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Melanoma addiction to the long non-coding RNA SAMMSON


Focal amplifications of chromosome 3p13–3p14 occur in about 10% of melanomas and are associated with a poor prognosis. The melanoma-specific oncogene MITF resides at the epicentre of this amplicon1. However, whether other loci present in this amplicon also contribute to melanomagenesis is unknown. Here we show that the recently annotated long non-coding RNA (lncRNA) gene SAMMSON is consistently co-gained with MITF. In addition, SAMMSON is a target of the lineage-specific transcription factor SOX10 and its expression is detectable in more than 90% of human melanomas. Whereas exogenous SAMMSON increases the clonogenic potential in trans, SAMMSON knockdown drastically decreases the viability of melanoma cells irrespective of their transcriptional cell state and BRAF, NRAS or TP53 mutational status. Moreover, SAMMSON targeting sensitizes melanoma to MAPK-targeting therapeutics both in vitro and in patient-derived xenograft models. Mechanistically, SAMMSON interacts with p32, a master regulator of mitochondrial homeostasis and metabolism, to increase its mitochondrial targeting and pro-oncogenic function. Our results indicate that silencing of the lineage addiction oncogene SAMMSON disrupts vital mitochondrial functions in a cancer-cell-specific manner; this silencing is therefore expected to deliver highly effective and tissue-restricted anti-melanoma therapeutic responses.

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Figure 1: Gene amplification and SOX10-mediated transcription drives SAMMSON expression in melanoma.
Figure 2: SAMMSON is required for melanoma growth and survival.
Figure 3: SAMMSON interacts with p32 to increase its mitochondrial localization and function.
Figure 4: Therapeutic potential of SAMMSON targeting in vivo.

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Primary accessions

Gene Expression Omnibus

Data deposits

Mass spectrometry data have been deposited in the Proteomics Identifications Database under accession numbers PXD002565. Gene expression data have been deposited in the Gene Expression Omnibus under accession number GSE70180.


  1. Garraway, L. A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005)

    ADS  CAS  Article  Google Scholar 

  2. Gembarska, A. et al. MDM4 is a key therapeutic target in cutaneous melanoma. Nature Med. 18, 1239–1247 (2012)

    CAS  Article  Google Scholar 

  3. Verfaillie, A. et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nature Commun . 6, 6683 (2015)

    ADS  CAS  Article  Google Scholar 

  4. Harris, M. L., Baxter, L. L., Loftus, S. K. & Pavan, W. J. Sox proteins in melanocyte development and melanoma. Pigment Cell Melanoma Res. 23, 496–513 (2010)

    CAS  Article  Google Scholar 

  5. Laurette, P. et al. Transcription factor MITF and remodeller BRG1 define chromatin organisation at regulatory elements in melanoma cells. eLife 4, 06857 (2015)

    Article  Google Scholar 

  6. Flaherty, K. T. et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363, 809–819 (2010)

    CAS  Article  Google Scholar 

  7. McHugh, C. A. et al. The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3. Nature 521, 232–236 (2015)

    ADS  CAS  Article  Google Scholar 

  8. Chu, C. et al. Systematic discovery of Xist RNA binding proteins. Cell 161, 404–416 (2015)

    CAS  Article  Google Scholar 

  9. Amamoto, R. et al. Mitochondrial p32/C1QBP is highly expressed in prostate cancer and is associated with shorter prostate-specific antigen relapse time after radical prostatectomy. Cancer Sci. 102, 639–647 (2011)

    CAS  Article  Google Scholar 

  10. Fogal, V. et al. Mitochondrial p32 protein is a critical regulator of tumor metabolism via maintenance of oxidative phosphorylation. Mol. Cell. Biol. 30, 1303–1318 (2010)

    CAS  Article  Google Scholar 

  11. Fogal, V. et al. Mitochondrial p32 is upregulated in Myc expressing brain cancers and mediates glutamine addiction. Oncotarget 6, 1157–1170 (2015)

    Article  Google Scholar 

  12. Fogal, V., Zhang, L., Krajewski, S. & Ruoslahti, E. Mitochondrial/cell-surface protein p32/gC1qR as a molecular target in tumor cells and tumor stroma. Cancer Res. 68, 7210–7218 (2008)

    CAS  Article  Google Scholar 

  13. Muta, T., Kang, D., Kitajima, S., Fujiwara, T. & Hamasaki, N. p32 protein, a splicing factor 2-associated protein, is localized in mitochondrial matrix and is functionally important in maintaining oxidative phosphorylation. J. Biol. Chem. 272, 24363–24370 (1997)

