Unravelling the Molecular Pathways of Feline Diffuse Iris Melanoma Progression

Unravelling the Molecular Pathways of Feline Diffuse Iris Melanoma Progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unravelling the Molecular Pathways of Feline Diffuse Iris Melanoma Progression D Kayes, B Blacklock, R McGeachan, E Scurrell, K Donnelly, L Murphy, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6228571/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Feline diffuse iris melanoma (FDIM) is the most common primary ocular tumour in cats, with high metastatic potential. Greater intraocular invasion correlates with increased mortality. No effective therapeutics exist for metastatic disease, partly due to a lack of known molecular targets associated with aggressive tumour behaviour. Here, we define the transcriptomic landscape of FDIM in treatment-naïve cats using bulk RNA sequencing on laser capture microdissection and core biopsy specimens from formalin-fixed paraffin-embedded tissue. Samples included ‘iris melanosis’ (dysplastic melanocytes confined to the anterior iris; n = 7), ‘early FDIM’ (neoplastic melanocytes confined to the iris stroma; n = 13), and ‘late FDIM’ (neoplastic infiltration into the ciliary body and sclera; n = 13). Iris melanosis exhibited genetic overlap with early FDIM, supporting its reclassification as ‘melanoma in situ.’ Early FDIM showed upregulation of genes linked to tumour initiation, immune recruitment, and motility (e.g., STOX1, PEG3, XIAP, MCAM, VIM ). Late FDIM exhibited immune microenvironment remodelling, immune evasion, and apoptosis inhibition (e.g., BIRC2, BIRC5, CCL2, HAVCR2 ), with downregulation of FOX1, FOXC2 , and SOX11 . These results provide critical biomarkers of disease progression, which may aid in the development of more accurate prognostic tests and more effective targeted therapies for FDIM. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Eye cancer Biological sciences/Cancer/Metastases Biological sciences/Cancer/Oncogenes Biological sciences/Cancer/Tumour biomarkers Biological sciences/Cancer/Tumour immunology Health sciences/Oncology Health sciences/Diseases Health sciences/Diseases/Eye diseases Biological sciences/Genetics/Cancer genetics Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Genetic markers Feline diffuse iris melanoma melanoma metastasis transcriptome gene expression uveal melanoma Figures Figure 1 Figure 2 Introduction Feline diffuse iris melanoma (FDIM) is the most common primary ocular tumour in the cat, leading to significant infiltrative destruction of the globe, glaucoma, and death secondary to metastatic disease [ 1 – 3 ]. FDIM originates from melanocytes lining the anterior surface of the iris [ 4 ]. Iris melanosis, defined as dysplastic melanocytes lining the anterior iris in up to three layers, is considered a benign precursor lesion, with neoplastic transformation currently characterised by invasion of the underlying iris stroma [ 4 ]. Early FDIM, where there is no evident thickening of the iris, can be clinically indistinguishable from iris melanosis [ 5 ]. Iris biopsy has been described as a useful adjunctive diagnostic tool for differentiating between iris melanosis and early FDIM [ 4 ]. The metastatic potential of FDIM is significant, with metastasis occurring in 19–63% of patients with FDIM, with the rate of metastasis proportional to the severity of ocular invasion [ 3 – 5 , 7 ]. Patients with early FDIM, where neoplastic melanocytes are confined to the iris and trabecular meshwork, have similar survival times as age-matched control cats [ 6 ]. In contrast, patients with late FDIM, where neoplastic melanocytes infiltrate into the iris, ciliary body and sclera, have decreased survival times and an increased metastatic rate [ 6 , 7 ]. Currently, there are no effective treatment options for metastatic FDIM, making early enucleation the preferred intervention to prevent metastasis [ 5 ]. The genetic and transcriptomic landscape of FDIM remains largely unexplored. A pilot study using targeted quantitative real-time polymerase chain reaction (RT-qPCR) discovered dysregulation in key genes, including KIT, LTA4, GNAQ, GNA11, BRAF and RASF1 in cats with FDIM [ 8 ]. These findings suggest that FDIM may share genetic mechanisms with human uveal melanoma (UM), highlighting the potential for cats to serve as a valuable model for studying human disease. Despite this promising insight, the transcriptomic landscape of FDIM has yet to be comprehensively investigated. The aim of this study was to define the transcriptomic landscape of FDIM to uncover the gene expression pathways that underpin disease progression. Using formalin-fixed, paraffin-embedded and laser microdissected tissue from patients with iris melanosis, early FDIM and late FDIM, we show for the first time that iris melanosis is a malignant precursor lesion, with genetic overlap with early FDIM. The transcriptomic changes that are associated with FDIM initiation and evolution to a malignant tumour phenotype are elucidated, identifying novel therapeutic and prognostic markers. Results Late feline diffuse iris melanoma is associated with increased metastasis and decreased survival. We recruited seven cats with iris melanosis (Fig. 1 A), 13 cats with early FDIM (Fig. 1 B), and 13 cats with late FDIM (Fig. 1 C). Clinicopathologic data are summarised in Fig. 1 D and Supplementary Table 1. In our dataset, patients with late FDIM were significantly older individuals (p < 0.001), had a higher incidence of secondary glaucoma (p = 0.005) and tumour histopathology exhibited significantly higher numbers of mitotic figures per high power field (p < 0.001). All patients with late FDIM showed marked local extension (Supplementary Table 1), including into the iridocorneal angle or trabecular meshwork (3/13 cats, 23%), the scleral venous plexus (9/13 cats, 69%), choroid (5/13 cats, 38%), or episcleral tissues (3/13 cats, 23%) with additional extension into the extraocular muscles in one cat (1/13, 8%). Metastatic disease to the liver, spleen or lungs was confirmed or suspected in 4/13 (31%) patients with late FDIM and no patients with early FDIM or melanosis ( Fig. 1 D and Supplementary Table 1 ) . Survival data was available for three cats with iris melanosis, six cats with early FDIM and seven cats with late FDIM. Kaplan-Meier survival analysis (Fig. 1 E) showed a significantly worse survival for patients with late FDIM compared to those with early FDIM (p = 0.044), with a median survival time of 9 and 27.5 months, for late and early FDIM, respectively. Our findings underscore the aggressive nature of late FDIM, with higher metastatic potential, and significantly reduced survival compared to early FDIM. Melanosis, early FDIM and late FDIM represents progressive disease In this study, we sought to elucidate the molecular mechanisms underpinning the progression from melanosis to early and late FDIM in cats (Fig. 2 ). Initially, we examined gene expression clustering, which revealed distinct and separate clustering of late FDIM and iris melanosis samples, reflecting significant differences in their gene expression profiles ( Fig. 2 A ) . In contrast, early