) where a score > 2 implies high conservation and the nature of amino substitution using Polyphen-2 analysis18.

CNV analysis was performed using an R package, ExomeCNV15. ExomeCNV uses the ratio of read depth between two samples at each capture interval. Here, the read depth data between baseline and DP melanomas were compared. Briefly, the read depth information was extracted through the PILEUP file generated from the BAM file after removing PCR duplicates using SAMtools. The average read depth at each capture interval was calculatedand the classify.eCNV module of ExomeCNV was run with the default parameters to calculate the copy number estimate for each interval. Subsequently, another R package commonly used to segment the copy number intervals, DNAcopy19, was called through ExomeCNV multi.CNV.analyze module with default parameters to do segmentation and sequential merging. The genomic regions with copy number 1 were called deletion and any regions with copy number >2 were called amplification. Circos20 was used to visualize the CNV data.

Protein detection

Western blots were probed with antibodies against p-ERK1/2 (T202/Y204), ERK1/2, C-RAF, AKT (Ser473), AKT (Thr308), AKT (Cell Signalig Technologies; all at 1:1000), N-RAS, B-RAF (Santa Cruz Biotechnology; both at 1:500), and tubulin (Sigma; 1:700). For B-RAF immunohistochemistry, paraffin-embedded formalin-fixed tissue sections were antigen-retrieved, incubated with the primary antibody (Santa Cruz Biotechnology; 1:50) followed by HRP-conjugated secondary antibody (Envision System, DakoCytomation). Immunocomplexes were visualized using the DAB (3,3′-diaminobenzidine) peroxidase method and nuclei hematoxylin-counterstained.

Genomic DNA and RNA quantifications

For real-time quantitative PCR, total RNA was extracted and cDNA quantified by the iCycler iQ Real Time PCR Detection System (BioRad). Data were normalized to TUBULIN and GAPDH levels. Relative expression is calculated using the delta-Ct method. gDNAs were extracted using the FlexiGene DNA Kit (Qiagen) (Human Genomic DNA-Female, Promega). B-RAF relative copy number was determined by quantitative PCR (cycle conditions available upon request) using the MyiQ single color Real-Time PCR Detection System (Bio-Rad). Total DNA content was estimated by assaying β-globin for each sample, and 20 ng of gDNA was mixed with the SYBR Green QPCR Master Mix (Bio-Rad) and 2 pmol/L of each primer. All primer sequences are provided in Supplementary Table S4.

Data processing

Statistical analyses were performed using InStat 3 Version 3.0b (GraphPad Software); graphical representations using DeltaGraph or Prism (Red Rock Software); and combination index calculation using CalcuSyn V2.1 (Biosoft). Calculations were made by CalcuSyn software using the method of Chou and Taladay. Interpretation of CI values is summarized as follows: CI < 0.1 (very strong synergy); = 0.1–0.3 (strong synergy); = 0.3–0.7 (synergy); 0.7–0.85 (moderate synergy); = 0.85–0.9 (slight synergy); = 0.90–1.10 (nearly additive); and = 1.10–1.20 (slight antagonism). The relevant correlated Log10 (CI) values are shown as follow: Log10 (CI 0.1) = −1; Log10 (CI 0.3) = −0.5228787452803376; Log10 (CI 0.7) = −0.1549019599857432, and Log10 (CI 0.85) = −0.07058107428570727.

Supplementary Material


We are grateful to G. Bollag (Plexxikon Inc.) for providing PLX4032, J. S. Economoufor biopsies, N. Doan for immunohistochemistry, B. Chmielowski and J. Glaspy for coordinated patient care, T.L. Toy for technical help with library generation for deep sequencing, and B. Harry for help with analysis of whole exome sequence data. R.S.L. acknowledges funding from the following: Burroughs Wellcome Fund, National Cancer Institute (K22CA151638), V Foundation for Cancer Research, Melanoma Research Foundation, Melanoma Research Alliance, American Skin Association, Joint Center for Translational Medicine, Sidney Kimmel Foundation, Stand Up to Cancer, Eli & Edythe Broad Center of Regenerative Medicine & Stem Cell Research, the Wesley Coyle Memorial Fund, Ian Copeland Melanoma Fund, Ruby Family Foundation, Louis Belley and Richard Schnarr Fund, and The Seaver Institute. R.F.K and G.V.L are supported by Program Grant No. 402761 from the National Health and Medical Research Council of Australia, Translational Research Program Grant No. 05/TPG/1-01 from the Cancer Institute New South Wales (CINSW). J.A.S. is supported by National Cancer Institute (K24CA097588) and American Cancer Society Melanoma Professorship. We are grateful to every patient volunteer who donated tissue(s) for this study.


Author Contributions H.S., G.M., X.K., M-K.L., H.L. designed, performed experiments and analyzed data. R.C.K., C.N., T.C. R.A.S., K.D., J.A.S., R.F.K., G.V.L., A.R., and R.S.L. recruited patient volunteers and/or provided reagents/tissues. H.S., G.M., X.K, M-K.L., H.L., J.A.S., R.F.K., G.V.L., S.F.N. and A.R. contributed to manuscript preparation. R.S.L. designed experiments and research aims, analyzed data, and wrote the paper.

Supplementary Information accompanies this paper at

Accession codes: Sequence data are archived at the NCBI Sequence Read Archive (SRA) under the accession code SRP010266.

Competing financial interests: A.R. and R.S.L. are the authors of patent application under PCT Application Serial No. PCT/US11/061552 (Compositions and methods for detection and treatment of B-RAF inhibitor-resistant melanomas)


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