Molecular stratification of metastatic melanoma using gene expression profiling : Prediction of survival outcome and benefit from molecular targeted therapy

Abstract

Melanoma is currently divided on a genetic level according to mutational status. However, this classification does not optimally predict prognosis. In prior studies, we have defined gene expression phenotypes (high-immune, pigmentation, proliferative and normal-like), which are predictive of survival outcome as well as informative of biology. Herein, we employed a population-based metastatic melanoma cohort and external cohorts to determine the prognostic and predictive significance of the gene expression phenotypes. We performed expression profiling on 214 cutaneous melanoma tumors and found an increased risk of developing distant metastases in the pigmentation (HR, 1.9; 95% CI, 1.05-3.28; P=0.03) and proliferative (HR, 2.8; 95% CI, 1.43-5.57; P=0.003) groups as compared to the high-immune response group. Further genetic characterization of melanomas using targeted deep-sequencing revealed similar mutational patterns across these phenotypes. We also used publicly available expression profiling data from melanoma patients treated with targeted or vaccine therapy in order to determine if our signatures predicted therapeutic response. In patients receiving targeted therapy, melanomas resistant to targeted therapy were enriched in the MITF-low proliferative subtype as compared to pre-treatment biopsies (P=0.02). In summary, the melanoma gene expression phenotypes are highly predictive of survival outcome and can further help to discriminate patients responding to targeted therapy.

NF1‐mutated melanoma tumors harbor distinct clinical and biological characteristics

“…All samples were obtained at the Department of Surgery at Skåne University Hospital, Lund, Sweden. The majority of the samples (146/162) and the associated molecular data were used in a previous study with a focus on gene expression‐based analysis (Cirenajwis et al ., 2015). The tumor cohort was retrospectively collected between 2000 and 2012.…”

Multiregion Whole-Exome Sequencing Uncovers the Genetic Evolution and Mutational Heterogeneity of Early-Stage Metastatic Melanoma

“…Data analysis included integration of the multiregion samples with a reference set of 214 melanomas (22) and subtype classification (Supplementary Information).…”