FDIM samples exhibited overlap with both groups, suggesting a transitional transcriptomic landscape during the progression from melanosis to late FDIM. The results suggest that iris melanosis is a malignant pre-cursor lesion, showing genetic overlap with early FDIM. Early FDIM Is Characterised by Upregulation of Cancer-Associated and Immune-modulatory Genes Next, we explored the transcriptomic landscape associated with the progression from melanosis to early FDIM. Comparative gene expression analysis revealed 91 upregulated and 7 downregulated genes in early FDIM compared to melanosis (Fig. 2 B). Among the upregulated genes were those linked to cancer formation (e.g., STOX1, PEG3, XIAP ) and immune cell recruitment (e.g., CCL28, VIM ), suggesting a shift towards increased cellular proliferation and remodelling of the immune microenvironment. Key biological themes identified by ingenuity pathway analysis (IPA) included cancer, organismal injury and abnormality, cell-cell signalling and interaction, cellular assembly and organisation, and hereditary disorder (Fig. 2 Bi). Notably, we identified significant upregulation of melanoma associated molecules MCAM (melanoma cell adhesion molecule) and LRCH1 (leucine rich repeats and calponin homology domain containing 1) in early FDIM, highlighting their potential as early indicators of disease progression from melanosis to early FDIM. Thus, we demonstrate that the progression from iris melanosis to early FDIM involves dysregulation of multiple cancer-associated genes, alterations in cell replication pathways, and modulation of the immune microenvironment. Late FDIM shows further, extensive transcriptomic reprogramming and immune modulation Next, we explored the transcriptomic landscape of late FDIM in comparison to melanosis (Fig. 2 C) and early FDIM (Fig. 2 D). Late FDIM showed a marked divergence in its molecular profile, with 101 upregulated and 146 downregulated genes compared to early FDIM, and 595 upregulated and 371 downregulated genes compared to melanosis. These findings demonstrate that the molecular profile of late FDIM diverges significantly from earlier disease stages. Compared to early FDIM, late FDIM demonstrates progression reflects advanced disease stages which are characterised by transcriptional reprogramming and immune modulation. Dysregulation of developmental transcription factors, including FOXC1, FOXC2 , and SOX11 , suggests increased differentiation and proliferation activity. Olfactory receptor pathways and hedgehog signalling networks were notably dysregulated, indicating broader systemic impacts on cellular communication and development. Genes involved in embryonic and organ development, such as SHH , NEUROG3 , and MAFA , were significantly dysregulated, while immune-related genes like CCL2 and HAVCR2 suggest ongoing immune modulation and changes in the tumour microenvironment. The transition from melanosis to late FDIM represents a profound biological shift, particularly in processes associated with tumorigenesis, cellular differentiation, and metabolic stress. Cancer pathway analysis showed that non-haematological solid tumour pathways and malignant neoplasm formation dominate late FDIM, with dysregulation of key transcription modulators, including members of the ZNF family, HMGA1 , and SOX genes. Immune evasion mechanisms were also evident, with alterations in genes like CCL2 and HAVCR2 , alongside oncogenes such as BIRC3 and BIRC5 , highlighting changes in apoptosis and survival pathways (Fig. 2 E). Stress response genes, including HSPA1L , reflect heightened cellular stress and metabolic demands. Consistent activation of cancer-related pathways, such as EIF2 signalling and translation elongation, further underscores the aggressive nature of late FDIM. Dysregulation of heat shock proteins and ribosomal proteins in late FDIM aligns with increased cellular stress and heightened metabolic activity. Finally, potential biomarkers of progression and therapeutic targets were identified, including MCAM , BIRC5 , and FOXC2 , which may offer new avenues for early detection and intervention in this aggressive disease. Materials and Methods Ethical approval was provided by the Veterinary Ethical Review Committee (VERC), The Royal (Dick) School of Veterinary Studies (67.21) and the Clinical Research Ethical Review Board (CRERB), The Royal Veterinary College (URN 2023 2236-2). Formalin-fixed, paraffin-embedded (FFPE) biosample collection Thirty-three archived formalin-fixed, paraffin-embedded (FFPE) feline eyes that were enucleated for reasons unrelated to the study between 1st January 2013 and 1st January 2023 were selected based on morphologic diagnosis of iris melanosis (dysplastic melanocytes confined in up to 3 layers lining the anterior iris stroma; n = 7), ‘early FDIM’ (neoplastic melanocytes confirmed to the iris stroma; n = 13), and ‘late FDIM’ (infiltration of neoplastic melanocytes through the iris stroma, ciliary body and sclera; n = 13). FFPE blocks were collected from Cytopath Veterinary Pathology, Dick White Referrals, The Royal Veterinary College and Royal (Dick) School of Veterinary Studies. Cats with a history of any prior treatment for any tumour type, or previous history of melanoma were excluded. Clinicopathological information Clinical and phenotypic data collected including patient age (years), sex, breed, age of the FFPE block (months), patient metastatic status at the time of presentation, patient survival after the initial diagnosis (months), development of metastasis and/or recurrence, and any adjuvant therapy provided. Laser capture microdissection (LCM) 2.5µm Haematoxylin and Eosin (H&E) stained sections were prepared from the FFPE blocks and digitally scanned. A diagnosis of melanosis, early FDIM or late FDIM was confirmed by an RCVS specialist in veterinary pathology (ES), and extent of the neoplastic population delineated. LCM was performed routinely using a ZEISS™ PALM MicroBeam Laser Microdissection unit (Carl Zeiss Microscopy GmbH, Carl-Zeiss-Promenade 10, 07745 Jena, Germany). The LCM tissue was stored on ice and RNA extraction performed using the Covaris E220 Evolution Focused Ultrasonicator and truXTRAC® FFPE RNA microTUBE Kit – Column (Covaris Ltd, Woddington, Brighton, UK) according to a previously published protocol [ 9 ]. Total RNA was characterised using RNA 6000 Pico kit on the Agilent 2100 Electrophoresis Bioanalyser (Agilent Technologies Inc., 5301 Stevens Creek Blvd, Santa Clara, CA, 95051, USA). Library Preparation First-strand cDNA was generated from 50ng of each total RNA sample using the SMARTer® Stranded Total RNA-Seq Kit v2 – Pico Input Mammalian kit (Clontech Laboratories Inc., Mountain View, CA, USA). Due to the high level of expected RNA degradation, no fragmentation was used. Illumina-compatible adapters and indexes were then added via 5 cycles of PCR. The SMARTer kit incorporates SMART® ( S witching M echanism A t 5’ end of R NA T emplate) cDNA synthesis technology and the directionality of the template-switching reaction preserves the strand orientation of the original RNA, making it possible to obtain strand-specific sequencing data from the synthesized cDNA. AMPure