Integrative Copy Number Analysis of Uveal Melanoma Reveals Novel Candidate Genes Involved in Tumorigenesis Including a Tumor Suppressor Role for PHF10/BAF45a

Uveal melanoma is the most common primary intraocular malignant tumor diagnosed in approximately six cases per million per year. Approximately 40% of patients develop metastatic melanoma to the liver within 10 years (1). Advances made in the local methods of treatment of primary uveal melanoma have not led to an improvement in survival and after metastasis there is a median survival time of less than 6 months (2). Uveal melanomas display characteristic genomic signatures including recurrent chromosomal aberrations, gene mutations, and gene expression profiles (GEPs), some of which are predictive of metastatic risk (3, 4). Mutations in GNAQ, GNA11, CYSLTR2, and PLCB4 that constitutively activate Gαq signaling are seen in almost all tumors in a mutually exclusive manner (5–8). Inactivating mutations in BAP1 at chromosome 3p21 (9) are found in tumors with loss of heterozygosity for Chr 3 (LOH3) and a GEP predictive of metastatic risk (class 2 tumors; ref. 10), and mutations in SF3B1 or EIF1AX are found in tumors with disomy 3 and a GEP associated with an intermediate and low likelihood of metastasis, respectively (class 1 tumors; refs. 4, 11, 12). Whole-genome sequencing has also revealed potential mutations in other genes (13).

In addition to LOH3, uveal melanomas are characterized by chromosomal alterations that can include Chr 1p loss, 6p gain, 6q loss, 8p loss, 8q gain, and 16q loss (14–20). These DNA copy number aberrations (CNAs) are an important category of genetic alterations that can lead to development and progression of cancers by affecting gene dosage, sometimes amplifying genes conferring a proliferative or metastatic advantage or leading to loss of function of tumor suppressor genes. However, the significance of CNAs in most cancers, including in uveal melanoma is poorly understood.

A global characterization of copy number, transcriptome, mutation, and methylation status in uveal melanoma has been described (20). This confirmed much previous work and showed four distinct molecular subtypes of CNA, each associated with a varying degree of metastatic risk. Here, we describe copy number analysis of 182 tumor samples from three different cohorts followed by integration of transcriptomic, methylation, and mutation data to look for genes driving copy number loss or gain, extending earlier studies investigating the relationship with chromosomal alterations and gene expression in uveal melanoma with microarrays (21). We describe candidate genes on chromosomes 1p, 8q, and 6q and provide evidence for a tumor suppressor role for PHF10 mapping to Chr 6q27. Functional studies provided insights into the consequences of loss of PHF10.

Data from 182 primary uveal melanoma tumor samples were obtained from three different cohorts: Washington University (WU), Cleveland Clinic (CC), and TCGA. All of the samples were enucleated specimens obtained from adult patients after they had provided written informed consent. Studies were conducted in accordance with recognized ethical guidelines (e.g., Declaration of Helsinki, CIOMS, Belmont Report, U.S. Common Rule) and were approved by an institutional review board. The data analysis workflow and methods of analysis of SNP arrays (copy number), RNA sequencing (RNA-seq), and exome sequencing are described in Supplementary Fig. S1.

Established cell lines derived from primary uveal melanomas [Mel202 (22), 92-1 (23) and Mel290 (24) (described in Supplementary Table S1)] were subjected to PHF10 knockdown with siRNA (Santa Cruz Biotechnology, sc-95343) or a siRNA control that consisted of scrambled sequence/nonsilencing siRNA (Santa Cruz Biotechnology, sc-37007); siCtrl). RNA-seq was performed with routine methods. In the case of Mel202 and 92-1, RNA was sent to the Centre Nacional d'Anàlisi Genòmica (CNAG, Barcelona, Spain) for processing. In the case of Mel290 (25), RNA was sent to Genewiz (www.genewiz.com). Raw counts were analyzed with DeSeq2 (26). RNA-Seq data from cell lines are available upon request.

To assay for migration, cells were transfected in 6-cm dishes. After 48 hours of incubation, they were trypsinized and subjected to an assay for chemotaxis as described elsewhere (27). Collagen invasion (ECM552 Millipore) and adhesion assays (ECM545 Millipore) were performed according to the manufacturer's instructions.

