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Integrated molecular drivers coordinate biological and clinical states in melanoma

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Abstract

We performed harmonized molecular and clinical analysis on 1,048 melanomas and discovered markedly different global genomic properties among subtypes (BRAF, (N)RAS, NF1, triple wild-type (TWT)), subtype-specific preferences for secondary driver genes and active mutational processes previously unreported in melanoma. Secondary driver genes significantly enriched in specific subtypes reflected preferential dysregulation of additional pathways, such as induction of transforming growth factor-β signaling in BRAF melanomas and inactivation of the SWI/SNF complex in (N)RAS melanomas, and select co-mutation patterns coordinated selective response to immune checkpoint blockade. We also defined the mutational landscape of TWT melanomas and revealed enrichment of DNA-repair-defect signatures in this subtype, which were associated with transcriptional downregulation of key DNA-repair genes, and may revive previously discarded or currently unconsidered therapeutic modalities for genomically stratified melanoma patient subsets. Broadly, harmonized meta-analysis of melanoma whole exomes revealed distinct molecular drivers that may point to multiple opportunities for biological and therapeutic investigation.

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Fig. 1: Identification of consensus driver genes in melanoma.
Fig. 2: Melanoma genomic subtypes have distinct global properties and secondary driver genes.
Fig. 3: SMGs exclusive to BRAF melanomas have implications for immunotherapy, and secondary drivers further segregate with p.Val600Glu– and p.Val600Lys–encoding hotspot mutations.
Fig. 4: (N)RAS melanomas frequently experience clonal mutations in the PBAF complex, and PBAF complex mutations are associated with improved OS and PFS when treated with immunotherapy.
Fig. 5: Identification of new drivers and enrichment of mutational signature 3 in TWT melanomas.
Fig. 6: Identification of new drivers and enrichment of mutational signature 3 in TWT melanomas.

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Data availability

All of the datasets used in the present study are publicly available. The raw sequence data can be obtained through dbGaP (https://www.ncbi.nlm.nih.gov/gap) and the ICGC Data Access Compliance Office (https://icgc.org/daco), or as described in the original papers (Supplementary Table 1). The accession codes can also be found in Supplementary Table 1. Publicly available databases used in the present study include MSigDB v.6.2 (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), ClinVar (https://www.ncbi.nlm.nih.gov/clinvar), ExAC (http://exac.broadinstitute.org), gnomAD (https://gnomad.broadinstitute.org), the Broad Institute Single Cell portal (https://singlecell.broadinstitute.org/single_cell) and ConsensusPathDB v.34 (http://cpdb.molgen.mpg.de).

Code availability

All software and bioinformatic tools used in the present study are publicly available.

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Acknowledgements

This work was supported by the National Cancer Institute (grant no. F31CA239347 to J.R.C.), National Institutes of Health (grant nos. 5T32HG002295-15 to J.R.C., and R01CA227388-02 and R21CA242861 to E.M.V.A.), and the Damon Runyon Clinical Investigator Award (to E.M.V.A.). F.D. was supported by the Claudia Adams Barr Program for Innovative Cancer Research and the AWS Cloud Credits for Research Program. The results presented in the present study are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). This publication and the underlying research are partly facilitated by Hartwig Medical Foundation and the Center for Personalized Cancer Treatment which have generated, analyzed and made available data for this research. We thank Petra Ross-Macdonald for providing clinical outcomes data used for the immunotherapy response validation analyses.

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Authors and Affiliations

Authors

Contributions

J.R.C., A.T.-W., S.A., F.D., B.R. and M.X.H. contributed to the analysis of genomic data. J.R.C., C.A.M., B.S., D.S., D.L. and E.M.V.A. contributed to the aggregation of raw sequence data. N.V., T.K., D.L. and E.M.V.A. contributed to analysis and data interpretation of the immunotherapy response. J.R.C., A.T.-W., S.A., N.V., F.D., B.R., J.L.W., R.H., F.S.H., B.S., D.S., D.L. and E.M.V.A. contributed to the interpretation of results and preparation of the manuscript.

Corresponding author

Correspondence to Eliezer M. Van Allen.

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Competing interests

E.M.V.A. is a consultant for Tango Therapeutics, Genome Medical, Invitae, Enara Bio, Monte Rosa Therapeutics, Manifold Bio and Janssen, provides research support to Novartis and Bristol-Myers Squibb, has equity in Tango Therapeutics, Genome Medical, Syapse, Enara Bio, Monte Rosa Therapeutics and Microsoft, receives travel reimbursement from Roche/Genentech, and has institutional patents filed on chromatin mutations and immunotherapy response, and methods for clinical interpretation.