XP beads (Beckman Coulter, Brea, CA, USA) were then used to purify the cDNA library. Depletion of ribosomal cDNA (cDNA fragments originating from highly abundant rRNA molecules) was performed using ZapR v2 and R-probes v2 specific to mammalian ribosomal RNA and human mitochondrial rRNA. R-probes bind to library fragments originating from rRNA (18S and 28S) and mitochondrial rRNA (m12S and m16S) and ZapR cleaves these fragments. Uncleaved fragments were then enriched by 16 cycles of PCR for the LCM samples and negative control or 13 cycles for the core samples before a final library purification using AMPure XP beads. Library Quality Control Libraries were quantified with the Qubit 2.0 Fluorometer and the Qubit dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA, USA) and assessed for quality and size distribution of library fragments using the Agilent 2100 Electrophoresis Bioanalyser and the DNA High Sensitivity Kit. The negative control RNA generated a similar quantity of library as the experimental samples. One late FDIM sample was excluded due to poor library mapping rates. Sequencing Sequencing (2x100) was performed on the NextSeq 2000 platform (llumina Inc., San Diego, CA, USA) using NextSeq 2000 P3 Reagents (200 Cycles). Libraries were combined in an equimolar pool based on Qubit and Bioanalyser assay results and each pool was sequenced on a P3 flow cell. PhiX Control v3 (Illumina Inc.) was spiked in at a concentration of 1% to allow troubleshooting in the event of any issues with the run. Statistical analysis and graphical display of the results Alignment and Gene-Level Counts RNA-seq data were processed using the nf-core 'rnaseq' pipeline v3.8.1 [ 10 – 19 ]. In brief, samples were aligned via STAR v2.6.1d, and gene-based counts produced using Salmon v.1.5.2. Feline data were aligned to the Felis_catus_9.0 reference genome, annotated using the corresponding GTF file for build accession GCA_000181335.4. Unsupervised Clustering Unsupervised consensus clustering of expression data was performed using the R Bioconductor package, 'cola' [ 20 ]. Prior to clustering, gene level count data were subject to a variance stabilising transformation using DESeq2 [ 21 ], and the resulting matrix was used as an input. Five different clustering algorithms were tested, evaluating two to six clusters in each case. The cola algorithm resamples count data a fixed number of times, repeating the clustering process on each iteration. The optimum clustering strategy was selected on the stability of the resulting clusters; the method by which samples cluster most consistently. Differential Expression Analysis Differential expression analysis was performed using DESeq2. Models were fitted treating the clusters identified by cola, sex, and, where appropriate, batch as factors. Log fold change (logFC) estimates were produced using the apeglm shrinkage method [ 22 ], which is intended provide more robust estimates in the event of high within-group variability. Shrunken logFC estimates were accompanied by s-values [ 23 ] an aggregate false sign rate which are broadly analogous to q-values. P-values were also computed and adjusted using the independent hypothesis weighting (IHW) method [ 24 ]. Results were annotated using biomaRt [ 25 ], and volcano plots generated using MaGIC Volcano Plot Tool [ 26 ]. Statistical analysis and graphical display of the results Unsupervised hierarchical clustering was performed and differentially expressed genes identified between each of the three cohorts (defined as logFC > 1.5 and adjusted p-value < 0.05). Pathway overrepresentation analyses was based on Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology-Biological Process (GO:) databases. Pathway analysis and graphical display of the data was performed using QIAGEN Ingenuity Pathway Analysis (IPA; QIAGEN Inc., https://digitalinsights.qiagen.com/IPA ) [ 27 ]. A Mann-Whitney U test (Wicoxon rank sum test) and Fisher’s Exact test was performed to compare clinical data between each cohort using R (version 4.4.2) (R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. .). Discussion In this study, we sought to identify the gene expression changes that lead to FDIM initiation and then to evolution of an aggressive late-stage neoplastic phenotype. We found that transformation from iris melanosis to early FDIM was associated with upregulation of STOX1, PEG3, XIAP and VIM . Subsequent decreased expression of SOX11, FOXC1, FOXC2 and increased expression of BIRC2, BIRC5 and HAVCR2 leading to progression to late FDIM. During tumour initiation, the upregulation of XIAP (X-linked inhibitor of apoptosis protein) and VIM (vimentin) suggests increased epithelial-mesenchymal transition (EMT) and apoptosis resistance. EMT is a process in which tumours acquire mesenchymal traits, facilitating tumour cell invasion of the surrounding stroma [ 28 , 29 ]. This hallmark EMT event in early FDIM pathogenesis mirrors that of UM, where VIM upregulation correlates with increased tumour invasiveness [ 30 ]. XIAP plays a dual role in apoptosis resistance and inflammation modulation via the NF-κB signalling pathway while also enhancing melanoma cell migration [ 28 ]. PEG3 , traditionally considered a tumour suppressor when localised to the nucleus, may promote oncogenesis through cytosolic accumulation, inhibiting growth suppressors [ 31 ]. Its upregulation in early FDIM may therefore contribute to tumour initiation. The tumour microenvironment (TME) appears to play a critical role in FDIM progression, mirroring UM. Unlike other solid tumours, increased tumour-associated lymphocytes (TALs) and tumour-associated macrophages (TAMs) in UM contribute to an inflammatory phenotype linked to metastasis and poor survival [ 32 – 36 ]. Monosomy 3 and BAP1 loss drive M2 macrophage polarisation and proinflammatory cytokine release, particularly CCL2 , a key driver of monocyte chemotaxis and M2 macrophage differentiation [ 37 ]. This promotes an inflammatory TME that inhibits natural killer (NK) cell-mediated cytolysis and induces angiogenesis, facilitating tumorigenesis and metastasis [ 36 ]. HAVCR2 (encoding the immune checkpoint TIM-3 ) is associated with poor prognosis by suppressing immune responses from macrophages, dendritic cells, NK cells, and Tregs [ 38 , 39 ]. The GAL9/TIM-3 axis induces cytotoxic T-cell apoptosis and correlates with increased PD-L1 expression, reducing survival [ 40 ]. High GAL9 levels in aqueous humour further indicate poor prognosis [ 41 ]. Additionally, the inflammatory UM phenotype upregulates PD-1 expression, contributing to resistance to T-cell-mediated tumour destruction [ 42 ]. CCL28 and its receptor CCR10 enhance immune suppression by recruiting Tregs, cancer-associated fibroblasts, and myeloid-derived suppressor cells (MDSCs) [ 43 ]. We propose that FDIM initiation is associated with immune cell recruitment via XIAP, CCL28 , and VIM . Evolution to late FDIM is marked by upregulation of CCL2, BIRC5 , and HAVCR2 , leading to an inflammatory phenotype with inhibition of T-cell-mediated cytolysis, mirroring the malignant behaviour of UM. Recent advances in immune checkpoint inhibition (ICI), particularly targeting PD-1 