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Background There remains a paucity of data regarding the efficacy of immune checkpoint therapy (ICT) combinations ± vascular endothelial growth factor (VEGF) targeted therapy (TT) in translocation renal cell carcinoma (tRCC). Methods This is a retrospective study of patients with advanced tRCC treated with ICT combinations at 11 centers in the US, France, and Belgium. Only cases with confirmed fluorescence in situ hybridization (FISH) were included. Objective response rates (ORR) and progression-free survival (PFS) were assessed by RECIST, and overall survival (OS) was estimated by Kaplan-Meier methods. Results There were 29 patients identified with median age of 38 (21-70) years, and F:M ratio 0.9:1. FISH revealed TFE3 and TFEB translocations in 22 and 7 patients, respectively. Dual ICT and ICT + VEGF TT were used in 18 and 11 patients, respectively. Seventeen (59%) patients received ICT combinations as first-line therapy. ORR was 1/18 (5.5%) for dual ICT and 4/11 (36%) for ICT + VEGF TT. At a median follow-up of 12.9 months, median PFS was 2.8 and 5.4 months in the dual ICT and ICT + VEGF TT groups, respectively. Median OS from metastatic disease was 17.8 and 30.7 months in the dual ICT and ICT + VEGF TT groups, respectively. Conclusion In this retrospective study of advanced tRCC, limited response and survival were seen after frontline dual ICT combination therapy, while ICT + VEGF TT therapy offered some efficacy. Due to the heterogeneity of tRCC, insights into the biological underpinnings are necessary to develop more effective therapies.

Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with clear-cell renal cell carcinomas (ccRCC). Although analyses of transcriptome, genetic alterations, and the tumor microenvironment (TME) have shed light into mechanisms of response and resistance to these agents, the role of epigenetic alterations in this process remains fully unknown. Experimental Design: We investigated the methylome of six ccRCC cohorts as well as one cell line dataset. Of note, we took advantage of the BIONIKK trial aiming to tailor treatments according to Paris Descartes 4-gene expression subgroups, and performed Illumina EPIC profiling for 46 samples related to patients treated with ipilimumab plus nivolumab, and 17 samples related to patients treated with sunitinib. Results: A group of tumors associated with enhancer demethylation was discovered, namely TED. TED was associated with tumors with sarcomatoid differentiation and poor clinical outcome. TED harbored TET1 promoter demethylation, activated the gene expression signature of epithelial–mesenchymal transition and IL6/JAK/STAT3 pathways, and displayed a TME characterized by both immune activation and suppressive populations, fibroblast infiltration, and endothelial depletion. In addition, TED was a predictive factor of resistance to the combination of first-line ipilimumab-nivolumab in the BIONIKK clinical trial. Finally, TED was associated with activation of specific regulons, which we also found to be predictive of resistance to immunotherapy in an independent cohort. Conclusions: We report on the discovery of a novel epigenetic phenotype associated with resistance to ICIs that may pave the way to better personalizing patients’ treatments. See related commentary by Zhou and Kim, p. 1170

Purpose:Immune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with clear-cell renal cell carcinomas (ccRCC). Although analyses of transcriptome, genetic alterations, and the tumor microenvironment (TME) have shed light into mechanisms of response and resistance to these agents, the role of epigenetic alterations in this process remains fully unknown.Experimental Design:We investigated the methylome of six ccRCC cohorts as well as one cell line dataset. Of note, we took advantage of the BIONIKK trial aiming to tailor treatments according to Paris Descartes 4-gene expression subgroups, and performed Illumina EPIC profiling for 46 samples related to patients treated with ipilimumab plus nivolumab, and 17 samples related to patients treated with sunitinib.Results:A group of tumors associated with enhancer demethylation was discovered, namely TED. TED was associated with tumors with sarcomatoid differentiation and poor clinical outcome. TED harbored TET1 promoter demethylation, activated the gene expression signature of epithelial–mesenchymal transition and IL6/JAK/STAT3 pathways, and displayed a TME characterized by both immune activation and suppressive populations, fibroblast infiltration, and endothelial depletion. In addition, TED was a predictive factor of resistance to the combination of first-line ipilimumab-nivolumab in the BIONIKK clinical trial. Finally, TED was associated with activation of specific regulons, which we also found to be predictive of resistance to immunotherapy in an independent cohort.Conclusions:We report on the discovery of a novel epigenetic phenotype associated with resistance to ICIs that may pave the way to better personalizing patients’ treatments.See related commentary by Zhou and Kim, p. 1170

