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. 2025 Apr 15;48(5):189–195. doi: 10.1097/CJI.0000000000000558

Immune Profiling of Uveal Melanoma Liver Metastases and Response to Checkpoint Inhibitors

Yusra F Shao *,✉, Yasmine Baca †, Andrew Hinton †, Joanne Xiu †, Ari VanderWalde †, Matthew Hadfield ‡, Soo J Park §, Sourat Darabi ∥, Takami Sato , Justin C Moser #
PMCID: PMC12052074  PMID: 40231356

Abstract

Responses to immune checkpoint inhibitors (ICIs) differ significantly between uveal melanoma (UM) and cutaneous melanoma (CM) patients. We investigated the immune profile of metastatic UM(mUM) patients and identified markers that are predictive of improved survival. Metastatic liver samples from 189 UM patients and 48 CM patients were analyzed at genomic and transcriptional levels, and survival analysis was performed on a subgroup of 76 ICI-treated mUM patients. UM liver metastases seem to preserve the genomic and immune characteristics of primary UM (pUM), with a low prevalence of ICI markers and high mutation rates of GNA11, GNAQ, BAP1, and SF3B. Compared with mCM, UM liver metastasis showed lower infiltration of several immune cells, but a greater proportion of M2 macrophages. Compared with UM liver metastases, CM liver metastases showed higher expression of G2M checkpoint and EF2 target genes. Among the mUM and mCM samples, expression of G2M and E2F pathway genes was highest in tumors with high TMB values and T-cell inflamed scores. A longer median overall survival (OS) was observed in mUM patients with higher expression of LAG3, HLA class I, or HLA class II; which may represent a small proportion of immune hot tumors. Expression patterns of G2M checkpoint and E2F targets suggest a possible contribution to immunotherapy response.

Key Words: uveal melanoma, liver metastasis, cutaneous melanoma, immune checkpoint inhibitor, tumor microenvironment

Uveal melanoma (UM) is the most common intraocular tumor arising from the pigmented layer of the eye.1 UM is rare, with an incidence of 5.2 cases per million per year in the United States.2 Although initial treatments, including radiation or enucleation, have a high success rate, approximately half of UM patients develop metastases within the first decade, at which point treatment options are more limited.3 Survival for patients with metastatic disease is limited to 6–12 months.4

For unclear reasons, UM has a propensity to metastasize to the liver. According to the Collaborative Ocular Melanoma Study (COMS) study, 93% of patients who died of metastatic UM (mUM) had metastasis to the liver, and 57% of patients had mUM exclusively in the liver.5 Several mechanisms have been proposed for this predilection, including increased expression of c-Met and CXCL12 by UM cells.6,7 However, additional investigations are needed to further elucidate the pathogenesis of liver metastasis from UM.

Treatment of mUM also poses a challenge. Tebentefusp is approved for the treatment of mUM in patients with the HLA-A 02:01 phenotype.8 Treatment options for all other patients remain limited. Recent clinical trials have shown improvements in survival in some mUM patients receiving immune checkpoint inhibitors (ICIs).9,10 However, these studies did not describe biomarkers that may correlate with ICI response, which are critical for improved patient selection. To better understand the biology of UM liver metastases, the aim of this study was to compare the molecular and immune profiles of liver metastases from UM and cutaneous melanoma (CM). We also aimed to correlate immune biomarkers with the response to ICI therapy in UM liver metastases.

METHODS

Patient Cohort

Two patient cohorts were identified. Cohort one included patients with UM liver metastasis who underwent genomic and transcriptomic profiling at Caris Life Sciences. This cohort was compared with samples from CM patients with liver metastasis who served as controls for RNA analysis. Cohort 2 included ICI-treated UM patients with liver metastasis for whom survival data were available from the CODEai database, which integrates molecular data with treatment information and clinical outcomes data. This study was conducted in accordance with the guidelines of the Declaration of Helsinki, Belmont report, and the US Common Rule. In keeping with the 45 CFR 46.101(b)(4), this study was performed utilizing retrospective, deidentified clinical data. Therefore, this study is considered exempt from IRB approval, and no patient consent was necessary from the subject.