This was performed with standard approaches by querying a set of proteins identified following PHF10 knockdown in cell lines. The following antibodies were used: PHF10 (ab154637, Abcam), JAZF1 (ab80329, Abcam), SMAD2 (ab40855, Abcam), and PCGF3/5 (ab201510, Abcam).

Three replicates per cell line were used for PHF10 knockdowns. Intergroup differences were assessed with a one-way ANOVA with Bonferroni post hoc test. Results were expressed as mean and SD or in boxplots. The Kaplan–Meier method and log-rank test were used to compare the survival plots and a univariate Cox proportion hazard model was used to compare the effects of high and low expression levels of select genes on overall survival. Time was computed for days. Survival was defined as the elapsed interval until date of last follow-up or date of death. An effect was considered significant at an FDR q-value less than 0.05.

Genomic DNA samples from 182 primary enucleated uveal melanomas (87 with matched normal DNA) from three different cohorts (Supplementary Table S2A) were profiled on three different high-resolution SNP platforms encompassing >700 K probes with a median interprobe spacing of ∼24 to ∼18 Kb. The analysis pipeline is described in Supplementary Fig. S1 and revealed a high degree of nonrandom CNAs across samples from all cohorts. Twelve broad events affected >10% of all samples. This included Chr 1q gain, 6p gain, 8p gain, 8q gain, 21q gain and Chr 1p loss, 3p loss, 3q loss, 6q loss, 8p loss, 9p loss, and 16q loss (Supplementary Table S2B; Supplementary Fig. S2A). Chr 8q gain and LOH3 were detected in more than 50% of all samples, consistent with previous studies (15, 28). Ploidy estimates revealed an average of 2.4n in 103 tumors from the WU and CC cohorts (Supplementary Fig. S2C) due to a minority of tumors being tetraploid that was more common in class 2 versus class 1 tumors consistent with BAP1 deficiency being the prognostic predictor for patients with polyploid tumors (29).

Unsupervised hierarchical clustering of all uveal melanoma samples (N = 182) revealed four distinct clusters similar to those described elsewhere (ref. 20; Supplementary Fig. S3): groups A and B correspond to those classified as class 1A and 1B tumors, respectively, and groups C and D correspond to those classified as class 2 tumors. Groups C and D were associated with monosomy 3 and were highly significantly associated with metastatic outcome (P = 1.44e−08). The monosomy 3 tumors from both groups C and D showed gain of the entire 8q arm in nearly all samples as reported previously in TCGA cohort (20). Patients with monosomy 3, 8p loss, and 8q gain had lower overall survival (OS) as described elsewhere (30–35). When examined independently, chromosomal alterations associated with metastatic disease were loss of 3p (P = 2.29 × 10⁻⁸), loss of 3q (P = 2.95 × 10⁻⁷), loss of 6q (P = 0.0246), gain of 8q (P = 0.0001), and loss of 8p (P = 6.29 × 10⁻⁵). Chr 8p loss was more common in group C and Chr 8p gain was more common in group D. We identified 77 transcripts from Chr 8p that could explain this difference (Supplementary Fig. S4).

We integrated global copy number and transcriptomic data from TCGA and CC cohorts to identify transcripts driving the CNAs. We first performed a global correlation analysis between DNA copy number and transcript levels in the 80 tumor samples from TCGA cohort and the 57 tumor samples from the CC cohort. Results are shown in Supplementary Table S3. There were 922 transcripts whose expression levels were correlated with copy number changes at FDR P < 0.05 in both cohorts or within TCGA alone where data for the CC cohort was missing (an additional 1,218 transcripts). Testing for enrichment of genes by computing the overlaps between our list and the Cytogenic set (C1) from MSigDB (36) revealed significant enrichment of genes/transcripts on chromosomes 1p, 3, 6, 8, and 16q (Table 1A,Table 1B). Loss of transcripts from the cytoband in which BAP1 is located (3p21) was significant in both TCGA and CC cohorts and BAP1 showed strong correlation between copy number change and expression in both cohorts (correlation coefficient = 0.77, FDR P = 8 × 10⁻¹⁵ and correlation coefficient = 0.53, FDR P = 0.001 in TCGA and CC, respectively).