    CAS  Article  Google Scholar 

  14. Yagi, M. et al. p32/gC1qR is indispensable for fetal development and mitochondrial translation: importance of its RNA-binding ability. Nucleic Acids Res. 40, 9717–9737 (2012)

    CAS  Article  Google Scholar 

  15. Hu, M. et al. p32 protein levels are integral to mitochondrial and endoplasmic reticulum morphology, cell metabolism and survival. Biochem. J. 453, 381–391 (2013)

    CAS  Article  Google Scholar 

  16. Matos, P. et al. A role for the mitochondrial-associated protein p32 in regulation of trophoblast proliferation. Mol. Hum. Reprod. 20, 745–755 (2014)

    CAS  Article  Google Scholar 

  17. Li, Y., Wan, O. W., Xie, W. & Chung, K. K. K. p32 regulates mitochondrial morphology and dynamics through parkin. Neuroscience 199, 346–358 (2011)

    CAS  Article  Google Scholar 

  18. Jiao, H. et al. Chaperone-like protein p32 regulates ULK1 stability and autophagy. Cell Death Differ. 22, 1812–1823 (2015)

    CAS  Article  Google Scholar 

  19. Richter-Dennerlein, R., Dennerlein, S. & Rehling, P. Integrating mitochondrial translation into the cellular context. Nature Rev. Mol. Cell Biol. 16, 586–592 (2015)

    Article  Google Scholar 

  20. Chacinska, A., Koehler, C. M., Milenkovic, D., Lithgow, T. & Pfanner, N. Importing mitochondrial proteins: machineries and mechanisms. Cell 138, 628–644 (2009)

    CAS  Article  Google Scholar 

  21. Wang, X. & Chen, X. J. A cytosolic network suppressing mitochondria-mediated proteostatic stress and cell death. Nature 524, 481–484 (2015)

    ADS  CAS  Article  Google Scholar 

  22. Wrobel, L. et al. Mistargeted mitochondrial proteins activate a proteostatic response in the cytosol. Nature 524, 485–488 (2015)

    ADS  CAS  Article  Google Scholar 

  23. Richter, U. et al. A mitochondrial ribosomal and RNA decay pathway blocks cell proliferation. Curr. Biol. 23, 535–541 (2013)

    CAS  Article  Google Scholar 

  24. Fantin, V. R. & Leder, P. Mitochondriotoxic compounds for cancer therapy. Oncogene 25, 4787–4797 (2006)

    CAS  Article  Google Scholar 

  25. Vazquez, F. et al. PGC1α expression defines a subset of human melanoma tumors with increased mitochondrial capacity and resistance to oxidative stress. Cancer Cell 23, 287–301 (2013)

    CAS  Article  Google Scholar 

  26. Haq, R. et al. Oncogenic BRAF regulates oxidative metabolism via PGC1α and MITF. Cancer Cell 23, 302–315 (2013)

    CAS  Article  Google Scholar 

  27. Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem. Sci. 35, 427–433 (2010)

    CAS  Article  Google Scholar 

  28. Dang, C. V. Links between metabolism and cancer. Genes Dev. 26, 877–890 (2012)

    CAS  Article  Google Scholar 

  29. Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999)

    CAS  Article  Google Scholar 

  30. Mestdagh, P. et al. A novel and universal method for microRNA RT–qPCR data normalization. Genome Biol. 10, R64 (2009)

    Article  Google Scholar 

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GapmeRs were designed by J. Lai. This work was supported by the Fonds Wetenschappelijk Onderzoek (FWO; #G.0646.14N and #3G056613), Foundation Against Cancer (STK#2014-126), UGent-IOF (F2013/IOF-Advanced/676) and STK grant F/2014/376. The PDX studies were funded by GOA/14/012 (KULeuven) and KPC_29_005 (Belgian Ministries of Health). The authors wish to thank N. Samyn for help with MS, M. Van Gele for providing NHME cultures and P. Wolter for scientific discussions and his central role in the establishment of the melanoma PDX platform. E.L. is a recipient of a postdoctoral fellowship from the Marie-Curie/VIB OMICS program. M.S. is the recipient of EMBO fellowship (ALTF 648-2013). F.A. is a senior researcher from the Research Fund Flanders (FWO). P.M. and R.V. are recipients of FWO postdoctoral and PhD fellowships, respectively. D.L. is supported by the Fonds National de la Recherche (FRS/FNRS). I.D. is supported by the Institut National du Cancer PAIR melanoma (MELA13-002), the “France Génomique” consortium (ANR10-INBS-09-08), and ANR-10-LABX-0030-INRT. The I.D. laboratory is an “équipe labellisée Ligue Nationale contre le Cancer”.