and CTLA-4 , have significantly improved outcomes in cutaneous melanoma. However, UM exhibits poor responses to ICI monotherapy due to a low tumour mutational burden and an immune-suppressive TME, which limits T-cell activation [ 44 ]. The approval of tebentafusp (Kimmtrak), an immune-mobilising monoclonal T-cell receptor against cancer (ImmTAC), has shown promise in UM [ 45 – 47 ]. Tebentafusp functions as a bispecific T-cell engager, directing T cells to lyse tumour cells presenting the melanocyte-specific antigen gp100280-288 via HLA-A02:01. However, this therapy is limited to HLA-A02:01-positive patients [ 46 ]. Given the identified immune landscape modification of FDIM, alternative immune-based therapies should be explored. CCL2 is prognostic for hepatocellular carcinoma (HCC), where targeting TAMs via CCL2/CCR2 blockade effectively reduces tumour growth, reverses the immunosuppressive TME, and enhances cytotoxic T-cell responses [ 48 ]. Combining CCR2 antagonism with anti-PD-1 therapy has demonstrated improved tumour responses in solid tumours resistant to ICI monotherapy [ 49 ]. This suggests a promising therapeutic avenue for overcoming immune evasion in FDIM. TIM-3 has emerged as a novel ICI target, with its inhibition enhancing antigen-specific T-cell responses. TIM-3 blockade, both as monotherapy and in combination with PD-1/PD-L1 or CTLA-4 inhibitors, has shown promise in human solid tumours, including cutaneous melanoma [ 50 , 51 ]. Given HAVCR2 upregulation in FDIM, TIM-3 inhibition may improve immune-mediated tumour control in this disease. Furthermore, the inhibitor of apoptosis BIRC5 (survivin) is implicated in chemotherapy resistance in UM by reducing apoptosis rates. BIRC5 -targeted therapy has been proposed to sensitize tumours to treatment and inhibit growth [ 52 ]. Vaccination with BIRC5 -derived peptides has shown promise in metastatic cutaneous melanoma, with survivin-based immunotherapy well tolerated in humans and may have a synergistic effect when combined with existing ICIs [ 53 ]. A surprising finding in our study was the downregulation of FOXC1, FOXC2 and SOX11 in late FDIM. FOXC1 and FOXC2 are developmental transcription factors critical for embryogenesis and tissue differentiation, especially neural crest and uveal development [ 54 , 55 ]. Specifically, loss-of-function mutations to FOXC1 in humans are associated with Axenfeld-Rieger Syndrome- a condition characterised by anterior segment dysgenesis of the eye [ 56 ]. While FOXC1 and FOXC2 are often upregulated in multiple human cancers to drive EMT and metastasis, their downregulation in late FDIM suggests an alternative mechanism in tumorigenesis [ 54 , 55 , 57 – 59 ]. One possibility is that FOXC1/FOXC2 loss results in tumour dedifferentiation, producing a more aggressive and plastic tumour phenotype. A single report has shown that poor FOXC1 expression was associated with decreased survival in UM, suggesting a similar mechanism of action may exist in UM [ 60 ]. The transcription factor SOX11 , has a context-dependent role in cancer, and appears to be upregulated in late FDIM. In certain human cancers, SOX11 acts as a tumour suppressor, whereas in others, such as prostate cancer, it promotes proliferation, migration and invasion [ 61 , 62 ]. Research of the role of FOXC1, FOXC2 and SOX11 in the context of UM is limited, and further investigation is needed to elucidate the roles of these transcription factors in both FDIM and UM. In human UM, a PCR-based test (DecisionDx-UM) analyses the expression of 15 genes and accurately stratifies patients into low risk (Class I) and high-risk (Class II) groups, with minimal RNA obtained from fine-needle biopsy specimens [ 63 , 64 ]. Data provided by this study could be used to develop an equivalent assay for FDIM, which could significantly enhance the diagnostic yield of iris biopsy specimens, providing crucial prognostic information and guiding clinical decision-making. There were several limitations of the study. The study lacked a control group of normal feline iris melanocytes, for which to compare RNA data. No conclusions can therefore be made as to the gene expression changes from normal melanocytes to iris melanosis. The sample size was small, and future studies should seek to recruit increased numbers of patients. Finally, the retrospective nature of the study meant that long-term follow-up data was lacking for some cats included in the study. Conclusion In conclusion, our findings offer new insights into the molecular landscape of FDIM, implying that iris melanosis is a malignant precursor lesion with genetic overlap with early FDIM. Based on this, we propose renaming iris melanosis to ‘melanoma in situ.’ This study highlights the molecular complexity of FDIM, revealing mechanisms of tumour initiation via immune modulation and EMT, followed by transcriptional dysregulation in late-stage disease characterised by immune evasion and apoptosis resistance. The downregulation of FOXC1, FOXC2 , and SOX11 suggests these transcription factors may play a role in maintaining tumour differentiation and regulating metastatic behaviour, warranting further investigation. The role of CCL2, HAVCR2 , and BIRC5 in immune modulation and tumour progression present potential therapeutic targets. Further research is needed to validate the functional roles of these molecular pathways, particularly within the immune microenvironment. Given the similarities identified between FDIM and human uveal melanoma, these findings have broader implications for understanding ocular melanoma pathogenesis across species. Declarations Acknowledgements Our sincere gratitude goes to all the caregivers of cats that generously consented to contribute samples to this study. We would also like to thank all the referring veterinary surgeons who provided invaluable help obtaining follow-up information on cases. We would like to extend our thanks to Melanie McMillan, Hana Mlcochova Michael Millar and Scott Maxwell (The University of Edinburgh) for their invaluable expertise and help with the project. RM was funded by the Wellcome Trust [225442/Z/22/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission Author Contributions Conceptualisation: K.B.B., B.B. Funding acquisition: K.B.B., D.K. Sample acquisition: D.K., K.B.B., E.S., R.P., G.F., A.S-B., R.T., Data curation: K.B.B., D.K., E.S. Data analysis: D.K., K.D., K.B.B, R.M., R.C., A.M., L.M., A.F., H.B., Supervision: K.B.B., B.B. Writing- original draft: D.K., K.B.B., all authors reviewed the manuscript. Corresponding author Correspondence to David Kayes ( [email protected] ) and Kelly Bowlt Blacklock [email protected] Data Availability Statement The datasets generated and analysed during the current study are available in the Sequence Read Archive (SRA,) National Center for Biotechnology Information (NCBI), project reference: PRJNA1238379, available at: PRJNA1238379 - SRA - NCBI Competing Interests The authors declare no competing interests. Funding Funding for the project was provided by the European College of Veterinary Ophthalmologists (ECVO), British Association of Veterinary Ophthalmologists (BrAVO) and the Easter Bush Collaborative Seed Corn Grant. References Dubielzig RR. K, K., McLellan, G. J., & Albert, D. 