The significance of most large-scale chromosomal changes in cancer is not known. Some are thought to reflect altered dosage of a set of genes (e.g., loss of genes for lipid biosynthesis on Chr 8p in breast cancer; ref. 37). However, loss or gain of some chromosomes or chromosomal arms can reflect loss or gain of an underlying tumor suppressor or oncogene, respectively, as with LOH3 and BAP1 (10). To search for genes driving regions of gain or loss in uveal melanoma, we looked for regions of common focal amplification or deletion and then asked which of the genes whose expression was correlated with CNA, resided in these regions. We then asked whether any of the genes in regions with focal alterations also harbored deleterious mutations in any uveal melanomas because this could be further evidence that the correct gene had been identified.

Copy number data from the 182 uveal melanoma primary cancer specimens were analyzed with GISTIC (38). By employing a q value threshold of 0.1, 246 independent regions of significantly recurrent somatic FCNAs were identified. These included 130 amplifications and 116 deletions. On average, after common germline variants were removed, there were 21 focal alterations per sample. Among these 246 regions, the CNA boundaries for amplification events revealed a median size of 46.81 kb (0.6–7614 kb) and a median size of 259 kb (24–17328 kb) for deletions. For each of these significant FCNAs, a “peak” region lying within a 99% confidence window was then identified that was most likely to contain the putative locus involved in tumorigenesis. These peaks each contained a median of two genes (range, 0–176) and included miRNAs and other noncoding RNAs). Thirteen regions contained more than 25 genes each, and the remaining 233 regions encompassed a total of 677 potential target genes (Supplementary Table S4; Fig. 1). In addition, there were 217 copy number and gene expression correlated transcripts present within one or more reported GISTIC peaks across the three cohorts (Supplementary Table S3).

Because focal alterations can be spurious and unrelated to tumorigenesis, peaks from different cohorts that overlap provide further evidence that a biologically relevant interval has been identified. Globally, we identified seven regions with focal alterations that overlapped in two of the three cohorts (Table 2). Six of these regions mapped to chromosomal regions with copy number change already implicated in uveal melanoma: Chr 3, Chr 1p36, Chr 6q, and Chr 8q. These are discussed below.

An overlapping region of deletion at Chr 1p36.11 (chr1:26387424-26491354) deleted a segment of TRIM63, the entire PDIK1L gene and a segment of FAM110D. Of these genes, TRIM63 showed the strongest correlation between deletion and expression in TCGA cohort (correlation coefficient = 0.64, FDR P = 3.15 × 10⁹; Supplementary Table S3). However, loss of expression of 1p is historically associated with poorer survival and some transcripts outside this region were strongly correlated with this (see below).

Other regions of overlapping deletion were: Chr 2q37 (chr2:241835544-241943252) detected in 8% of tumors. This region harbors SNED1, LOC200772, CROCC2, and C2orf54 but they did not exhibit correlation with between expression and copy number change. Chr 2q27 was also deleted in a Pan Cancer study (ref. 38; ING5) and harbors HTR2B whose upregulation discriminates class 2 from class 1 tumors (39). We also identified an overlapping deletion in CC and WU cohorts around the Chr 3 centromere (chr3:89516653-93735022); and an overlapping deletion on Chr 6q25 (chr6:154470218-154726987) that harbors CNKSR3 (40).