Author information




E.L. and R.V. performed most experiments. M.S. performed the experiments described in Fig.3f. P.L., S.A. and I.D. provided ChIP-seq data. J.W. and J.v.d.O. provided melanoma clinical samples. J.v.d.O. and E.R. provided histopathology support. S.E. and K.G. performed the mass spectrometric measurement and analysis. C.L., K.V., S.V.A. and P.M. performed copy number variant analysis and profiling experiments and interpretation of the data. A.R., E.H. and F.A. provided support with the PDX models. P.B. provided technical support for the electron microscopy. P.M., M.F. and S.A. provided bioinformatics support. D.L.J.L., B.d.S., P.M. and J.V. helped with the interpretation of the data. J.-C.M. and E.L. designed most of the experiments and wrote the manuscript.

Corresponding author

Correspondence to Jean-Christophe Marine.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 SAMMSON is a lineage-specific lncRNA expressed in the vast majority of melanomas.

a, SAMMSON is a polyadenylated and multi-exonic lncRNA that contains one additional exon (in red) downstream of the four GENECODE19-annotated exons (blue). For each melanoma (SKCM) sample in the TCGA, the mapped RNA-seq data were converted into a coverage plot. The coverage data are normalized for library size and log1p transformed. Subsequently, the coverage data for all primary (SKCM.01) and metastatic samples (SKCM.06) in the region chromosome 3:70,040,000–70,140,000 is plotted as two heat maps. b, Cap analysis of gene expression-sequencing(CAGE-seq), RNA-seq data and RT–qPCR analyses from short-term melanoma cultures (MM lines) confirmed that SAMMSON is not a read-through transcript from the upstream MITF locus. CAGE-seq counts as defined by the FANTOM5 mammalian promoter expression atlas for two melanoma cell lines at the SAMMSON locus (panels 1 and 2), location of the a 3′ rapid amplification of cloned/cDNA ends (RACE) fragment for SAMMSON (panel 3) and RNA-seq counts from a primary melanoma tumour in the SAMMSON locus. c, SAMMSON and MITF copy number as measured by qPCR in short-term melanoma cultures and melanoma cell lines. Reference human genomic DNA was used as scaling control. Error bars represent s.d. of qPCR replicates (n = 2). A significant correlation between MITF and SAMMSON copy number was observed (bottom; Spearman’s rank rho = 0.933, P < 0.001). d, Expression of SAMMSON in human short-term melanoma (MM) and NHME cultures relative to the expression average of three housekeepings (left) and correlation with MITF expression by western blot (right; for gel source data, see Supplementary Fig. 1). Error bars represent s.d. of three replicates (n = 3). e, Expression correlation between MITF-M and SAMMSON in melanoma clinical samples from the TCGA database. f, Read counts were generated from RNA-seq data from TCGA melanoma samples (SKCM) and normalized to the library size. Samples were subdivided into proliferative and invasive groups as described previously3 and box plots were generated for SAMMSON, ZEB1 and MITF. Differential expression analysis between the proliferative and invasive groups was done using edgeR50. lfc, log fold change; pval, uncorrected P value; fdr, false-discovery-rate-corrected pval. g, Relative expression in 60 cancer cell lines (NCI60 panel). h, Fraction of lncRNA expression variation (n = 1,472) across the NCI60 panel by cancer type according to a generalized additive model (GAM).

Extended Data Figure 2 SAMMSONexpression in melanoma, but not other cancer, cell lines, is at least partly SOX10-dependent.

a, H3K27ac ChIP-seq data generated in house using a series of short-term melanoma cultures2 were integrated with cancer cell lines data retrieved from ENCODE. A clear H3K27ac peak is present upstream SAMMSON in all, but one (MM001), melanoma lines. No peak is detected in the vast majority (19/20) of non-melanoma cancer cell lines, of which 9 are shown. b, UCSC screenshots of ChIP-seq data for SOX10, 3HA-MITF and H3K27ac at the MITF and SAMMSON loci in Mel501. c, ChIP-qPCR of endogenous SOX10 in 501Mel cells at the indicated loci. The IgG antibody was used as a negative control. SOX10 recruitment to its well-established targets GPR110, TYR and SOX10 itself, but not to a non-SOX10 target PRM1, confirms the specificity of the SOX10 ChIP experiment. c, Western blotting analysis of total protein lysates of SK-MEL-28 confirming efficient knockdown of SOX10 and MITF. GAPDH was used as a loading control (for gel source data, see Supplementary Fig. 1). e, Fold change RNA expression levels in 501Mel cells transfected with a control siRNA pool (siCtrl) or pools targeting MITF (siMITF) or SOX10 (siSOX10).