Next-generation Sequencing (NGS)

NGS was performed on genomic DNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor samples using the NextSeq platform (Illumina, Inc.). Before molecular testing, tumor enrichment was achieved by harvesting targeted tissue using manual microdissection techniques. Matched normal tissue was not sequenced. A custom-designed SureSelect XT assay was used to enrich 592 whole-gene targets (Agilent Technologies). All variants were detected with >99% confidence based on allele frequency and amplicon coverage, with an average sequencing depth of coverage > 500x and an analytic sensitivity of 5%. The identified genetic variants were interpreted by board-certified molecular geneticists and categorized as pathogenic, likely pathogenic, or variant of unknown significance, according to the American College of Medical Genetics and Genomics (ACMG) standards. Only pathogenic and likely pathogenic variants (PVs) were included in the comparative analyses.

Whole Transcriptome Sequencing (WTS)

RNA expression data were evaluated for mRNAs isolated from formalin-fixed paraffin-embedded tumor samples using the Agilent SureSelect Human All Exon V7 bait panel (Agilent Technologies) and Illumina NovaSeq platform (Illumina Inc.). Transcripts per million (TPM) were reported. The immune cell fraction was calculated by quanTIseq using these transcriptomic data.11 In addition, mRNA expression data were subjected to pathway enrichment analysis via gene set enrichment analysis (GSEA)12 (for which the significance was determined by a P-value <0.05 and an FDR q value <0.25), differential gene expression analysis, and the T-cell inflamed score.13

Tumor Mutational Burden (TMB)

TMB was measured by counting all nonsynonymous missense, nonsense, inframe insertion/deletion, and frameshift mutations found per tumor that had not been previously described as germline alterations in the dbSNP151 and Genome Aggregation Database (gnomAD), or benign variants identified by Caris geneticists, and the value was adjusted by dividing by a factor of 1.2 to ensure that the fraction of TMB-high matched the observed published clinical data.14 A cutoff point ≥10 mutations per MB was used based on the KEYNOTE-158 pembrolizumab trial,15 which showed that patients with a TMB ≥ 10 mt/MB across several tumor types had higher response rates than patients with a TMB of <10 mt/MB. Caris Life Sciences is a participant in the Friends of Cancer Research TMB Harmonization Project.16

Microsatellite Instability (MSI)

A combination of multiple test platforms was used to determine the MSI status of the tumors profiled, including fragment analysis (FA, Promega), IHC MLH1, M1, MSH2, G2191129, MSH6 (44), PMS2, and EPR3947 antibodies (Ventana Medical Systems, Inc.) and NGS (for tumors tested with the NextSeq platform, 7,000 target microsatellite loci were examined and compared with the reference genome hg19 from the University of California).

Immunohistochemistry (IHC)

IHC was performed on full formalin-fixed paraffin-embedded (FFPE) sections of glass slides. The slides were stained using automated staining techniques, per the manufacturer’s instructions, and were optimized and validated per Clinical Laboratory Improvement Amendments and College of American Pathologists (CLIA/CAP) and ISO requirements. The results were categorized as positive or negative by defined thresholds specific to each marker based on published clinical literature that associates biomarker status with patient responses to therapeutic agents. A board-certified pathologist evaluated all the IHC results independently. The primary antibody used against PD-L1 was SP142 (Spring Biosciences). The staining was regarded as positive if its intensity on the membrane of the tumor cells was 2+ (on a semiquantitative scale of 0–3: 0 for no staining, 1+ for weak staining, 2+ for moderate staining, or 3+ for strong staining) and the percentage of positively stained cells was >1%.

Data and Statistical Analysis

The molecular alterations in the cohorts were analyzed via the χ2 test or Fisher exact test. TMB and tumor microenvironment cell fractions were analyzed using nonparametric Kruskal-Wallis tests. A P-value <0.05 was considered to indicate a trending difference. P-values were further corrected for multiple comparisons using the Benjamini-Hochberg method to avoid type I errors, and an adjusted P-value (ie, q value) of <0.05 was considered to indicate a significant difference.

Survival Analysis

The time from the first dose of ICI treatment to the date of last contact was obtained from the CODEai database using insurance claims data and reported as a surrogate for overall survival (OS). Kaplan-Meier estimates were calculated for molecularly defined patient cohorts. P-values <0.05 indicated statistical significance. The expression levels of PRAME, LAG3, HLA I, and HLA II were determined from WTS data, with high and low expression assigned according to median expression values.

RESULTS

A total of 189 samples obtained from patients with UM liver metastasis were included in cohort one and were compared with samples from 48 CM liver metastases. Cohort 2 included 76 ICI-treated patients with UM liver metastasis for whom survival data were available in CODEai.

