Gene Expression Profiling in Uveal Melanoma: A Cutting-Edge Innovation in Ophthalmic Pathology

Ling Paulina Gronczewska

Pathologists are at the forefront of personalised medicine, using molecular tools to tailor diagnoses, prognoses, and treatments. In ophthalmic pathology, one of the most significant developments in recent years has been the integration of gene expression profiling (GEP) into the management of uveal melanoma, the most common primary intraocular malignancy in adults in the United Kingdom (1). Traditionally risk stratification has been based on tumour size, cell morphology, and mitotic activity (2). The development of GEP assays, particularly DecisionDx-UM, has significantly improved prognostic accuracy in uveal melanoma, paving the way for personalised care based on molecular tumour profiling (3).

Understanding Gene Expression Profiling in Uveal Melanoma

Uveal melanoma is a highly aggressive cancer with a strong tendency to metastasise, particularly to the liver, which accounts for around 95% of metastases (4). Despite there being options for local treatment, approximately 50% of patients eventually develop systemic metastases (5). Hence, accurate risk stratification is essential for guiding surveillance and therapeutic decisions.

The DecisionDx-UM test analyses the expression of 15 genes—12 discriminating and 3 control genes—using RNA extracted from fine-needle aspiration biopsy samples (6). It classifies tumours into three categories: Class 1A (low metastatic risk), Class 1B (intermediate risk), and Class 2 (high risk),  with studies having demonstrated that Class 2 tumours carry approximately a 70% risk of metastasis within five years, while Class 1A tumours have a risk of less than 5% (6, 7).

This molecular classification has been shown to outperform chromosomal analysis methods such as monosomy 3 detection and cytogenetic profiling in predictive accuracy and reproducibility (8).

Clinical Impact of GEP in Uveal Melanoma

1. Personalised Surveillance

GEP results directly inform surveillance intensity. Patients with Class 2 tumours are subjected to more frequent liver imaging, often ranging from every 3 to 6 months, to detect metastases early, while on the other hand, Class 1A patients may avoid excessive imaging, reducing unnecessary anxiety and healthcare costs (9).

2. Tailored Therapeutic Approaches

High-risk patients may be referred for adjuvant clinical trials aimed at delaying or preventing metastatic spread, such as those investigating immune checkpoint inhibitors or liver-directed therapies (10, 11). In some cases, GEP results also influence decisions regarding local therapy (12).

3. Patient Empowerment and Psychological Impact

Studies have shown that molecular prognostication provides psychological benefits, even when results are unfavourable. Patients report improved decision-making capacity and reduced uncertainty, especially when results are explained in conjunction with genetic counselling (13).

4. Advancement of Research and Registry Data

Large-scale use of GEP may also be able to facilitate the creation of prospective registries that track metastatic development, treatment outcomes, and quality of life, and various other metrics contributing to the evolving landscape of UM research (14).

Barriers to Widespread Implementation

Despite its benefits, several barriers hinder the global adoption of GEP in UM care:

1. Cost and Insurance Coverage

The DecisionDx-UM test costs around USD 8,000, and while it is covered by Medicare in the U.S., questions remain about its availability in low- and middle-income countries (15). This financial barrier is significant for both patients and healthcare systems.

2. Infrastructure and Technical Expertise

FNAB requires skilled ophthalmic oncologists, and GEP analysis must be performed in specialised laboratories. Many centres lack the infrastructure or trained personnel to perform these procedures effectively (16, 17).

3. Limited Clinician Familiarity and Confidence

Some clinicians, especially in community or non-academic settings, are unfamiliar with the interpretation of GEP results or are hesitant to rely on molecular data over traditional histopathological features (18, 19).

4. Ethical and Psychosocial Concerns

Delivering high-risk prognoses without adequate genetic counselling may increase patient anxiety or lead to misunderstandings about prognosis, particularly among populations with low health literacy (20).

Strategies for Overcoming Barriers

To address these challenges, several strategies can be implemented:

1. Health Policy and Reimbursement Reform

Governments and insurers should be encouraged to cover GEP testing by demonstrating its long-term cost-effectiveness in reducing metastatic surveillance burdens and improving patient outcomes (21).

2. Centralized Testing and Telemedicine

Peripheral hospitals can collaborate with centralized laboratories for testing, while teleophthalmology platforms can enable remote genetic counselling and GEP interpretation (22).

3. Clinician Education and Multidisciplinary Training

Interdisciplinary tumour boards, CME courses, and digital toolkits can enhance clinician familiarity with GEP and its integration into treatment planning (23).

4. Integration of Psychosocial Support

Providing genetic counselling as part of the testing protocol improves understanding, reduces distress, and supports shared decision-making between patients and clinicians (24).

Conclusion

Gene expression profiling has revolutionised the management of uveal melanoma, providing accurate prognostic information that can inform further steps. Unlike traditional histopathological techniques,  GEP offers more meaningful classification that guides surveillance, informs treatment choices, and empowers patients. As the role of the pathologist continues to evolve, innovations like DecisionDx-UM illustrate how cutting-edge molecular diagnostics are reshaping ocular oncology.