Extended Data Figure 3 SAMMSON promotes the in vitro growth and survival of human melanomas.

a, Colony formation assays 7 days after seeding of metastatic melanoma cells transfected with a GapmeR control (Ctrl), GapmeR3 and GapmeR11. b, Evaluation of cell death by co-staining for annexin V and propidium iodide (PI) followed by flow cytometric analysis. The graph is an average of three biological replicas and shows the percentage of cells alive, single positive or double positive. c, Efficiency of SAMMSON knockdown using an siRNA against SAMMSON. The expression of SAMMSON is relative to the average of three different housekeeping genes. d, Percentage of remaining living cells upon siSAMMSON, measured by flow cytometry, is indicated on y-axis ± s.e.m. e, Evaluation of the capacity of exogenus SAMMSON and SAMMSON mutants (SAMMSONgap3mut and SAMMSONgap11mut, in which mismatches were introduced into the GapmeR3 and GapmeR11 target sequences) to rescue cell death in SK-MEL-28 treated with GapmeRs. The percentage of remaining living cells is indicated on the y-axis ± s.e.m. f, Effect of a caspase-9 inhibitor on caspase-3/7 activation in SK-MEL-28 treated with GapmeRs. The graph is an average of three biological replicas; caspase-3/7 activity is relative to control sample (Ctrl) ± s.e.m. P value was calculated by ANOVA. g, Relative SAMMSON expression in MM001 cells transfected with an empty or SAMMSON-encoding expression vector of three different biological replicates ± s.e.m. P values were calculated by ANOVA. h, Colony formation assays 7 days after seeding 1,000, 5,000 or 10,000 MM001 as described in g. i, Colony formation assays 7 days after seeding of SK-MEL-28 transfected with a control GapmeR (Ctrl) or GapmeR3 and exposed to either vehicle or an EC50 dose of vemurafenib (PLX4032) or pimasertib.

Extended Data Figure 4 SAMMSON does not regulate MITF expression in cis.

a, Relative expression of MITF as determined by microarray gene expression profiling in the indicated melanoma cell lines treated with GapmeR3 (SAMMSON knockdown) (n > 3) or expressing exogenous SAMMSON (n = 4); no significant differences in MITF expression were observed (limma, Benjamini–Hochberg adjusted P > 0.05), except in MM034, in which SAMMSON knockdown resulted in a 1.5-fold downregulation of MITF (limma, Benjamini–Hochberg adjusted P = 0.013). b, Validation of the array data in a by qPCR for SAMMSON (left) and MITF-M (right) in all the cell lines used for the arrays. Expression is relative to three different housekeeping genes. The graph shows an average of three different biological replicas ± s.e.m. The MITF-M protein levels were assessed by western blotting (bottom; for gel source data, see Supplementary Fig. 1).

Extended Data Figure 5 a, SAMMSON localizes primarily to the cytoplasm and largely co-localizes with mitochondria.

Quantification of SAMMSON in nuclei and cytoplasm of SK-MEL-28. Data are expressed as nuclear/cytoplasmic ratio ± s.e.m. Data are shown for MALAT1 (exclusively nuclear) and TBP (cytoplasmic). The graph shows an average of three different fractionation experiments. b, SAMMSON RNA-FISH in a panel of melanoma cell lines and NHMEs. SAMMSON is shown in yellow and DAPI in blue. c, Quantification of SAMMSON RNA-FISH results described in b. Number of fluorescent spots in total per cell, nucleus and cytoplasm of MM057 (n = 10), MM087 (n = 10) and SK-MEL-28 (n = 7) melanoma cells are shown. d, Quantification of SAMMSON in cytoplasm, mitochondria and mitoplasts of SK-MEL-28. Data are expressed as fraction/total ratio ± s.e.m. Data are shown for mitochondrial 16S rRNA (exclusively mitochondrial) and TBP (cytoplasmic). The graph is an average of three different fractionation experiments. e, The purity of the fractions was assessed by western blotting using nuclear (UBF1), cytoplasmic (β-actin) and mitochondrial markers (HSP60 and VDAC1; for gel source data, see Supplementary Fig. 1).

Extended Data Figure 6 A large fraction of cytoplasmic SAMMSON co-localizes with mitochondria.