At Chr 6q27, we identified an overlapping deletion of a region harboring WDR27, c6orf120, PHF10, and TCTE3 (chr6:169652569-170152990). In the case of WDR27 and PHF10, expression was correlated with deletion in both TCGA and CC cohorts (Supplementary Table S3). Further inspection of this region revealed that uveal melanoma patient MM016 (WU cohort) harbored a homozygous deletion of this region that resulted from a complex deletion of one Chr 6q homolog and a second deletion that spanned 54 kb at Chr6:170099399-170152990. This tumor had been diagnosed in a young woman of 24 years and it had very quickly metastasized. It had a differentiated histology (spindle), was very large (24 × 22 mm), and was disomic for Chr 3. It had the following chromosomal changes: +1q, +6p, −6q, +8, +11p, −11q, +22. MM016 did not harbor a mutation in any previously identified prognostic driver (BAP1, EIF1AX, SF3B1) although it did harbor an oncogenic mutation in GNAQ and had upregulated PRAME (not shown) consistent with its metastatic features and its classification as a class IB tumor. Homozygous deletions are rare and can point to the locale of tumor suppressors. Further inspection revealed a second homozygous deletion spanning this region (Chr 6:170073603-170123579; hg19) in a tumor of a 91-year-old female patient from the CC cohort (GSM1082739). The overlapping regions of homozygous deletion in both of these tumors disrupted PHF10, C6orf120 and WDR27 (Fig. 2). Analysis of exome data also revealed a somatic frameshift mutation in PHF10 in a class 2 tumor MM133 (c.678delT, p.F226fs; Supplementary Table S5) that was validated with Sanger sequencing (Supplementary Fig. S5). Read counts revealed loss of the wild-type allele consistent with a potential role for PHF10 as a tumor suppressor. Tumor MM133 arose in a patient of 54 years of age. It was medium–large (19 × 15 mm) of undifferentiated histology (epithelioid), no metastasis at enucleation, and lacked a detectable BAP1 mutation. We also identified a deleterious missense mutation (p.D453E, rs761295711) in PHF10 in TCGA tumor A8KM (Supplementary Table S5).

There were two regions of copy number gain on Chr 8q. A region at Chr 8q22.1 was detected in WU and TCGA cohorts (chr8: 94721058-94749641) and harbors RBM12B. The second region mapped to Chr 8q24.3 (chr8:144992452-144993067) but only harbored a segment of PLEC whose expression was only weakly correlated with copy number gain in TCGA (Supplementary Table S3). However, a number of nearby transcripts from Chr 8q24.3 exhibited stronger correlation between copy number gain and expression and were also correlated with overall survival (discussed further below).

Methylated CpG sites correlated with gene expression in both TCGA and CC cohorts are shown in Supplementary Table S6. There were 1,984 transcripts with a significant correlation with hypo- or hypermethylated CpG sites. Expression of 206 of these transcripts was also correlated with chromosomal copy number levels. These were primarily from Chr 3p21, 6p21, 3q21, 1p36, 8p21, and 8q24 (Supplementary Fig. S6). Genes in deleted regions where there was also hypermethylation included EXTL1, RUNX3, and WASF2. These also resided in a region of focal deletion at Chr 1p36.11 (chr1:18079813-27712917; Supplementary Table S4). At several regions of copy number gain, there were clusters of upregulated genes exhibiting hypomethylation including on Chr 6p21.1 (KLHDC3, SLC29A1, BYSL, POLR1C, PPP2R5D, and XPO5) and Chr 8q24.3 (PTK2, PTP4A3, CYHR1, PPP1R16A, RECQL4, and ZNF517). Upregulation of ENPP2, mapping to 8q24.3 and previously reported as a gene expressed at high levels in class 2 tumors (21) appears to have been driven exclusively by methylation (Supplementary Table S6). MTUS1, mapping to Chr 8p22 is one of the 12 prognostic transcripts (41) differentiating class 1 from the aggressive class 2 tumors. Its expression was correlated with both copy number and methylation in TCGA; however, it was not significant in the CC cohort (Supplementary Tables S3 and S6). Expression of MTUS1 was also significantly associated with metastasis and overall survival (see below).