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Extended data

Extended Data Fig. 1 Overlap between SMGs from the entire cohort and subtype analyses.

a, Overlap between the subtype-specific SMGs and the SMGs that were identified via the entire cohort (M1000). Most of the SMGs identified in the entire cohort analysis were not identified through the subtype specific analysis (115 of 178, 64.6%).

Extended Data Fig. 2 MECOM/BMP5 immunotherapy validation (overall survival and RECIST response).

External validation analysis of overall survival for MECOM/BMP5 mutations using the Roh, Riaz, Hugo, and Rodig whole-exome cohorts (n = 194 total) for a, all melanomas, b, BRAF melanomas, and c, non-BRAF melanomas, excluding post treatment biopsies. These cohorts were chosen because they were immunotherapy treated, whole-exome sequenced, cohorts not included in our discovery cohort. Due to the diverse treatment regimens in each of these trials and cohorts, we were unable to correct for drug. Further, since we did not have access to raw sequencing data from all these studies, we could not calculate and correct for tumor purity and utilized published variant calls. The hazard rate ratios of MECOM/BMP5 mutations when correcting for only mutational load was (a) 0.59 (multivariate Cox proportional-hazards, p = 0.09) for all melanomas, (b) 0.46 (multivariate Cox proportional-hazards, p = 0.16) for BRAF melanomas, and (c) 0.68 (multivariate Cox proportional-hazards, p = 0.31) for non-BRAF melanomas. These results are similar to what was observed in the discovery cohort (Supplementary Table 8), although this validation cohort size was not powered to achieve statistical significance. d, The association between the BRAF subtype and MECOM/BMP5 mutations for clinical benefit to immunotherapy (via RECIST) in our limited validation cohort was similar to our discovery cohort findings, but not statistically significant. The p-values shown in a-c) are derived from the log-rank test.

Extended Data Fig. 3 PBAF complex immunotherapy validation (overall survival and RECIST response).

External validation analysis of overall survival for PBAF mutations using the Roh, Riaz, Hugo, and Rodig cohorts (n = 194), which are immunotherapy treated, whole-exome sequenced, cohorts not included in our discovery cohort. a, Survival curves between PBAF-mutants and non-PBAF mutants. b, Survival curves between PBAF-mutants and non-PBAF mutants where PBAF mutants are classified by having mutations in ARID2, PBRM1, SMARCA4, and SMARCB1, which are the 4 PBAF complex genes commonly used in clinical sequencing panels. This limited validation cohort lacked sufficient samples with co-mutation of (N)RAS and PBAF complex genes (n = 9), and thus validation analysis was only performed on all tumors. Due to the unique treatment regimens in each of these cohorts, we were unable to correct for drug. Further, because we did not have access to raw sequencing data from these studies, we could not calculate and correct for tumor purity. When correcting only for mutational load the hazard ratio of PBAF mutations in the whole-exome cohorts, (a) when considering all genes in the PBAF complex, was 1.07 (multivariate Cox proportional-hazards, p = 0.80). The differences in these findings relative to the primary larger cohort may indicate differences in patient population and study size relative to our discovery cohort. (b) When considering only mutations in ARID2, PBRM1, SMARCA4, and SMARCB1 as PBAF-mutant, the HRR was 0.86 (multivariate Cox proportional-hazards, p = 0.61). The p-values for a-b) are derived from the log-rank test.

Extended Data Fig. 4 NMF validation of deconstructSigs results on genomic subtypes via SomaticSignatures.

a, NMF statistics for BRAF melanomas. b, Cosine similarity between COSMIC signatures and signatures decomposed via NMF for BRAF melanomas. c, NMF statistics for (N)RAS melanomas. d, Cosine similarity between COSMIC signatures and signatures decomposed via NMF for (N)RAS melanomas. e, NMF statistics for NF1 melanomas. f, Cosine similarity between COSMIC signatures and signatures decomposed via NMF for NF1 melanomas. g, NMF statistics for TWT melanomas. h, Cosine similarity between COSMIC signatures and signatures decomposed via NMF for TWT melanomas. The cophenetic correlation coefficient and residual sum of squares (RSS) suggests 3 is the optimal number of signatures for each genomic subtype.

Extended Data Fig. 5 NMF simulations via SomaticSignatures on TWT melanomas removing 35 random non-signature 3 samples each simulation.

A total of 35 signature 3 samples were identified via deconstructSigs in our signature analysis. To ensure that our NMF validation in TWT melanomas (Supplementary Fig. 17) is actually identifying signature 3 because it is indeed present, and not because it’s a flat signature, we performed 1000 simulations removing 35 random non-signature 3 samples each time. Signature 3 was identified 927 times (92.7%), which corroborates the deconstructSigs results and suggests signature 3 is the third most dominant signature in TWT melanomas. Performing 1000 simulations when removing the 35 signature 3 samples each time never yielded the identification of signature 3 via NMF.