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FOXC1, the new player in the cancer sandbox. Oncotarget 2018;9(8):8165-8178, doi:10.18632/oncotarget.22742 Gilding LN, Somervaille TCP. The Diverse Consequences of FOXC1 Deregulation in Cancer. Cancers 2019;11(2):184, doi:10.3390/cancers11020184 Tümer Z, Bach-Holm D. Axenfeld–Rieger syndrome and spectrum of PITX2 and FOXC1 mutations. European Journal of Human Genetics 2009;17(12):1527-1539, doi:10.1038/ejhg.2009.93 Han B, Bhowmick N, Qu Y, et al. FOXC1: an emerging marker and therapeutic target for cancer. Oncogene 2017;36(28):3957-3963, doi:10.1038/onc.2017.48 Yang Z, Jiang S, Cheng Y, et al. FOXC1 in cancer development and therapy: deciphering its emerging and divergent roles. Therapeutic Advances in Medical Oncology 2017;9(12):797-816, doi:10.1177/1758834017742576 Wang T, Zheng L, Wang Q, et al. Emerging roles and mechanisms of FOXC2 in cancer. Clin Chim Acta 2018;479(84-93, doi:10.1016/j.cca.2018.01.019 Bakalian S, Faingold D, Zoroquiain P, et al. The expression of FoxC1 in Uveal Melanoma. Investigative Ophthalmology & Visual Science 2014;55(13):5057-5057 Yang Z, Jiang S, Lu C, et al. SOX11: friend or foe in tumor prevention and carcinogenesis? Ther Adv Med Oncol 2019;11(1758835919853449, doi:10.1177/1758835919853449 Yao Z, Sun B, Hong Q, et al. The role of tumor suppressor gene SOX11 in prostate cancer. Tumour Biol 2015;36(8):6133-8, doi:10.1007/s13277-015-3296-3 Harbour JW, Chen R. The DecisionDx-UM Gene Expression Profile Test Provides Risk Stratification and Individualized Patient Care in Uveal Melanoma. PLoS Curr 2013;5(doi:10.1371/currents.eogt.af8ba80fc776c8f1ce8f5dc485d4a618 Plasseraud KM, Cook RW, Tsai T, et al. Clinical Performance and Management Outcomes with the DecisionDx-UM Gene Expression Profile Test in a Prospective Multicenter Study. Journal of Oncology 2016;2016(1-9, doi:10.1155/2016/5325762 Additional Declarations No competing interests reported. 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18:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6228571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6228571/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09632-5","type":"published","date":"2025-07-19T16:04:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79558209,"identity":"23bb5a0d-84fb-46fc-a93f-4db32f9a7488","added_by":"auto","created_at":"2025-03-31 08:01:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":335951,"visible":true,"origin":"","legend":"\u003cp\u003eLate feline diffuse iris melanoma (FDIM) is a more locally invasive disease associated with metastatic disease and reduced survival times. Haematoxylin and eosin staining showing the histological progression from iris melanosis (A), to early FDIM (B) and then late FDIM (C). Iris melanosis (A) is characterised by dysplastic melanocytes lining the anterior iris stroma in up to three layers (blue bracket). Progression to early FDIM (B) occurs with invasion of the underlying stroma (blue arrows). In late FDIM (C), neoplastic melanocytes infiltrate the iris, ciliary body and sclera. The blue arrows highlight invasion of the scleral venous plexus. The overview of the clinicopathologic data from the study (D) shows that cats with late FDIM were significantly (p\u0026lt;0.05) older, had higher mitotic activity, and were the only cats with metastatic disease. Statistically significant differences are indicated by * Kaplan-Meier survival analysis (E) revealed a significantly (p=0.04) reduced survival time for cats with late FDIM.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6228571/v1/c0e49fd507636e6abc912dd0.png"},{"id":79558211,"identity":"da967f7a-2db9-4535-86d6-2470b45d1401","added_by":"auto","created_at":"2025-03-31 08:01:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106774,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic analysis of feline diffuse iris melanoma (FDIM). The principal component analysis (PCA) plot grouping samples by the top 500 transcripts after variance-stabilising transformation of transcript counts. There is clear and separate clustering of the iris melanosis and late FDIM samples, with the early FDIM samples overlapping both groups, showing that early FDIM is an intermediate disease stage. Volcano plots highlight the number of significantly dysregulated transcripts between early FDIM and iris melanosis (Bi), late FDIM and iris melanosis (Ci) and between late and early FDIM (Di). A logfold-change threshold was set at -1.5 and 1.5 and an adjusted P-value threshold of 0.05 was used. Ingenuity pathway analysis identified cancer and organismal injury and abnormalities to be the top dysregulated disease pathways between early FDIM and iris melanosis (Bii), late and iris melanosis (Cii) and late and early FDIM (Dii). The disease progression of FDIM is associated with key molecular events (E). Tumour initiation is associated with upregulation of STOX1, PEG3, XIAP and VIM, increasing invasive tendencies and immune cell recruitment. Progression to late FDIM is characterised by downregulation of SOX11, FOXC1 and FOXC2, likely leading to a more dedifferentiated and plastic cellular phenotype. Significant upregulation of BIRC2 and BIRC5 leads to inhibition of apoptosis. Additionally, upregulation of BIRC5, CCL2 and HAVCR2 lead to immune-microenvironment remodelling, associated with the M2 (immunosuppressive) tumour associated macrophages as well as T-regulator (Treg) cells and inhibition of cytotoxic T-cells (CTC), aiding immune escape. Created in BioRender. Kayes, D. (2025) https://BioRender.com/m39r385.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6228571/v1/8023150c6837527570f254e7.png"},{"id":87467786,"identity":"e0ecfb61-7f7b-4276-a381-310886b52891","added_by":"auto","created_at":"2025-07-24 08:11:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1286607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6228571/v1/f77c11df-1d20-4a89-a1d7-31958c60c3f1.pdf"},{"id":79558673,"identity":"9a3d6c33-99da-46f8-863a-e90467ce2f44","added_by":"auto","created_at":"2025-03-31 08:09:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24579,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6228571/v1/4291921c9f5a928944cc0c86.