Supplementary Fig. S1: Percentage of drugs per drug class among drug libraries used in the HTS; Supplementary Fig. S2: Synergistic GFP reactivation in YB5 cells by HDACi TSA and positive hits identified in the HTS; Supplementary Fig. S3: Optimization of the treatment schedule of the decitabine and proscillaridin A combination; Supplementary Fig. S4: Transcriptome analysis using Metascape; Supplementary Table S1: List of GO-Terms identified by Metascape analysis in gene data sets exclusively up-regulated by proscillaridin A treatment and exclusively up-regulated by the combination treatment; Supplementary Table S2: List of GO-Terms identified by Metascape analysis among down-regulated genes shared by proscillaridin A and the combinatorial treatment with decitabine; Supplementary Table S3: Antagonistic hits identified in combination HTS with sequential decitabine treatment; Supplementary Table S4. Antagonistic hits identified in combination HTS with simultaneous decitabine treatment; Supplementary Table S5. Antagonistic hits identified in combination HTS with sequential TSA treatment.

Figure S2. Focal copy number deletion identified by the GISTIC algorithm for a) 𝑇𝐸𝐷+ and b) 𝑇𝐸𝐷−, respectively, in the TCGA-KIRC cohort.

Supplementary Tables S1-S23

PDF file - 135K, Supplementary Figure 1. Flow chart for patient selection and different tested performed.

LENOX regulates melanoma cell proliferation and survival

LENOX and RAP2C knockdown induce mitochondrial dysfunction, release of reactive oxygen species (ROS), DNA damage and cell cycle block

Purpose: Translocation renal cell carcinoma (tRCC) is a rare subtype of kidney cancer involving the TFEB/TFE3 genes. We aimed to investigate the genomic and epigenetic features of this entity.Experimental Design: Cytogenomic analysis was conducted with 250K single-nucleotide polymorphism microarrays on 16 tumor specimens and four cell lines. LINE-1 methylation, a surrogate marker of DNA methylation, was conducted on 27 cases using pyrosequencing.Results: tRCC showed cytogenomic heterogeneity, with 31.2% and 18.7% of cases presenting similarities with clear-cell and papillary RCC profiles, respectively. The most common alteration was a 17q gain in seven tumors (44%), followed by a 9p loss in six cases (37%). Less frequent were losses of 3p and 17p in five cases (31%) each. Patients with 17q gain were older (P = 0.0006), displayed more genetic alterations (P < 0.003), and had a worse outcome (P = 0.002) than patients without it. Analysis comparing gene-expression profiling of a subset of tumors bearing 17q gain and those without suggest large-scale dosage effects and TP53 haploinsufficiency without any somatic TP53 mutation identified. Cell line–based cytogenetic studies revealed that 17q gain can be related to isochromosome 17 and/or to multiple translocations occurring around 17q breakpoints. Finally, LINE-1 methylation was lower in tRCC tumors from adults compared with tumors from young patients (71.1% vs. 76.7%; P = 0.02).Conclusions: Our results reveal genomic heterogeneity of tRCC with similarities to other renal tumor subtypes and raise important questions about the role of TFEB/TFE3 translocations and other chromosomal imbalances in tRCC biology. Clin Cancer Res; 19(17); 4673–84. ©2013 AACR.