Extended Data Fig. 6 DSB repair deficiency - unweighted sum of HRD associated CNA events.

a, Distribution of the unweighted sum of HRD associated CNA events (loss of heterozygosity, telomeric allelic imbalance, large scale transitions) in signature 3 (yellow) and non-signature 3 (purple) melanomas in the entire cohort. Signature 3 tumors were significantly enriched in HRD associated copy number events via a Mann-Whitney U test (p = 6.21 × 10−5, two-sided). b, Density plot of HRD associated copy number events in the entire cohort. c, Distribution of HRD associated copy number events in signature 3 and non-signature 3 melanomas in TWT melanomas (Mann-Whitney U, p = 5.49 × 10−3, two-sided). d, Density plot of HRD associated copy number events in the TWT melanomas. In (a) and (c) the data is represented as a boxplot where the middle line is the median, the lower and upper edges of the box are the first and third quartiles, the whiskers represent the interquartile range (IQR) multiplied by 1.5, and beyond the whiskers are outlier points.

Extended Data Fig. 7 Indel mutational signatures on the 390 WGS tumors.

Cosine similarity between COSMIC indel mutational signatures and the suggested solution NMF results from SigProfileExtractor. Indel mutational signatures revealed that a, BRAF, b, (N)RAS, and c, NF1 melanomas were associated with indel signatures ID1, ID2 and ID13 (associated with UV), while d, TWT melanomas were associated with indel signatures ID1, ID8 (associated with NHEJ), and ID13. e, Mutational signature 3 was associated with indel signatures ID1 and ID8, and was the sole mutational signature associated with ID8. f, Interestingly, when removing signature 3 tumors from the TWT melanoma cohort, TWT melanomas were still associated with indel signature ID8. Thus, the increased genomic instability of TWT melanomas in general is enough to result in ID8.

Extended Data Fig. 8 Comparison of transcriptional profiles between DSB repair deficient and DSB repair intact TWT melanomas.

a, The workflow used to identify transcriptional differences between putative DSB repair deficient (presence of signature 3) and non-DSB repair deficient (no contribution of signature 3) TWT tumors. b, Pearson correlation between signature 3 contribution and normalized gene expression in TWT melanomas (Methods) identified 9 positive and 10 negative signifi- cant correlations for DNA-repair genes (Pearson’s, p-value cutoff < 0.05; Methods). Genes highlighted in purple function in DSB repair pathways, including HR. Opacity was used to show the density of non-significant points along both axes.

Extended Data Fig. 9 Differential expression analysis between signature 3 and non-signature 3 TWT melanomas.

a, DESeq2 log2 fold-change vs edgeR log2 fold-change for cumulative set of DNA-repair genes. b, Significance vs log2 fold-change of significantly differentially expressed DNA repair genes as determined by DESeq2. Yellow points indicate genes whose expression was significantly correlated with signature 3 contribution and significantly differentially expressed. Green points indicate genes that were only significantly differentially expressed. Genes highlighted in purple function in DSB repair. Opacity was used to show the density of non-significant points along both axes.

Extended Data Fig. 10 Methylation and signature 3 contribution.

a, Pearson correlation between signature 3 contribution and methylation β-values plotted on the x-axis vs. difference in median methylation between signature 3 and non-signature 3 TWT samples on the y-axis. Six probe sites were significantly correlated with signature 3 contribution, had a significant difference in median β-values (via Mann-Whitney U), and had methylation β-values significantly associated with gene expression. Of the six probe sites, INO80 was the only gene involved in HR repair. Opacity was used to show the density of non-significant points along both axes. b, Expression of INO80 was significantly correlated with methylation β-values at INO80-ch.15.415873F (Pearson’s, r = −0.51, p = 8.516 × 10−5). Points in yellow are from signature 3 TWT samples and points in purple are from non-signature 3 TWT samples.

Supplementary information

Supplementary Information

Supplementary Figs. 1–31 and Note

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–13

Supplementary Data 1

Complete list of all somatic mutations in this cohort (MAF file).

Supplementary Data 2

MutSigCV2, OncodriveFML and MutPanning results for entire cohort and genomic subtypes (including V600E and V600K); q values are Benjamini–Hochberg-corrected P values.

Supplementary Data 3

GISTIC2.0 amplifications and deletions thresholded by gene results.

Supplementary Data 4

DeconstructSigs signature contributions for whole cohort and validation set.

Supplementary Data 5

Differential expression results between signature 3 and nonsignature 3 TWT melanomas; padj is the Benjamini–Hochberg-corrected P value.

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Conway, J.R., Dietlein, F., Taylor-Weiner, A. et al. Integrated molecular drivers coordinate biological and clinical states in melanoma. Nat Genet 52, 1373–1383 (2020). https://doi.org/10.1038/s41588-020-00739-1

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