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. 2015 Jun 18;161(7):1681-96.
doi: 10.1016/j.cell.2015.05.044.

Genomic Classification of Cutaneous Melanoma

Cancer Genome Atlas Network
Collaborators

Genomic Classification of Cutaneous Melanoma

Cancer Genome Atlas Network. Cell. .

Abstract

We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multi-dimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making.

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Figures

Figure 1
Figure 1. Landscape of Driver Mutations in Melanoma
(A) Total number of mutations, age at melanoma accession, and mutation subtype (BRAF, RAS [N/H/K], NF1, and Triple-WT) are indicated for each sample (top). (Not shown are one hyper-mutated and one co-occurring NRAS BRAF hot-spot mutant). Color-coded matrix of individual mutations (specific BRAF and NRAS mutations indicated) (middle), type of melanoma specimen (primary or metastasis), and mutation spectra for all samples (bottom) are indicated. For the two samples with both a matched primary and metastatic sample, only the mutation information from the metastasis was included. (B) BRAF mutations that co-occur with RAS family member and NF1 mutations are illustrated across the BRAF protein. (C) Fraction of BRAF V600/K601E and non-V600/K601E co-occurring with the RAS (N/H/K), NF1, NF1/RAS (N/H/K) combined cohort and no NF1/RAS (N/H/K) mutations. (D) NF1 mutations found in melanoma whole-exome sequencing data across the NF1 protein. (E) Fraction of NF1 missense and truncating mutations co-occurring with RAS hot-spot or non-BRAF/RAS hot-spot mutations. (Mut, mutation). See also Figure S1.
Figure 2
Figure 2. Landscape of Copy-Number Alterations in Melanoma
(A) GISTIC 2 analysis across four subtypes with selected highlighted genes from significant minimal common regions. (B) Fraction of BRAF, RAS (N/H/K), NF1, and Triple-WT subtypes with focal amplifications determined by GISTIC 2 for BRAF and MITF (left) and KIT, PDGFRA, KDR, MDM2, CDK4, CCND1, and TERT (right). Asterisk indicates significant increase in amplification in the indicated mutation subtype compared to the rest by Fisher’s exact test (p < 0.01, FDR < 0.05). (C) Landscape of mutation subtypes, selected cosmic mutations, and subtype-specific enriched copy-number amplifications. Per sample mutation rate, age, and mutation subtype (BRAF, RAS, NF1, and Triple-WT) (top), color-coded matrix of individual mutations and amplifications (specific BRAF and NRAS mutations indicated) (middle), and type of melanoma specimen (primary or metastasis) and mutation spectra for all samples (bottom) are shown. See also Figure S2.
Figure 3
Figure 3. Analysis of Protein Expression Levels in Melanoma Samples
Individual protein levels were determined by RPPA across mutation subtypes. (A) Phospho-MAP2K1/MAP2K2 (MEK1/2) S217/S221 was elevated in both the BRAF and RAS hot-spot mutation subtypes compared to NF1 and Triple-WT. (B) Only RAS hot-spot mutant samples showed higher median levels of phospho-T202 Y204 MAPK1/MAPK3 (ERK1/2). (C) Triple-WT melanomas had the highest median KIT protein expression. (D and E) (D) NF1 mutant melanomas had a higher median level of CRAF expression, and Triple-WT had higher BCL-2 levels (E) compared to BRAF and RAS subtypes. (F) Median IGFBP2 levels were highest in BRAF hot-spot mutant samples. Kruskal-Wallis test, and the post hoc Kruskal Nemenyi test for pairwise comparisons. *p < 0.05, **p < 0.01, ***p < 0.005, ****p < 0.001, #p = 5.4e–36. See also Figure S3.
Figure 4
Figure 4. Pathways Altered in Melanoma
(A) Percentage of recurrently altered pathways in the four melanoma subtypes (BRAF = V600/K601 mutants, RAS [N/H/K] = G12, G13, and Q61 mutants) through integration of mutation, copy-number variation, and hypermethylation data are indicated (n = 316; not shown are one hyper-mutated and one co-occurring BRAF/NRAS hot-spot mutant sample). Manual curated pathway shows percentage of TP53, CDKN2A/RB1, and MAPK/AKT pathway across all samples (note: percentages of alterations of MAPK and AKT pathway are combined, given their high level of interconnectivity). a, amplification; d, deletion, m, mutation. (B) Co-occurring somatic CNAs, mutations, and mRNA expression (color code indicated on graph) for the PI(3)K/mTOR pathway across the four mutation subtypes (left). Bar graph indicating percentage of fraction of subtypes with AKT3 activation or PTEN inactivation (right). Enrichment of a given alteration in a subgroup is estimated by Fisher’s exact test. See also Figure S4.
Figure 5
Figure 5. Integrative Analysis across Multiple Molecular Data Platforms Provides Insights into the Biology and Prognostic Significance of Immune Infiltrates in Cutaneous Melanoma
(A and B) (A) Unsupervised clustering of 329 melanoma samples using the top 1,500 genes showing the maximum absolute deviation identify three clusters defined as ‘‘immune-high,” ‘‘keratin-high,” and ‘‘microphthalmia-associated transcription factor (MITF)-low” based on gene function of discriminatory mRNAs and (B) post-accession survival curves for RNA subgroups. (C) Distribution of lymphocytic scores determined by histopathology analysis according to sample type (described in detail in the Supplemental Experimental Procedures). (D) Post-accession survival curves for high and low lymphocytic infiltration scores. (E) Overlap of LCK high and low protein expression obtained from RPPA data with lymphocytic infiltration scores determined by pathology and RNA immune subgroups determined by mRNA clustering analysis. (F) Association of LCK protein with post-accession survival. Three curves describe cumulative survival rates of three tertile patient subsets (p = 0.007 with log-rank test). See also Figures S5, S6, and S7 and Data S1.
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