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unravelling the Molecular Pathways of Feline Diffuse Iris Melanoma Progression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFeline diffuse iris melanoma (FDIM) is the most common primary ocular tumour in the cat, leading to significant infiltrative destruction of the globe, glaucoma, and death secondary to metastatic disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. FDIM originates from melanocytes lining the anterior surface of the iris [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIris melanosis, defined as dysplastic melanocytes lining the anterior iris in up to three layers, is considered a benign precursor lesion, with neoplastic transformation currently characterised by invasion of the underlying iris stroma [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Early FDIM, where there is no evident thickening of the iris, can be clinically indistinguishable from iris melanosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Iris biopsy has been described as a useful adjunctive diagnostic tool for differentiating between iris melanosis and early FDIM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe metastatic potential of FDIM is significant, with metastasis occurring in 19\u0026ndash;63% of patients with FDIM, with the rate of metastasis proportional to the severity of ocular invasion [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with early FDIM, where neoplastic melanocytes are confined to the iris and trabecular meshwork, have similar survival times as age-matched control cats [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, patients with late FDIM, where neoplastic melanocytes infiltrate into the iris, ciliary body and sclera, have decreased survival times and an increased metastatic rate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, there are no effective treatment options for metastatic FDIM, making early enucleation the preferred intervention to prevent metastasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genetic and transcriptomic landscape of FDIM remains largely unexplored. A pilot study using targeted quantitative real-time polymerase chain reaction (RT-qPCR) discovered dysregulation in key genes, including \u003cem\u003eKIT, LTA4, GNAQ, GNA11, BRAF\u003c/em\u003e and \u003cem\u003eRASF1\u003c/em\u003e in cats with FDIM [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These findings suggest that FDIM may share genetic mechanisms with human uveal melanoma (UM), highlighting the potential for cats to serve as a valuable model for studying human disease. Despite this promising insight, the transcriptomic landscape of FDIM has yet to be comprehensively investigated.\u003c/p\u003e \u003cp\u003eThe aim of this study was to define the transcriptomic landscape of FDIM to uncover the gene expression pathways that underpin disease progression. Using formalin-fixed, paraffin-embedded and laser microdissected tissue from patients with iris melanosis, early FDIM and late FDIM, we show for the first time that iris melanosis is a malignant precursor lesion, with genetic overlap with early FDIM. The transcriptomic changes that are associated with FDIM initiation and evolution to a malignant tumour phenotype are elucidated, identifying novel therapeutic and prognostic markers.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eLate feline diffuse iris melanoma is associated with increased metastasis and decreased survival.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe recruited seven cats with iris melanosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), 13 cats with early FDIM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and 13 cats with late FDIM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Clinicopathologic data are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and Supplementary Table\u0026nbsp;1. In our dataset, patients with late FDIM were significantly older individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had a higher incidence of secondary glaucoma (p\u0026thinsp;=\u0026thinsp;0.005) and tumour histopathology exhibited significantly higher numbers of mitotic figures per high power field (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAll patients with late FDIM showed marked local extension (Supplementary Table\u0026nbsp;1), including into the iridocorneal angle or trabecular meshwork (3/13 cats, 23%), the scleral venous plexus (9/13 cats, 69%), choroid (5/13 cats, 38%), or episcleral tissues (3/13 cats, 23%) with additional extension into the extraocular muscles in one cat (1/13, 8%).\u003c/p\u003e \u003cp\u003eMetastatic disease to the liver, spleen or lungs was confirmed or suspected in 4/13 (31%) patients with late FDIM and no patients with early FDIM or melanosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and Supplementary Table\u0026nbsp;1\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSurvival data was available for three cats with iris melanosis, six cats with early FDIM and seven cats with late FDIM. Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) showed a significantly worse survival for patients with late FDIM compared to those with early FDIM (p\u0026thinsp;=\u0026thinsp;0.044), with a median survival time of 9 and 27.5 months, for late and early FDIM, respectively.\u003c/p\u003e \u003cp\u003eOur findings underscore the aggressive nature of late FDIM, with higher metastatic potential, and significantly reduced survival compared to early FDIM.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMelanosis, early FDIM and late FDIM represents progressive disease\u003c/h2\u003e \u003cp\u003eIn this study, we sought to elucidate the molecular mechanisms underpinning the progression from melanosis to early and late FDIM in cats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Initially, we examined gene expression clustering, which revealed distinct and separate clustering of late FDIM and iris melanosis samples, reflecting significant differences in their gene expression profiles \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In contrast, early FDIM samples exhibited overlap with both groups, suggesting a transitional transcriptomic landscape during the progression from melanosis to late FDIM. The results suggest that iris melanosis is a malignant pre-cursor lesion, showing genetic overlap with early FDIM.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEarly FDIM Is Characterised by Upregulation of Cancer-Associated and Immune-modulatory Genes\u003c/h3\u003e\n\u003cp\u003eNext, we explored the transcriptomic landscape associated with the progression from melanosis to early FDIM. Comparative gene expression analysis revealed 91 upregulated and 7 downregulated genes in early FDIM compared to melanosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among the upregulated genes were those linked to cancer formation (e.g., \u003cem\u003eSTOX1, PEG3, XIAP\u003c/em\u003e) and immune cell recruitment (e.g., \u003cem\u003eCCL28, VIM\u003c/em\u003e), suggesting a shift towards increased cellular proliferation and remodelling of the immune microenvironment. Key biological themes identified by ingenuity pathway analysis (IPA) included cancer, organismal injury and abnormality, cell-cell signalling and interaction, cellular assembly and organisation, and hereditary disorder (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eBi). Notably, we identified significant upregulation of melanoma associated molecules \u003cem\u003eMCAM\u003c/em\u003e (melanoma cell adhesion molecule) and \u003cem\u003eLRCH1\u003c/em\u003e (leucine rich repeats and calponin homology domain containing 1) in early FDIM, highlighting their potential as early indicators of disease progression from melanosis to early FDIM.\u003c/p\u003e \u003cp\u003eThus, we demonstrate that the progression from iris melanosis to early FDIM involves dysregulation of multiple cancer-associated genes, alterations in cell replication pathways, and modulation of the immune microenvironment.