SAMMSON and mitochondrial 16S rRNA RNA-FISH in four different melanoma cell lines. SAMMSON probes, labelled with Quasar570, are shown in red and 16S rRNA probes, labelled with Quasar670, are shown in yellow; DAPI is in cyan.

Extended Data Figure 7 RAP–MS identifies the mitochondrial protein p32 as a SAMMSON interactor.

a, The ‘Metrics’ table provides an overview of the MS experiment. For every MS analysis, the table contains the number of analysed spectra, the total number of identified spectra (peptide to spectra matches (PSMs)), the number of distinct peptide sequences, protein numbers and a false discovery rate (FDR) estimation based on searches against a reversed database. b, Western blot for p32 in NHMEs and in a panel of short-term melanoma cultures. Vinculin is used as a loading control and normalizer for the quantification. c, RNA-FISH for 16S rRNA (in red) and immunofluorescence for p32 (in yellow) in NHMEs and in a panel of melanoma cell lines. DAPI is in cyan. d, Pulldown of SAMMSON (and HRPT) under native conditions and upon incubation with RNase A, followed by western blotting. e, Western blot confirming enrichment of p32 following immunoprecipitation with anti-p32 antibodies. b, d, e, For gel source data, see Supplementary Fig. 1.

Extended Data Figure 8 SAMMSON modulates mitochondrial metabolism in a p32-dependent manner.

a, RNA-FISH for 16S rRNA (in red) and immunofluorescence for p32 (in yellow) in melanoma cells (MM034) transfected with a control GapmeR (Ctrl) or with GapmeR11. Magnification, ×200. b, Evaluation of OXPHOS complexes functionality by high-resolution respirometry in SK-MEL-28 treated with GapmeR3. The graph shows one representative experiment. c, JC-1 reveals a decrease in mitochondrial membrane potential upon GapmeR11 transfection, as assessed by the ratio between J-aggregates and J-monomers. The graph is an average of four different biological replicates ± s.e.m. P values were calculated by ANOVA. d, Evaluation of ATP production in SK-MEL-28 transfected with GapmeRs and exposed to oligomycin (used here as control). ATP production measured by luciferase is expressed as a percentage, an average of three different experiments, relative to the control sample (Ctrl) ± s.e.m. P values were calculated by ANOVA. e, Colony formation assays 5 days after seeding SK-MEL-28 transfected with a control GapmeR (Ctrl) or GapmeR3, and either with an empty or a p32-expressing vector. f, Colony formation assays 5 days after seeding, showing cell growth of SK-MEL-28 transfected with a control GapmeR (Ctrl) or GapmeR3, and either with an empty vector or a vector expressing a tagged version of p32 that cannot localize to the mitochondria. g, Quantification of the colony assay described in f. The data represent the density (occupancy area) relative to the Ctrl + pcDNA3.1 sample. The data are presented as average of three different biological replicates ± s.e.m. P values were calculated by ANOVA. h, Immunofluorescence using antibodies directed against SDHA and ATPB in MM034 melanoma cells upon GapmeR11 transfection. Magnification, ×600.

Extended Data Figure 9 SAMMSON and p32 silencing affects mitochondria integrity.

a, b, Representative electron microscopy images of melanoma cells transfected with Ctrl GapmeR and GapmeR3 or with siRNA targeting p32. c, Quantification of percentage of mitochondria with intact cristae (top) and area and length (middle and bottom) in cells described in b; the total number of mitochondria evaluated per condition is indicated on the x-axis.

Extended Data Figure 10 SAMMSON silencing decreases melanoma growth in vivo.

a, Table describes the origin and BRAF, NRAS and TP53 mutational status of the melanoma lesions that were used to generate the Mel006 and Mel010 PDX models. b, Gene set enrichment analyses among differentially expressed genes in melanoma lesions obtained from Mel006 PDX mice treated (i.v. injections) with a control GapmeR (Ctrl) or GapmeR3. c, Tumour volume of cohorts of Mel010 PDX mice treated (intra-tumour injections) with a control GapmeR (Ctrl) or GapmeR3. Data are means ± s.d. of three different biological replicates (P value was calculated by two-ways ANOVA). d, Tumour weight of the melanoma lesions derived from PDX mice (Mel006) treated with combinations of control GapmeR (Ctrl) and GapmeR3 with either vehicle or dabrafenib by daily oral gavage (vehicle or dabrafenib) and i.v. injection of the GapmeRs every 2 days. P value was calculated by t-test.

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Leucci, E., Vendramin, R., Spinazzi, M. et al. Melanoma addiction to the long non-coding RNA SAMMSON. Nature 531, 518–522 (2016).

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