We looked for protein damaging mutations in genes affected by copy number alteration (Supplementary Table S7). In addition to Chr 3p21, which harbors BAP1 and the handful of deleterious alterations in PHF10 at Chr 6q27, there was very little enrichment of mutations in any other single gene, and very few genes had the hallmarks of a tumor suppressor. On Chr 1p36, two tumors harbored mutations in MTOR, and at Chr 1p34.1, one TCGA tumor (AA9A) harbored a mutation in KDM4A (exon13:c.C1942T:p.P648S) with loss of the wild-type allele. Exon 32 of PTK2 mapping to Chr 8q24.3 was somatically mutated in MM127 (c.G3091T:p.A1031S), a class 2 tumor with an activating mutation in GNAQ, no identified mutation in BAP1, SF3B1, or EIF1AX and no apparent copy number change (Supplementary Table S2B). CYC1 on Chr 8q24.3 was mutated in TCGA tumor A883 (p.D209delinsDYY). On Chr 16q23.1, MON1 homolog B (MON1B) showed the highest correlation with expression in TCGA cohort (Supplementary Table S3) and was mutated in TCGA tumor A87Y (c.C1390T:p.R464X). A87Y was a primary tumor from an enucleated specimen that had metastasized. The same mutation has been described in an aggressive cutaneous neuroendocrine tumor (Merkel cell) where Merkel cell polyomavirus has been implicated in a subset of tumors (42). In rare cases, neuroendocrine tumors such as those of Merkel cells can metastasize to the uvea (43). Hence, it is possible that the uveal melanoma in A87Y represents a metastasis from a different primary tumor or that in some instances loss of function of MON1B is associated with the development of uveal melanoma.

We asked how genes from regions exhibiting significantly high correlation with expression and CNAs influenced patient overall survival and compared the results to chromosomal alterations alone. We performed a genome-wide univariate Cox proportional hazard model and compared the effects of high and low gene expression in TCGA data. A total of 6,341 transcripts were significantly associated with overall survival at q-value < 0.05 (Supplementary Table S8). Next, we selected the most significant transcripts from regions of CNA and asked whether they were also associated with metastasis (Supplementary Table S3). This resulted in 1,071 transcripts. Pathway enrichment analysis revealed significant enrichment for metabolism of RNA, immune system, and mRNA stability. The majority of these 1,072 transcripts were from genes encoded by Chr 3p (165 genes), 3q (108 genes), 8q (152 genes), 6p (80 genes), and 1p (73 genes). Out of these 1,072 genes, 424 genes had a HR < 1 (lower expression associated with good prognosis) and 647 genes had HR > 1 (lower gene expression associated with poor prognosis).

We focused on 92 genes present within focal regions of alterations reported from our GISTIC analysis and compared the top three cluster of genes present at 8q24, 1p36, and 3p21 with chromosomal alterations alone (Table 2). In the case of Chr 3, downregulation of BAP1 expression (HR = 11.39; CI: 3.36–38.54; P = 9.0e−05) was more significant than monosomy 3 (HR = 14.5; CI: 3.37–62.44; P = 3.3e−04) or the presence of BAP1 mutations (HR = 6.58, CI: 2.4–17.89, P = 2.18e−04; Fig. 3A). In addition, there were eight other transcripts within this cluster which were highly correlated with each other (Fig. 3A), two of which have been previously reported as downregulated in class 2 versus class 1 UM (RPL29 and SCAP; ref. 39). On Chr 1p36 we identified a cluster of genes (Table 2) highly correlated with poor outcome and associated with metastasis although 1p loss had not been significant in TCGA cohort (HR = 1.62; CI: 0.66–3.98; P = 0.29; Fig. 3B; Supplementary Table S3). A cluster of transcripts at chromosome 8q24.3, encoded by 13 genes were strongly correlated with metastasis and poor overall survival (Table 2; Fig. 3B). Compared with chromosomal 8q gain (HR = 6.10; CI: 1.42–26.34; P = 0.01) the HR were lower for the cluster of genes on 8q24 (Supplementary Table S3); however, the confidence interval are wide and more accurate estimates will require larger sample sizes. MTUS1 on chromosome 8p was more strongly correlated with metastasis than Chr 8p gain (Supplementary Table S3; Table 2; Fig. 3B),

We used siRNAs to investigate the consequences of PHF10 knockdown in the established uveal melanoma cell lines Mel202, 92.1, and Mel290 (Fig. 3). We first profiled transcriptome-wide changes with RNA-Seq (Supplementary Tables S9–S11). All lines were wild type for PHF10 coding sequence, although Mel202 and 92.1 had lost one copy of Chr 6q. There were 363 differentially expressed transcripts with a fold change of 2 (adjusted P < 0.05) shared by at least two of the three cell lines (Fig. 4A). Significant gene ontology terms for these shared transcripts (Supplementary Table S12) included cell adhesion (GO:0007155), developmental process (GO:0032502), cell development (GO:0048468), regulation of multicellular organismal process (GO:0051239), and extracellular matrix organization (GO:0030198; Fig. 4B).