Genomic Characteristics of UM and CM Liver Metastasis

Among UM liver metastasis samples, GNA11 (50%, n=95/189), GNAQ (43%, 81/189), BAP1 (56%, 105/189), and SF3B (15%, 29/189) had high mutation rates. Twelve percent (n=23/189) of the samples were positive for PD-L1 expression, 1.6% (3/189) were TMB-high, and none were MSI-high (Fig. 1).

FIGURE 1.

FIGURE 1

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Oncoprint of clinically relevant alterations in 189 UM liver metastases. Blue=altered or high expression. White=wild-type or low expression.

QuanTIseq analysis of CM and UM liver metastasis showed significantly lower infiltration of M1 macrophages (median cell abundance 0.9% vs. 2.4%), monocytes (nonzero 8.2% vs. 43.8%), CD4-positive T cells (nonzero 11.5% vs. 47.9%), and regulatory T cells (median cell abundance 0.8% vs. 1.4%) in UM liver metastasis compared with CM (P<0.001, Fig. 2). In contrast, a significantly greater proportion of M2 macrophages was observed in UM liver metastasis compared with CM liver metastasis (median cell abundance 7.8% vs. 2.8%, P<0.0001).

FIGURE 2.

FIGURE 2

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Comparison of TME characteristics (quanTIseq) between UM samples and CM samples within liver metastases. The red asterisks in panel A indicate P<0.0001 for lower abundance in the mUM samples compared with the mCM samples. The black asterisks indicate P<0.0001 for greater abundance in mUM compared with mCM.

GSEA of UM and CM liver metastases revealed significantly greater expression of G2M checkpoint and EF2 target genes in CM samples compared with UM samples (Fig. 3A). When comparing T-cell–inflamed, T-cell–intermediate, and T-cell–noninflamed tumors among liver metastases from both CM and UM, the average Z-scores for both G2M and E2F pathways were highest for inflamed samples and lowest for noninflamed samples (P<0.001 and <0.01, respectively) (Fig. 3B). A similar trend was observed when comparing TMB values, with higher average Z-scores for the G2M and E2F pathways being greater for TMB-high compared with TMB-low liver metastases (P<0.001) (Fig. 3C).

FIGURE 3.

FIGURE 3

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Significant GSEA results for UM samples versus CM samples within liver metastasis. A, GSEA pathways that are enriched in CM liver metastasis compared with UM liver metastasis. B, Z-score distribution of the G2M and E2F pathways for T-cell inflamed values (with combined uveal melanoma and cutaneous melanoma). C, Z-score distribution of the G2M and E2F pathways for TMB values (with combined uveal melanoma and cutaneous melanoma).

Survival Analysis of UM Liver Metastasis

In cohort 2, there was no significant difference in the median OS of patients with liver-metastasized UM sorted by high or low PD-L1 expression (P=0.966) (Fig. 4A) or by high or low PRAME expression (P=0.78) (Fig. 4B). A comparison of patients sorted by LAG3 expression revealed longer OS in the LAG3-high expression group than in the LAG3-low expression group (median OS 9.7 vs. 3.5 mo, P=0.45, 95% CI: 0.57–3.56) (Fig. 4C). Similarly, comparison of patients sorted by HLA class II expression revealed longer OS in the HLA class II-high subgroup than in the HLA class II-low subgroup (median OS 10.4 mo vs. 2.3 mo, P=0.414, 95% CI: 0.49–5.52) (Fig. 4D). A comparison of patients sorted by HLA class I expression revealed longer OS in the HLA class I-high subgroup than in the HLA class I-low subgroup (median OS of 13.2 mo vs. 2.5 mo, P=0.157, 95% CI: 0.72–6.61) (Fig. 4E). BAP1 and SF3B1 mutation status were not associated with survival differences according to ICI treatment.

FIGURE 4.

FIGURE 4

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CODEai analysis for ICI-treated uveal melanoma patients with liver metastasis. A, PD-L1 high versus PD-L1 low. B, PRAME high versus PRAME low. C, LAG3 high versus LAG3 low. D, HLA class II high versus HLA class II low. E, HLA class I high versus HLA class I low.