Supplementary Table 3: Ingenuity Pathway Analysis (IPA) for genes which gain promoter DNA methylation in C1 relative to C2 epi-cluster. Supplementary Table 4: Gene Ontology analysis for differentially methylated regions (DMR) between C1 and C2 epi-clusters, as assessed by GREAT online tool. Supplementary Table 5: List of motifs enriched in differentially methylated regions between C1 and C2 epi-clusters, as assessed by GREAT online tool. Supplementary Table 6: DAVID functional annotations analysis for enriched pathways related to genes which gain DNA methylation in renal cell carcinoma samples. Supplementary Table 7: List of gene promoters located in CG islands and which are assessed for DNA methylation using DREAM. The genes which showed the highest frequency of DNA methylation in our dataset are in the top. Furthermore, the frequency of gene promoters which gain DNA methylation in either RCC samples derived from the proximal or distal cells of the nephron are also reported. Supplementary Table 8: Validation dataset showing the prevalence of promoter DNA methylation for 25 genes in clear-cell renal cell carcinoma dataset of The Cancer Genome Atlas (TCGA) project. Those genes were both methylated and repressed in the TCGA dataset.

Ectopic LENOX expression increases oxidative phosphorylation in HEKT cells

Table S1. Drug screening results after treatment at 10 µM for 72h

Unsupervised clustering of DNA methylation using 56 genes epi-signature in an independent data set encompassing 37 renal cell carcinomas cases. C2 contains the majority of oncocytoma and chromophobe RCC cases (n=11/13) as compared to C1 which encompasses the remaining RCC subtypes including clear-cell RCC (ccRCC) , papillary RCC (pRCC) and translocation RCC (tRCC).

Extensive dysregulation of chromatin-modifying genes in clear cell renal cell carcinoma (ccRCC) has been uncovered through next-generation sequencing. However, a scientific understanding of the cross-talk between epigenetic and genomic aberrations remains limited. Here we identify three ccRCC epigenetic clusters, including a clear cell CpG island methylator phenotype (C-CIMP) subgroup associated with promoter methylation of VEGF genes (FLT4, FLT1, and KDR). C-CIMP was furthermore characterized by silencing of genes related to vasculature development. Through an integrative analysis, we discovered frequent silencing of the histone H3 K36 methyltransferase NSD1 as the sole chromatin-modifying gene silenced by DNA methylation in ccRCC. Notably, tumors harboring NSD1 methylation were of higher grade and stage in different ccRCC datasets. NSD1 promoter methylation correlated with SETD2 somatic mutations across and within spatially distinct regions of primary ccRCC tumors. ccRCC harboring epigenetic silencing of NSD1 displayed a specific genome-wide methylome signature consistent with the NSD1 mutation methylome signature observed in Sotos syndrome. Thus, we concluded that epigenetic silencing of genes involved in angiogenesis is a hallmark of the methylator phenotype in ccRCC, implying a convergence toward loss of function of epigenetic writers of the H3K36 histone mark as a root feature of aggressive ccRCC. Cancer Res; 77(18); 4835–45. ©2017 AACR.

Targeting epigenetic pathways is a promising approach for cancer therapy. Here, we report on the unexpected finding that targeting calcium signaling can reverse epigenetic silencing of tumor suppressor genes (TSG). In a screen for drugs that reactivate silenced gene expression in colon cancer cells, we found three classical epigenetic targeted drugs (DNA methylation and histone deacetylase inhibitors) and 11 other drugs that induced methylated and silenced CpG island promoters driving a reporter gene (GFP) as well as endogenous TSGs in multiple cancer cell lines. These newly identified drugs, most prominently cardiac glycosides, did not change DNA methylation locally or histone modifications globally. Instead, all 11 drugs altered calcium signaling and triggered calcium-calmodulin kinase (CamK) activity, leading to MeCP2 nuclear exclusion. Blocking CamK activity abolished gene reactivation and cancer cell killing by these drugs, showing that triggering calcium fluxes is an essential component of their epigenetic mechanism of action. Our data identify calcium signaling as a new pathway that can be targeted to reactivate TSGs in cancer. Cancer Res; 76(6); 1494–505. ©2015 AACR.