\u003c/p\u003e\n\u003ch3\u003eLate FDIM shows further, extensive transcriptomic reprogramming and immune modulation\u003c/h3\u003e\n\u003cp\u003eNext, we explored the transcriptomic landscape of late FDIM in comparison to melanosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and early FDIM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Late FDIM showed a marked divergence in its molecular profile, with 101 upregulated and 146 downregulated genes compared to early FDIM, and 595 upregulated and 371 downregulated genes compared to melanosis. These findings demonstrate that the molecular profile of late FDIM diverges significantly from earlier disease stages.\u003c/p\u003e \u003cp\u003eCompared to early FDIM, late FDIM demonstrates progression reflects advanced disease stages which are characterised by transcriptional reprogramming and immune modulation.\u003c/p\u003e \u003cp\u003eDysregulation of developmental transcription factors, including \u003cem\u003eFOXC1, FOXC2\u003c/em\u003e, and \u003cem\u003eSOX11\u003c/em\u003e, suggests increased differentiation and proliferation activity. Olfactory receptor pathways and hedgehog signalling networks were notably dysregulated, indicating broader systemic impacts on cellular communication and development. Genes involved in embryonic and organ development, such as \u003cem\u003eSHH\u003c/em\u003e, \u003cem\u003eNEUROG3\u003c/em\u003e, and \u003cem\u003eMAFA\u003c/em\u003e, were significantly dysregulated, while immune-related genes like \u003cem\u003eCCL2\u003c/em\u003e and \u003cem\u003eHAVCR2\u003c/em\u003e suggest ongoing immune modulation and changes in the tumour microenvironment. The transition from melanosis to late FDIM represents a profound biological shift, particularly in processes associated with tumorigenesis, cellular differentiation, and metabolic stress. Cancer pathway analysis showed that non-haematological solid tumour pathways and malignant neoplasm formation dominate late FDIM, with dysregulation of key transcription modulators, including members of the \u003cem\u003eZNF\u003c/em\u003e family, \u003cem\u003eHMGA1\u003c/em\u003e, and \u003cem\u003eSOX\u003c/em\u003e genes. Immune evasion mechanisms were also evident, with alterations in genes like \u003cem\u003eCCL2\u003c/em\u003e and \u003cem\u003eHAVCR2\u003c/em\u003e, alongside oncogenes such as \u003cem\u003eBIRC3\u003c/em\u003e and \u003cem\u003eBIRC5\u003c/em\u003e, highlighting changes in apoptosis and survival pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Stress response genes, including \u003cem\u003eHSPA1L\u003c/em\u003e, reflect heightened cellular stress and metabolic demands.\u003c/p\u003e \u003cp\u003eConsistent activation of cancer-related pathways, such as \u003cem\u003eEIF2\u003c/em\u003e signalling and translation elongation, further underscores the aggressive nature of late FDIM. Dysregulation of heat shock proteins and ribosomal proteins in late FDIM aligns with increased cellular stress and heightened metabolic activity. Finally, potential biomarkers of progression and therapeutic targets were identified, including \u003cem\u003eMCAM\u003c/em\u003e, \u003cem\u003eBIRC5\u003c/em\u003e, and \u003cem\u003eFOXC2\u003c/em\u003e, which may offer new avenues for early detection and intervention in this aggressive disease.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eEthical approval was provided by the Veterinary Ethical Review Committee (VERC), The Royal (Dick) School of Veterinary Studies (67.21) and the Clinical Research Ethical Review Board (CRERB), The Royal Veterinary College (URN 2023 2236-2).\u003c/p\u003e\n\u003ch3\u003eFormalin-fixed, paraffin-embedded (FFPE) biosample collection\u003c/h3\u003e\n\u003cp\u003eThirty-three archived formalin-fixed, paraffin-embedded (FFPE) feline eyes that were enucleated for reasons unrelated to the study between 1st January 2013 and 1st January 2023 were selected based on morphologic diagnosis of iris melanosis (dysplastic melanocytes confined in up to 3 layers lining the anterior iris stroma; n\u0026thinsp;=\u0026thinsp;7), \u0026lsquo;early FDIM\u0026rsquo; (neoplastic melanocytes confirmed to the iris stroma; n\u0026thinsp;=\u0026thinsp;13), and \u0026lsquo;late FDIM\u0026rsquo; (infiltration of neoplastic melanocytes through the iris stroma, ciliary body and sclera; n\u0026thinsp;=\u0026thinsp;13). FFPE blocks were collected from Cytopath Veterinary Pathology, Dick White Referrals, The Royal Veterinary College and Royal (Dick) School of Veterinary Studies. Cats with a history of any prior treatment for any tumour type, or previous history of melanoma were excluded.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological information\u003c/h2\u003e \u003cp\u003eClinical and phenotypic data collected including patient age (years), sex, breed, age of the FFPE block (months), patient metastatic status at the time of presentation, patient survival after the initial diagnosis (months), development of metastasis and/or recurrence, and any adjuvant therapy provided.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaser capture microdissection (LCM)\u003c/h3\u003e\n\u003cp\u003e2.5\u0026micro;m Haematoxylin and Eosin (H\u0026amp;E) stained sections were prepared from the FFPE blocks and digitally scanned. A diagnosis of melanosis, early FDIM or late FDIM was confirmed by an RCVS specialist in veterinary pathology (ES), and extent of the neoplastic population delineated. LCM was performed routinely using a ZEISS\u0026trade; PALM MicroBeam Laser Microdissection unit (Carl Zeiss Microscopy GmbH, Carl-Zeiss-Promenade 10, 07745 Jena, Germany). The LCM tissue was stored on ice and RNA extraction performed using the Covaris E220 Evolution Focused Ultrasonicator and truXTRAC\u0026reg; FFPE RNA microTUBE Kit \u0026ndash; Column (Covaris Ltd, Woddington, Brighton, UK) according to a previously published protocol [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTotal RNA was characterised using RNA 6000 Pico kit on the Agilent 2100 Electrophoresis Bioanalyser (Agilent Technologies Inc., 5301 Stevens Creek Blvd, Santa Clara, CA, 95051, USA).\u003c/p\u003e\n\u003ch3\u003eLibrary Preparation\u003c/h3\u003e\n\u003cp\u003eFirst-strand cDNA was generated from 50ng of each total RNA sample using the SMARTer\u0026reg; Stranded Total RNA-Seq Kit v2 \u0026ndash; Pico Input Mammalian kit (Clontech Laboratories Inc., Mountain View, CA, USA). Due to the high level of expected RNA degradation, no fragmentation was used. Illumina-compatible adapters and indexes were then added via 5 cycles of PCR. The SMARTer kit incorporates SMART\u0026reg; (\u003cb\u003eS\u003c/b\u003ewitching \u003cb\u003eM\u003c/b\u003eechanism \u003cb\u003eA\u003c/b\u003et 5\u0026rsquo; end of \u003cb\u003eR\u003c/b\u003eNA \u003cb\u003eT\u003c/b\u003eemplate) cDNA synthesis technology and the directionality of the template-switching reaction preserves the strand orientation of the original RNA, making it possible to obtain strand-specific sequencing data from the synthesized cDNA. AMPure XP beads (Beckman Coulter, Brea, CA, USA) were then used to purify the cDNA library. Depletion of ribosomal cDNA (cDNA fragments originating from highly abundant rRNA molecules) was performed using ZapR v2 and R-probes v2 specific to mammalian ribosomal RNA and human mitochondrial rRNA. R-probes bind to library fragments originating from rRNA (18S and 28S) and mitochondrial rRNA (m12S and m16S) and ZapR cleaves these fragments. Uncleaved fragments were then enriched by 16 cycles of PCR for the LCM samples and negative control or 13 cycles for the core samples before a final library purification using AMPure XP beads.