Although there can be considerable differences in cell lines versus primary tumors, a number of transcripts altered by PHF10 knockdown exhibited the same trend in the PHF10-mutant tumors MM16 and A8KB (TCGA; Supplementary Table S13). These included downregulated transcripts IL6ST, JAM3, JAZF1, PCGF5, SMAD2, and TRIM22 and upregulated transcripts PHRF1, POLR3D, and TCF3. Many of these were transcription factors/chromatin remodelers and Western blot analysis of the PHF10 knockdowns with antibodies to some of the encoded peptides confirmed altered protein expression of JAZF1, PCGF5, and SMAD2 in the cell lines (Fig. 4C).

We also asked whether there were transcripts that were also upregulated or downregulated in correlation with PHF10 changes in the larger cohort of uveal melanomas. We categorized all 80 TCGA samples into those with low and high PHF10 gene expression based on the survival data using maximally selected rank statistics. We then compared these two groups (PHF10 high and low) and identified 10,272 significant differentially expressed transcripts (FDR P < 0.05; Supplementary Table S14A). There were 4,388 transcripts shared between TCGA PHF10 low samples and the UM PHF10 knockdown cell lines that included IL6ST, JAM3, PCGF5, and PHRF1. Employing a more stringent filtering criteria for the TCGA data (FC > 2) there were 222 transcripts enriched in pathways including development (GO:2000026, regulation of angiogenesis (GO:0045765), and focal adhesion (KEGG:04510; Supplementary Table S14).

We used migration, invasion, and adhesion assays to contrast cells treated with either a PHF10 suppressing siRNA or a nonsilencing control. No change in invasion was observed with PHF10 knockdown (Supplementary Fig. S8). However, PHF10 suppression trended toward less adhesion to most ECM proteins in both Mel202 and 92.1 cell lines, but conversely showed no effect in Mel290 (Fig. 4D–F). Knockdown of PHF10 resulted in significantly reduced migration in a chemotaxis assay in Mel202 and 92.1, but not in Mel290 (Fig. 4G).

Here we describe large-scale and focal CNAs in uveal melanoma identified by interrogating 182 primary tumors from three different datasets and nominate candidate genes that may contribute to tumorigenesis following integration of gene expression, methylation and mutation data. Clustering of broad copy number changes revealed four tumor groups characterized primarily by alterations involving 1p loss, 1p gain, monosomy 3, 6p gain, 6q loss, 8p gain/loss 8q gain, and 16q loss. This is similar to what was recently described for TCGA (20) and previously identified with gene expression profiling alone (44). Although, there were no significant differences in the ploidy estimates between the class 1 and class 2 subgroups, class 2 tumors tended to exhibit higher ploidy states (tetraploidy), consistent with BAP1 deficiency being the prognostic predictor for patients with polyploid tumors (29).

Integration of copy number with expression showed enrichment of genes in cytogenetic bands 1p36, 6p21, 8q22, 8q24, and 3p21 (the locale of BAP1), consistent with comparative genomic hybridization analysis (18). To search for candidate genes that might be driving copy number alterations, we looked for focal alterations and identified seven regions overlapping events in two independent cohorts. Deletion events localized to chromosomes 1p36.11, 2q37, 3cen, 6q25, and 6q27 and amplification events mapped to 8q22.1 and 8q24.3.

Loss of chromosome 3 was correlated with downregulation of expression of BAP1 (10). From our studies of clonality in uveal melanoma, BAP1 mutations were found to be occasionally present in the subclones suggesting loss of one Chr 3 homolog precedes mutations in BAP1 (33). Thus, it is unlikely that haploinsufficienty of BAP1 is driving LOH3. However, we identified an overlapping region of deletion at 3p11.1-q11.1 that contains PROS1 and ARL13B as well as pericentromeric sequence. This has not been described before in the context of uveal melanoma and its significance is not clear and will require further investigation.