Tumor heterogeneity is a key feature of melanomas that hinders development of effective treatments. Aiming to overcome this, we identified LINC00518 (LENOX; lincRNA-enhancer of oxidative phosphorylation) as a melanoma-specific lncRNA expressed in all known melanoma cell states and essential for melanoma survival in vitro and in vivo. Mechanistically, LENOX promoted association of the RAP2C GTPase with mitochondrial fission regulator DRP1, increasing DRP1 S637 phosphorylation, mitochondrial fusion, and oxidative phosphorylation. LENOX expression was upregulated following treatment with MAPK inhibitors, facilitating a metabolic switch from glycolysis to oxidative phosphorylation and conferring resistance to MAPK inhibition. Consequently, combined silencing of LENOX and RAP2C synergized with MAPK inhibitors to eradicate melanoma cells. Melanomas are thus addicted to the lncRNA LENOX, which acts to optimize mitochondrial function during melanoma development and progression.Significance:The lncRNA LENOX is a novel regulator of melanoma metabolism, which can be targeted in conjunction with MAPK inhibitors to eradicate melanoma cells.

PDF file - 62K

LENOX regulates RAP2 mitochondrial localisation

XLSX file - 39K, List of somatic mutations discovered in exome sequencing in MITF/TFE TRCC cases.

PDF file - 498K, Coordinate of the exonic breakpoint of the novel KHSRP/TFE3 translocation.

List of 369 probe sets defining the C-CIMP (CpG island methylator phenotype) in ccRCCs, including mean beta-values of those in C-CIMP (cluster 3) versus no-CIMP (cluster 2). Expression values as well as fold-changes of gene expression of those genes are also reported.

RAP2 paralog expression in melanoma cells

Purpose: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype–phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here, we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron.Experimental Designs: We performed deep profiling of DNA methylation and transcriptome in diverse histopathological RCC subtypes and validated DNA methylation in an independent dataset as well as in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets.Results: Our data provide the first mapping of methylome epi-signature and indicate that RCC subtypes can be grouped into two major epi-clusters: C1, which encompasses clear-cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2, which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed 3-fold more hypermethylation as compared with C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC.Conclusions: Our data define the epi-clusters that can discriminate between distinct RCC subtypes and for the first time define the epigenetic basis for proximal versus distal tubule derived kidney tumors. Clin Cancer Res; 22(24); 6236–46. ©2016 AACR.

Supplementary Materials and Methods and Supplementary Figure Legends

LENOX is required for xenograft growth

Purpose:MITF/TFE translocation renal cell carcinoma (TRCC) is a rare subtype of kidney cancer. Its incidence and the genome-wide characterization of its genetic origin have not been fully elucidated.Experimental Design: We performed RNA and exome sequencing on an exploratory set of TRCC (n = 7), and validated our findings using The Cancer Genome Atlas (TCGA) clear-cell RCC (ccRCC) dataset (n = 460).Results: Using the TCGA dataset, we identified seven TRCC (1.5%) cases and determined their genomic profile. We discovered three novel partners of MITF/TFE (LUC7L3, KHSRP, and KHDRBS2) that are involved in RNA splicing. TRCC displayed a unique gene expression signature as compared with other RCC types, and showed activation of MITF, the transforming growth factor β1 and the PI3K complex targets. Genes differentially spliced between TRCC and other RCC types were enriched for MITF and ID2 targets. Exome sequencing of TRCC revealed a distinct mutational spectrum as compared with ccRCC, with frequent mutations in chromatin-remodeling genes (six of eight cases, three of which were from the TCGA). In two cases, we identified mutations in INO80D, an ATP-dependent chromatin-remodeling gene, previously shown to control the amplitude of the S phase. Knockdown of INO80D decreased cell proliferation in a novel cell line bearing LUC7L3–TFE3 translocation.Conclusions: This genome-wide study defines the incidence of TRCC within a ccRCC-directed project and expands the genomic spectrum of TRCC by identifying novel MITF/TFE partners involved in RNA splicing and frequent mutations in chromatin-remodeling genes. Clin Cancer Res; 20(15); 4129–40. ©2014 AACR.