DISCUSSION

In this study, we investigated the differences in the immune profiles of UM liver metastasis and CM liver metastasis and evaluated the correlation of immune marker expression with response to ICIs. The genomic and immune landscapes of primary UM have been extensively described, and these are known to be distinct from those of CM. While CM commonly carries high TMB with mutations in the KRAS/NRAS/BRAF pathways, 85% of primary UM are known to carry GNAQ/GNA11, 45% carry BAP1, and 25% carry SF3B1 mutations.17,18 Despite ample knowledge about the biology of primary UM, knowledge about the molecular and immune landscape of muM remains limited, given the overall rarity of this disease. We aimed to fill this gap in our study with a large dataset of 189 liver mUM samples that underwent multiomics and immune profiling. In our study, 50% of liver mUM carried GNA11, and 43% carried GNAQ mutations. This finding is slightly different than what was observed in the primary UM of the choroid, where GNAQ mutations are more prevalent.17 BAP1 and SF3B1 mutations were detected in 50% and 15%, respectively, of UM liver metastases, which is concordant with findings from primary UM.18 As such, liver mUM seems to conserve the genetic profile of primary UM.

UM samples exhibited a low prevalence of commonly used biomarkers of ICI response (PD-L1, TMB, MSI) in our study. Multiomic comparisons of UM and CM liver metastases were performed in a smaller study including 28 UM and 38 CM liver metastasis samples, reporting a relative lack of traditional ICI biomarkers in UM, with 6% of liver mUM samples expressing PD-L1, which has been associated with poor outcomes in UM.19,20 This percentage is much lower than the PD-L1 positivity rate observed in our study sample (12%). Furthermore, the study showed no difference in immune cell infiltrates between liver mUM and CM samples.19 Our study showed a significantly greater abundance of M2 macrophages and a lower abundance of M1 macrophages in liver mUM samples compared with CM samples. This finding is consistent with findings from primary UM patients known to have a greater abundance of immune-suppressing M2 macrophages. Furthermore, a greater abundance of M2 macrophages in primary UM samples is associated with worse survival.21 We could not confirm these findings in our study due to the small sample size.

G2M checkpoint and E2F target genes are involved in cell proliferation. The E2F and G2M pathways were previously reported to be enriched in CM samples relative to UM samples, although the clinical implications of lower expression in the context of UM were not investigated further.19 Studies in large breast cancer cohorts have reported associations between poor prognosis and high-expression of both the E2F and G2M pathways.22,23 A higher E2F pathway score was further associated with increased immune cell infiltration in breast tumors, and the authors suggested that this score may be valuable in predicting response to ICI therapy.22 In our study, the expression of the E2F and G2M pathways was associated with other markers of immune response, such as the TMB and T-cell inflamed score. The majority of higher inflamed tumors and TMB-high tumors identified in this combined analysis likely represented CM samples. Therefore, these results should be generalized to UM with caution.

Due to its morphologic and functional characteristics, the eye is considered an immune-privileged site with unique immune-suppressing responses, such as anterior chamber-associated immune deviation.24 In the background of this, primary UMs are considered immune-silent with low PD-L1 expression, low TMB, and overall low leukocyte infiltrate fraction.25,26 Our study showed that these immune features of UM persist in patients with metastatic disease, possibly explaining the decreased response to ICIs. Tebentefusp, a bispecific T-cell engager able to activate the immune response at low immune infiltrate levels, has shown an OS benefit in patients with mUM.27 However, tebentefusp is approved for only patients with the HLA-A* 02:01 phenotype, and a majority of patients will continue to require alternative treatments such as ICIs.

Although ICI therapy has exhibited low response rates in metastasized UM patients, a small proportion of patients will have long-lasting responses. For example, in the GEM 1402 study of ipilimumab and nivolumab in mUM patients, 4 patients remained on treatment at the end of follow-up. Therefore, there is a need to identify predictive biomarkers of response to ICIs in patients with mUM. In our study, we observed no differences in OS among ICI-treated patients sorted according to PD-L1 or PRAME status. A comparison of ICI-treated patients according to the expression status of LAG3, HLA class II, and HLA class I genes revealed longer OS in the high-expression groups (median difference of 6.2, 8.1, and 10.7 mo, respectively). However, due to the small cohort sizes (n=25, n=14, n=19), the observed differences were not statistically significant. Notably, high LAG3 expression detected by IHC was associated with longer median OS in CM patients treated with relatlimab and nivolumab.28 The combination of relatlimab and nivolumab in patients with mUM is being studied in a phase 2 trial, but the results are pending.29 High LAG3 expression is likely represented in a subset of tumors in which LAG3 drives a negative feedback loop to create an active immune environment.30