The region of 8q gain in uveal melanoma has been narrowed to 8q23-q24 in a number of studies (16–19) and we identified focal amplification of a segment of PLEC at chr. 8q24.3. However, there were a number of candidates outside this region with higher correlation coefficients and significant P values (Supplementary Table S3). 8q gain is associated with poor prognosis and expression of a number of transcripts from Chr 8q24.3 exhibited high correlation with copy number gain. Their overexpression was more significantly associated with metastasis and worse overall survival. We also showed that in addition to Chr 8q24.3 gain, upregulation of PTK2 and PTP4A3 could be a consequence of demethylation. These findings are consistent with earlier studies of PTP4A3 that also showed it to be a strong predictor of metastasis where its overexpression increased cell migration and invasiveness (45). These results are also consistent with earlier array-CGH studies where the importance of chromosome 8q gain on metastasis was demonstrated, along with losses of 3, 8p, and 16q (46).

PTK2/FAK is an established driver of some tumors and is a target for amplification at 8q in primary hepatocellular carcinoma (HCC; ref. 47) and breast cancer (48). The number of copies of Chr 8q24.3 in the region harboring PTK2 ranged from three to nine in the CC and WU cohorts and given its association with poor overall survival, these data are consistent with earlier data, including a clear association between metastatic potential of uveal melanoma and 8q ploidy of five copies or more (13). Tumor MM127 harbored an A1031S alteration in PTK2 and no other detectable abnormalities. This alteration alters a highly conserved “DAKNL” motif in the C-terminal end of PTK2 that is conserved from humans to flies. It lies in a paxillin-binding subdomain of the FAT domain that is required for localizing PTK2 to focal adhesions in response to integrin stimulation (49, 50). We hypothesize that this p.A1031S alteration strengthens cellular adhesion mediated by FAK. Inhibition of FAK has been implicated in improving local control in HPV-negative head and neck squamous cell carcinoma (HNSCC; ref. 51) and in castrate-resistant prostate cancer (52) and might also be a therapeutic target in uveal melanoma. This is consistent with recent studies showing that oncogenic mutations found in GNAQ in uveal melanoma activate YAP through FAK and that inhibition of FAK reduces uveal melanoma growth (53). Chr 8q24 also harbors MYC, which is amplified in about 30% of uveal melanomas (54), but its expression was not significantly upregulated as a consequence of 8q24 amplification.

Integrated analyses also revealed that a cluster of genes on chr1p36 is associated with metastasis and worse overall survival compared with broad chromosomal changes.

At Chr 6q27, we obtained evidence for PHF10/BAF45a as a novel tumor suppressor for uveal melanoma on the basis of three tumors with definitive loss-of-function mutations: two tumors with overlapping homozygous deletion (HD) and one with a frameshift mutation and concomitant loss of the wild-type allele (MM133). GEP had classified one MM016 as a class 1B tumor and MM133 as a class 2 tumor although they lacked SF3B1 and BAP1 mutations respectively. However, in addition to the PHF10 frameshift mutation, MM133 also harbored a mutation in SF3B1 leading to a R625C alteration and was disomic for Chr 3 despite being classified as a class 2 tumor (Supplementary Table S2). We also identified a missense mutation in TCGA tumor A8KM (p.D453E, rs761295711). This mutation was very rare (frequency in Exac is 8.3 × 10⁻⁶) and although tumorigenicity of missense alterations is hard to predict, this alteration was predicted to be damaging. Tumor A8KM had also lost one copy of Chr 3 (Supplementary Table S1) and PRAME was upregulated but it lacked identified mutations in BAP1, SF3B1, or EIF1AX (Supplementary Table S2). Hence, the presence of alterations in PHF10 and BAP1 were mutually exclusive in the small number of tumors sampled. Loss of 6q where PHF10 resides was not correlated with worse overall survival in TCGA, nor were levels of PHF10.