Legends to Supplementary Figures and Tables

Raw data for β-values of probe sets related to our 56 genes signature in an independent dataset encompassing 4 normal kidneys and 37 renal cell carcinomas belonging to diverse histopathological subtypes.

XLSX file - 12K, List of fusion transcripts identified in our dataset with validation accuracy.

LENOX regulates mitochondrial morphology

PDF file - 130K, Supplementary Figure 3. A) Gene Set enrichment Analysis (GSEA) showing enrichment of mitosis pathways in cases with 17q gain. B, Ingenuity Pathway Analysis of genes expressed differently between samples with partial 17q gain and without it showing activation of ICOS-ICOSL signaling in T helper cells and activation of the CTLA4 signaling pathway.

Supplementary Table 10: List of samples misclassified among the clear-cell renal cell carcinomas dataset with the corresponding new classification. Among those, many of them have been demonstrated to belong to the C4 cluster of long non-coding RNA classification. The first table reports the 5 misclassified TCGA clear-cell RCC (KIRC) using gene expression and which turned out to be chromophobe RCC (chRCC). The second table reports on misclassified TCGA KIRC cases with DNA methylation available. Supplementary Table 11: List of importance scores for genes used in the statistical model for predicting outcome of patients with clear-cell RCC. Supplementary Table 12: Associations (both training and validation datasets) between mutational load, TCGA clear-cell renal cell carcinomas transcriptomic subtypes classification and recurrent somatic mutations (VHL, PBRM1, BAP1 and KDM6A) identified in clear-cell renal cell carcinoma TCGA dataset with our 56 genes epi-signature. P-values less than 0.05 is considered as statistically significant.

Supplementary Figure 1. Independent validation of C-CIMP A) Supervised clustering of TCGA samples in ccRCC validation dataset assessed by 27k Infinium arrays. The (beta value) level of DNA methylation is represented with a color scale. Each column represents a sample; each row a probe set. The transcriptomic subtype in TCGA, the copy number variation at 9p23.1 locus (CDKN2A), somatic mutation status of four genes (PBRM1, BAP1, SETD2 and VHL) are indicated by red, green and gray squares, with annotations in the legend. B) Kaplan-Meier curves showing distinct outcome of patients according to the three subgroups of DNA methylation classification, with patients belonging to C-CIMP subgroup having the worst outcome. Supplementary Figure 2. Expression vs DNA methylation between C-CIMP and no-CIMP subgroups using volcano and starburst plots. A) All CpG loci analyzed for C-CIMP association; x-axis: β-value difference in DNA methylation between C-CIMP and no-CIMP clusters; y-axis: p-value of the corrected FDR between the two groups; red: probes significantly different between the two subgroups. B) TCGA promoter DNA methylation vs. gene expression analyzed by RNAseq. Log-10 (FDR adjusted p-value) is plotted for DNA methylation (x-axis) and gene expression for each gene (y-axis). Green indicates genes that are downregulated for gene expression and which gain DNA methylation in C-CIMP cluster versus no-CIMP cluster. Red indicates genes that are upregulated for gene expression and gain DNA methylation. Supplementary Figure 3. Association between somatic mutations and DNA methylation subgroups. A) Mutational load is increased in C-CIMP as compared to no-CIMP and low-CIMP subgroups. B) Association between somatic mutations of BAP1 and SETD2 in C-CIMP subgroup as compared to low-CIMP and no-CIMP subgroups. Supplementary Figure 4. Predictive value of VEGF receptor methylation to predict response to sunitinib. Kaplan-Meier curves for progression-free survival of patients treated with sunitinib according to tumor methylation status of (A) FLT4, (B) FLT1, and (C) FLT3 genes in Beuselinck study. Supplementary Figure 5. C-CIMP subgroup in metastatic ccRCC A) Supervised clustering of DNA methylation in Beuselinck study was consistent with the three DNA methylation subgroups C-CIMP, low-CIMP and no-CIMP. B) Kaplan-Meier curves for progression-free survival of patients treated with sunitinib according to their CIMP status. Supplementary Figure 6. Correlation between methylome signature of NSD1 mutations of patients with Sotos syndrome and ccRCC. A) Supervised clustering of DNA methylation in TCGA ccRCC using genome-wide methylome signature of NSD1 mutations in Sotos syndrome. B) Kaplan-Meier curves for overall survival of patients with ccRCCs according to NSD1 mutations in genome-wide methylome signature. Note that the analyzed cohort encompasses 271 ccRCCs assessed by Infinium 450K arrays.