However, to achieve equitable access to this exciting new technology, healthcare systems must address barriers related to cost, infrastructure, education, and psychosocial support. With coordinated efforts across policy, practice, and education, GEP can be fully integrated into frontline care, fulfilling its promise as a cornerstone of personalised medicine in ophthalmology.

References

  1. Types of eye cancer | Cancer Research UK [Internet]. www.cancerresearchuk.org. Available from: https://www.cancerresearchuk.org/about-cancer/eye-cancer/stages-types/types.
  2. Shields CL, Furuta M, Thangappan A, et al. Metastasis of uveal melanoma millimeter-by-millimeter in 8033 consecutive eyes. Arch Ophthalmol. 2009;127(8):989–98.
  3. Onken MD, Worley LA, Char DH, et al. Collaborative Ocular Oncology Group report number 1: prospective validation of a multi-gene prognostic assay in uveal melanoma. Ophthalmology. 2012;119(8):1596–603.
  4. Woodman SE. Metastatic Uveal Melanoma. The Cancer Journal. 2012;18(2):148–52.
  5. Jager MJ, Shields CL, Cebulla CM, et al. Uveal melanoma. Nat Rev Dis Primers. 2020;6(1):24.
  6. Plasseraud KM, Cook RW, Tsai T, et al. Clinical performance of a prognostic gene expression assay in uveal melanoma. JAMA Ophthalmol. 2017;135(5):472–8.
  7. Walter SD, Chao DL, Feuer W, Schiffman J, Char DH, Harbour JW. Prognostic Implications of Tumor Diameter in Association With Gene Expression Profile for Uveal Melanoma. JAMA Ophthalmol. 2016 Jul 1;134(7):734-40. doi: 10.1001/jamaophthalmol.2016.0913. PMID: 27123792; PMCID: PMC4966166.
  8. Harbour JW, Onken MD, Roberson ED, et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science. 2010;330(6009):1410–3.
  9. Yağmur Seda Yeşiltaş, Zabor EC, Wrenn J, Oakey Z, Singh AD. Surveillance for Metastasis in High-Risk Uveal Melanoma Patients: Standard versus Enhanced Protocols. Cancers [Internet]. 2023 Oct 17 [cited 2024 May 20];15(20):5025–5. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10605386/.
  10. Algazi AP, Tsai KK, Shoushtari AN, et al. Clinical outcomes in metastatic uveal melanoma treated with PD-1 and PD-L1 antibodies. Cancer. 2016;122(21):3344–53.
  11. Zager JS, Orloff M, Pier Francesco Ferrucci, Glazer ES, Ejaz A, Richtig E, et al. FOCUS phase 3 trial results: Percutaneous hepatic perfusion (PHP) with melphalan for patients with ocular melanoma liver metastases (PHP-OCM-301/301A). Journal of Clinical Oncology. 2022 Jun 1;40(16_suppl):9510–0.
  12. Shields CL, Shields JA. Management of posterior uveal melanoma: past, present, and future. Indian J Ophthalmol. 2018;66(12):1953–66.
  13. Williams BK, Siegel JJ, Alsina KM, Johnston L, Sisco A, LiPira K, et al. Uveal Melanoma Patient Attitudes Towards Prognostic Testing Using Gene Expression Profiling. Melanoma Management. 2022 Sep;9(3).
  14. Aaberg Thomas M, Covington Kyle R, Tsai T, Shildkrot Y, Plasseraud Kristen M, Alsina Katherina M, et al. Gene Expression Profiling in Uveal Melanoma: Five-Year Prospective Outcomes and Meta-Analysis. Ocular Oncology and Pathology [Internet]. 2020 [cited 2026 Jan 15];6(5):360–7. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7574618/.
  15. Castle Biosciences Inc. DecisionDx-UM billing and reimbursement information. [Internet]. 2023 [cited 2025 Apr 29]. Available from: https://castlebiosciences.com.
  16. Safety of Fine-needle Aspiration Biopsy in Choroidal Melanoma – Retina Today [Internet]. Retina Today. 2011 [cited 2025 May 12]. Available from: https://retinatoday.com/articles/2011-nov-dec/safety-of-fine-needle-aspiration-biopsy-in-choroidal-melanoma.
  17. Plasseraud KM, Wilkinson JK, Oelschlager KM, Poteet TM, Cook RW, Stone JF, et al. Gene expression profiling in uveal melanoma: technical reliability and correlation of molecular class with pathologic characteristics. Diagnostic Pathology. 2017 Aug 4;12(1).
  18. Rao RC, Honavar SG. Ethical dilemmas in the use of genetic testing for ocular tumors. Indian J Ophthalmol. 2018;66(3):371–4.
  19. Chattopadhyay C, Kim DW, Gombos DS, et al. Uveal melanoma: from diagnosis to treatment and the science in between. Cancer. 2016;122(15):2299–312.
  20. Chien JL, Sioufi K, Yang D, et al. Telepathology and digital imaging in ocular pathology. Eye (Lond). 2019;33(12):1953–9.
  21. Valsangkar NP, Downs K, Chamberlain M, et al. The role of molecular testing in the management of uveal melanoma. Cancer. 2021;127(12):1929–39.
  22. Cook SA, Hussain RN, Damato B. The value of prognostic testing for uveal melanoma: a patient perspective. Eye (Lond). 2021;35(2):552–7.

Leave a Reply