PHF10 (AKA BAF45a) is a component of the PBAF complex (a SWI/SNF-like complex) that is involved in chromatin remodeling (55), which in turn can affect transcriptional regulation. During PHF10 silencing, other components of the PBAF complex (SMARCE1, SMARCC1, and SMARCA4; Supplementary Fig. S7) were upregulated, potentially as a compensation mechanism. RNA sequencing data were available for MM016 and A8KM and we identified a number of chromatin remodelers with altered expression that exhibited the same trend in cell lines with PHF10 knockdown. We confirmed alteration at the protein level of some of these including PCGF5, SMAD2, and JAZF1 (55). JAZF1 is required for ciliated cell differentiation in vitro (56) and is involved in transcriptional repression. It can be fused to other genes in endometrial stromal tumors where it disrupts the polycomb repressive complex 2 (PRC2), abolishes histone methyl transferase activity, and activates chromatin/genes normally repressed by PRC2 (57). PCGF5 encodes a component of PRC1. It is required for the differentiation of mouse embryonic stem cells (mESC) toward a neural cell fate where it functions both as a repressor for the SMAD2/TGFβ signaling pathway and as a facilitator for neural differentiation. Its loss impairs the reduction of H2AK119ub1 and H3K27me3 around neural-specific genes, keeping them repressed.

PHF10 loss also led to downregulation of adhesion and cell migration in Mel202 and 92.1 cell lines. This was not observed in Mel290, but this line lacks mutations in the known drivers BAP1, SF3B1, EIF1AX so is not a “typical” uveal melanoma cell line. Moreover, this line is very migratory and not very adhesive at baseline. Some transcripts such as ITGA7, ITGA10, JAM3, LAMC1, EPHB3, HES1, and PIK3CB that are involved in adhesion and migration were downregulated after PHF10 kd. However some such as EPHA3, PPP1R9B, ADAM15, POSTN, and COL4A1 were upregulated, highlighting the complex relationship between metastasis, migration, and adhesion. Further studies will be required to understand the role PHF10 plays in these processes.

Mutations in PHF10 that drive tumor development have not been described before, although mutations in other PBAF components such as ARID2, PBRM1, SMARCA4 (BRG), and SMARCB1 (BAF47) are implicated in other cancer types (58, 59). PHF10 lies in a region of Chr 6q that harbors an unidentified tumor suppressor for a variety of epithelial cancers (60–62), so it should also be strongly considered as a candidate for this elusive gene (63).

A.M. Bowcock is listed as a co-inventor on a patent entitled “Compositions and Methods for Detecting Cancer Metastasis” to Washington University, which is licensed to Castle Biosciences. A.D. Singh reports receiving speakers bureau honoraria from Eckert and Zeigler; holds ownership interest (including patents) in Aura Biosciences; and is a consultant/advisory board member for Immunocore and Isoaid. J.W. Harbour is listed as inventor of a patent entitled “Method for predicting risk of metastasis” and coinventor on a patent entitled “Compositions and Methods for Detecting Cancer Metastasis” to Washington University, both licensed to Castle Biosciences, and is a consultant/advisory board member for Castle Biosciences, Aura Biosciences, and Immunocore. No potential conflicts of interest were disclosed by the other authors.

Conception and design: H. Anbunathan, R. Verstraten, A.M. Bowcock

Development of methodology: H. Anbunathan, R. Verstraten, A.M. Bowcock

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.D. Singh, J.W. Harbour, A.M. Bowcock

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H. Anbunathan, R. Verstraten, A.M. Bowcock

Writing, review, and/or revision of the manuscript: H. Anbunathan, R. Verstraten, A.D. Singh, J.W. Harbour, A.M. Bowcock

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H. Anbunathan, A.M. Bowcock

Study supervision: A.M. Bowcock

This work was supported in part by NCI grant R01CA161870 (to A.M. Bowcock and J.W. Harbour) and R01CA125970 (to J.W. Harbour). We thank Asif Chowdhury for technical assistance, Anita Rogic for help with the migration assays, and Dr. Jacqueline Frost for editorial comments. We acknowledge core grant NCI No. CA1667 to MD Anderson that supports validation of cancer cell lines.

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