LENOX overexpression promotes resistance to BRAF and glycolysis inhibition.

XLSX file - 172K, List of genes that have been reported previously to be involved in translocations.

Supplementary Figure S1. Drug screening based on the measurement of GFP fluorescence by flow cytometry. Supplementary Figure S2. Dose response curves of GFP reactivation after cardiac glycosides treatment. Supplementary Figure S3. Tumor suppressor genes reactivation after treatment with validated hits. Supplementary Figure S4. Analysis DNA methylation and histone modifications following treatments with validated hits. Supplementary Figure S5. Characterization of Ca2+ fluxes in YB5 and HEK293 cells. Supplementary Figure S6: Tumor suppressor gene reactivation is dependent on CamK activity. Supplementary Figure S7. Promoter occupancy of MeCP2 is decreased after cardiac glycoside treatment. Supplementary Figure S8: Specificity of MeCP2 antibody. Supplementary Figure S9: Assessing the specificity of MeCP2 redistribution after proscillaridin treatment. Supplementary Figure S10. Percentage of YB5 cells expressing GFP after drug treatment is significantly reduced after leptomycin treatment. Supplementary Figure S11. Cell viability assays show that drug induced toxicity is dependent on CamK activity in SW48 colon cancer cell line. Supplementary Figure S12. Cell viability assays show that drug induced toxicity is dependent on CamK activity in K562 leukemia cell line. Supplementary Figure S13. Cell viability assays show that drug induced toxicity is dependent on CamK activity in HL-60 leukemia cell line. Supplementary Figure S14. Scheme describing proposed mechanism of action of newly identified candidate epigenetic drugs.

List of 95 CpG located in promoter CG islands which are differentially methylated and expressed between C1 and C2 epi-clusters.

XLSX file - 185K, List of fusion transcripts identified in TCGA cohort, including the number of reads identified spanning the fusion junction.

List of DNA methylation levels for each CpG sites detected by DREAM in the 2 normal kidneys and the 22 renal cell carcinomas samples. Each column represents one sample and each raw one CpG site with the relative annotations (coordinates, CpG islands or not, distance from transcription start site (TSS). The coordinates of each CpG sites are provided according to Hg18 version of the Human Genome.

PDF file - 86K, Supplementary Table 1. Clinicopathologic characteristics of patients with confirmed Translocation RCC Supplementary Table 2: Clinicopathological features of TFE3 overexpressing renal cell carcinoma with no TFE3 translocation identified. Supplementary Table 3: List of upstream regulators predicted to be activated or inhibited in two cases of translocation renal cell carcinoma with concomitant 17q gain and 17p loss as compared to two cases with balanced karyotype.

PDF file - 79K, Material and Methods describing the TCGA dataset with the RNAseq and exome-sequencing analysis.

XLSX file - 20K, Clinicopathological features of MITF/TFE TRCC samples selected in our cohort for RNA and exome sequencing.

XLSX file - 10K, List of all ccRCC included from the TCGA with the identified translocations in each sample.