“…Importantly, we pooled the data from the multiregion samples with a reference set of 214 metastatic VTN LPA EXOC3L4 DTX3L HERC1 TOP1 TRIM43 LRBA COL7A1 APOBR MX2 TIAM1 KRTAP11-1 KRT8 PLA2G4D SLC26A10 GIT2 ATP13A3 KDM2B FAM71D Mm1772 MUC4 ILVBL TPRX1 CNOT2 UTP14A DNASE1L1 CSF2RA GRPR AIFM1 POU6F1 FAM111A PVRL4 LSS PIK3CA SLC25A5 GAB3 G6PD BHLHB9 FRMD7 PNMA5 GPRASP1 USP26 TAF1 UBQLN2 FRMPD4 FANCB PORCN DRP2 REPS2 CXorf67 PLS3 DSPP OVGP1 LRRC45 TNFSF12 HSPG2 MMS22L TNNT3 KPNB1 CUL1 ABCA2 PIK3CA FBRSL1 ZIC5 FAM153B ANKZF1 RPAP3 PIK3CA CRIPAK TCHH ADCK1 SLC38A11 PIK3R1 LRRIQ3 B3GALT6 Mm1641 NUTM2A PTEN CDK12 BRAF LRIG3 CIITA LCK PTPRB SYK PKD1 UTP6 LRRC7 DPP9 EFCC1 CNTNAP2 ROCK2 IDH1 SLC25A27 NOTCH2 RBM5 MED24 CELSR1 SERF1A CELA1 Mm1767 NEXN NCAN KAT6A EPPK1 C1QTNF2 KIAA1549 KIAA0355 TBX3 CDRT15L2 TSPAN2 FRMPD3 HCLS1 TM7SF2 VPS52 NDUFA13 PIP5K1C OR56A1 ZBTB16 PTPLAD2 ZFP92 PKDREJ ADAT1 MED1 MYH6 NEB CNBD1 BBX TOPAZ1 AHNAK2 CRELD1 CRYBG3 TBC1D8B TRANK1 PARP15 TMEM5 ITGB6 NOP14 PPP2R3C ANKRD30B PER3 PER3 GRIA4 NPHS1 WDFY4 IVL BEND3 TET1 CA7 HEATR1 CCDC120 JAK3 IGFBP2 HDGFRP2 RFPL4A NWD1 RBM5 NOTCH2 WDR45 TBK1 IMPA1 LRRCC1 LRCOL1 FIZ1 ZNF385C CSMD1 GLYATL2 FOXO3 ZFYVE27 CUX2 KIF19 SLC35A1 TDG FAM178B BPTF RBM27 NWD2 DPYSL4 NEUROG3 ZNF831 PODXL USH2A HGS EXOC6B PSKH2 TGFBR2 ZNF529 GRXCR1 PCDHAC2 CCDC88C DTX3L ABCA13 AEBP1 LTN1 CELA1 TWIST1 NCKAP5L IL37 MTMR4 CDK2 KCNC1 ANKRD34C IFFO1 PLXNA2 SLC38A3 FAM169A NPY2R CNOT1 NAGK SLC35F4 NDC1 DNAH6 ACE2 LAMA1 Mm1702 WWC2 SLC22A24 SATB1 TIAM2 GLP1R PLXNA4 HDAC2 CACNA1I OR10G9 MSN CCDC30 ZNF730 PLD3 PLEKHN1 DNAH6 C20orf144 GPD2 ANKRD62 NHSL2 PCDHB10 SLC2A3 SLC2A146 GUCY2C GALNT8 PLBD1 SLITRK2 TAS2R7 DEDD CHRNE TNC RANBP17 AARS2 ZNF23 ANK3 FCGBP IGSF3 SSTR1 C9orf50 ZNF681 FRMD3 NPAS3 ZIC2 ADAD1 HMMR NLRP4 SIPA1L1 TBC1D2 COQ6 C9orf43 MPHOSPH8 MED15 SEC16A SALL3 PPP2R2B TBP NOC3L ZBBX ANKRD31 VPS13C Mm1749 Mm1742 ACSS3 UBAP2L CFAP44 FAM115A NEBL PATE1 OPN5 CPAMD8 COL3A1 TMEM63C FAM170A VPS13D IMPG1 ELMO3 NYAP2 CECR1 TBCA HAUS3 POU3F1 SFT2D3 RBMXL1 KIAA2018 ARL6IP4 ADAM32 ADAMTS19 DND1 LTBP3 INPP4A MST1 POU3F3 NKX2-4 PRR23A KCNS2 SEL1L2 APC2 DESI2 GAD2 ADM5 PHF2 PCDHA6 EDEM1 MYH3 LGALS12 NR1D1 DSE SEZ6 PRKRA COL8A2 NUMBL DCTN5 CHTF18 UBE2Q2 ADAM21 EPS8L2 TLDC1 AFAP1L1 HMCN1 USP25 JAK2 SESTD1 ADRM1 GDF1 AKAP9 MYH14 DCC JAKMIP2 ILF3 HEXIM1 CGREF1 NEURL1B TPP2 FGF23 SLC2A3 FGFRL1 CRIPAK melanoma tumors previously profiled using the same platform, allowing to relate the gene expression levels of the ITH regions in relation to the full melanoma expression space (22). We determined the intratumoral expression of a subtype signature shown to classify melanomas into four molecular subgroups: highimmune (associated with a good prognosis), microphthalmiaassociated transcription factor (MITF)-low proliferative (associated with a poor prognosis), MITF-high pigmentation (associated with a poor prognosis), and normal-like.…”

Consensus of Melanoma Gene Expression Subtypes Converges on Biological Entities

“…The "High-Immune" group was distinguished by elevated expression of immune genes, the "Normal-like" group by genes expressed in surrounding normal cells, the "MITF-high Pigmentation" and the "MITF-low Proliferative" groups displayed increased expression of cell-cycle genes, while the "MITF-low Proliferative" group had decreased expression of melanocyte differentiation genes. These subtypes were derived in stage IV tumors (Jönsson et al, 2010), but have since been recapitulated and firmly established in primary tumors (Harbst et al, 2012;Nsengimana et al, 2015) and stage III tumors (Cirenajwis et al, 2015). In 2015, the TCGA landmark study reported three melanoma gene expression subtypes (Cancer Genome Atlas Network, 2015).…”

“…First, we classified the 329 TCGA samples (Table 1, https://tcga-data.nci.nih.gov/tcga/) according to the Lund subtypes (Cirenajwis et al, 2015). An extensive overlap between TCGA and Lund classification schemes was observed ( Figure 1B).…”