It is interesting to note that patients with high LAG3 and HLA class II expression exhibited improved survival in our study, as both are reportedly highly expressed in high-risk UM patients and are suggested targets for adjuvant immunotherapy.31 In addition, low expression of HLA class I has been associated with improved survival in primary UM patients, as tumor recognition and clearance are thought to occur via natural killer cells.32 However, in the context of ICIs, downregulation of HLA class I in CM patients is associated with worse survival, which is in line with our findings in ICI-treated mUM patients.33

Limitations

Although a large population of liver mUM samples was included in our dataset, comprehensive immune and genomic profiling data were unavailable for many samples from patients receiving ICI therapy. This limits our ability to draw definitive conclusions about the correlation between biomarkers and survival for trends observed in small sample sizes. Furthermore, our retrospective assessment of survival was based on last contact with the health care system as per insurance claims data and may not be representative of real OS. In addition, our study used RNA data to determine the expression of biomarkers for outcome analysis, which limits comparisons to studies in which IHC analysis was used.

CONCLUSIONS

UM liver metastases seem to preserve the genomic and immune characteristics of primary UM and differ significantly from CM liver metastases. ICI-treated UM patients with liver metastasis exhibited longer median survival with high expression of LAG3, HLA class I, and HLA class II, likely representing a small proportion of immune hot tumors responding to ICIs. More comprehensive characterization of emerging immunotherapy biomarkers and development of novel treatment strategies targeting the immune system are needed for this rare but lethal disease.

CONFLICTS OF INTEREST/FINANCIAL DISCLOSURES

S.D. reports a consultant role at BostonGene. J.C.M. reports consultant/advisory roles at BMS (6/2020, 12/2021), Amunix (02/2021), Thirona Bio (03/2021-present), Adagene (11/2021-8/2022), Imaging Endpoints (3/2022-present), Boxer Capitol (2021), Oberland Capital (2022), IQVIA (2023), Genome Insight (2023), Incyte (2023), Novotech (2023-present), Red Arrow Therapuetics (2023-present), Werewolf Therapuetics (2023), and the Caris Molecular Tumor Board (7/2021-present). J.C.M. also reports research support from NovoCure (Inst), Genentech (Inst), Alpine Immune Sciences (Inst), Amgen (Inst), Trishula Therapeutics (Inst), BioEclipse Therapeutics (Inst), FujiFilm (Inst), ImmuneSensor (Inst), Simcha (Inst), Repertoire Immune Sciences (Inst), Nektar Therapuetics (Inst), Synthorx Inc (Inst), Istari Oncology (Inst), Ideaya Biosciences (Inst), Rubius (Inst), University of Arizona (Inst), Senwha (Inst), Storm Therapeutics (Inst), Werewolf Therapeutics (Inst), Fate Therapeutics (Inst), Y-Mab (Inst), Agenus (Inst), T-Scan (Inst), Iovance (Inst), and Adaptimmune (Inst). J.C.M. also reports honoraria from Caris Life Sciences(2019), Daiichi-Sankyo (2019), and Tgen (2020). J.C.M. also reports speaker bureau roles at Caris Life Sciences (2022-present), Immunocore (2021-present), and Castle Biosciences (2023-present). All the remaining authors have declared that there are no financial conflicts of interest with regard to this work.

Footnotes

The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The deidentified sequencing data are owned by Caris Life Sciences and cannot be publicly shared due to the data usage agreement. Qualified researchers can apply for access to these summarized data by contacting Joanne Xiu, PhD (jxiu@carisls.com) and signing a data usage agreement.

Contributor Information

Yusra F. Shao, Email: shaoy@karmanos.org.

Yasmine Baca, Email: ybaca@carisls.com.

Andrew Hinton, Email: ahinton@carisls.com.

Joanne Xiu, Email: jxiu@carisls.com.

Ari VanderWalde, Email: avanderwalde@carisls.com.

Matthew Hadfield, Email: matthew_hadfield@brown.edu.

Soo J. Park, Email: sjp047@health.ucsd.edu.

Sourat Darabi, Email: Sourat.Darabi@hoag.org.

Takami Sato, Email: Takami.Sato@jefferson.edu.

Justin C. Moser, Email: jmoser@honorhealth.com.

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