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLibrary Quality Control\u003c/h2\u003e \u003cp\u003eLibraries were quantified with the Qubit 2.0 Fluorometer and the Qubit dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA, USA) and assessed for quality and size distribution of library fragments using the Agilent 2100 Electrophoresis Bioanalyser and the DNA High Sensitivity Kit. The negative control RNA generated a similar quantity of library as the experimental samples. One late FDIM sample was excluded due to poor library mapping rates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSequencing\u003c/h2\u003e \u003cp\u003eSequencing (2x100) was performed on the NextSeq 2000 platform (llumina Inc., San Diego, CA, USA) using NextSeq 2000 P3 Reagents (200 Cycles). Libraries were combined in an equimolar pool based on Qubit and Bioanalyser assay results and each pool was sequenced on a P3 flow cell. PhiX Control v3 (Illumina Inc.) was spiked in at a concentration of 1% to allow troubleshooting in the event of any issues with the run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and graphical display of the results\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAlignment and Gene-Level Counts\u003c/strong\u003e \u003cp\u003eRNA-seq data were processed using the nf-core 'rnaseq' pipeline v3.8.1 [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In brief, samples were aligned via STAR v2.6.1d, and gene-based counts produced using Salmon v.1.5.2. Feline data were aligned to the Felis_catus_9.0 reference genome, annotated using the corresponding GTF file for build accession GCA_000181335.4.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eUnsupervised Clustering\u003c/strong\u003e \u003cp\u003eUnsupervised consensus clustering of expression data was performed using the R Bioconductor package, 'cola' [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Prior to clustering, gene level count data were subject to a variance stabilising transformation using DESeq2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and the resulting matrix was used as an input. Five different clustering algorithms were tested, evaluating two to six clusters in each case. The cola algorithm resamples count data a fixed number of times, repeating the clustering process on each iteration. The optimum clustering strategy was selected on the stability of the resulting clusters; the method by which samples cluster most consistently.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDifferential Expression Analysis\u003c/strong\u003e \u003cp\u003eDifferential expression analysis was performed using DESeq2. Models were fitted treating the clusters identified by cola, sex, and, where appropriate, batch as factors. Log fold change (logFC) estimates were produced using the apeglm shrinkage method [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which is intended provide more robust estimates in the event of high within-group variability. Shrunken logFC estimates were accompanied by s-values [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] an aggregate false sign rate which are broadly analogous to q-values. P-values were also computed and adjusted using the independent hypothesis weighting (IHW) method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Results were annotated using biomaRt [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and volcano plots generated using MaGIC Volcano Plot Tool [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and graphical display of the results\u003c/h2\u003e \u003cp\u003eUnsupervised hierarchical clustering was performed and differentially expressed genes identified between each of the three cohorts (defined as logFC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pathway overrepresentation analyses was based on Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology-Biological Process (GO:) databases. Pathway analysis and graphical display of the data was performed using QIAGEN Ingenuity Pathway Analysis (IPA; QIAGEN Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digitalinsights.qiagen.com/IPA\u003c/span\u003e\u003cspan address=\"https://digitalinsights.qiagen.com/IPA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA Mann-Whitney U test (Wicoxon rank sum test) and Fisher\u0026rsquo;s Exact test was performed to compare clinical data between each cohort using R (version 4.4.2) (R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we sought to identify the gene expression changes that lead to FDIM initiation and then to evolution of an aggressive late-stage neoplastic phenotype. We found that transformation from iris melanosis to early FDIM was associated with upregulation of \u003cem\u003eSTOX1, PEG3, XIAP\u003c/em\u003e and \u003cem\u003eVIM\u003c/em\u003e. Subsequent decreased expression of \u003cem\u003eSOX11, FOXC1, FOXC2\u003c/em\u003e and increased expression of \u003cem\u003eBIRC2, BIRC5\u003c/em\u003e and \u003cem\u003eHAVCR2\u003c/em\u003e leading to progression to late FDIM.\u003c/p\u003e \u003cp\u003eDuring tumour initiation, the upregulation of \u003cem\u003eXIAP\u003c/em\u003e (X-linked inhibitor of apoptosis protein) and \u003cem\u003eVIM\u003c/em\u003e (vimentin) suggests increased epithelial-mesenchymal transition (EMT) and apoptosis resistance. EMT is a process in which tumours acquire mesenchymal traits, facilitating tumour cell invasion of the surrounding stroma [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This hallmark EMT event in early FDIM pathogenesis mirrors that of UM, where \u003cem\u003eVIM\u003c/em\u003e upregulation correlates with increased tumour invasiveness [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eXIAP\u003c/em\u003e plays a dual role in apoptosis resistance and inflammation modulation via the NF-κB signalling pathway while also enhancing melanoma cell migration [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003ePEG3\u003c/em\u003e, traditionally considered a tumour suppressor when localised to the nucleus, may promote oncogenesis through cytosolic accumulation, inhibiting growth suppressors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Its upregulation in early FDIM may therefore contribute to tumour initiation.\u003c/p\u003e \u003cp\u003eThe tumour microenvironment (TME) appears to play a critical role in FDIM progression, mirroring UM. Unlike other solid tumours, increased tumour-associated lymphocytes (TALs) and tumour-associated macrophages (TAMs) in UM contribute to an inflammatory phenotype linked to metastasis and poor survival [\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Monosomy 3 and \u003cem\u003eBAP1\u003c/em\u003e loss drive M2 macrophage polarisation and proinflammatory cytokine release, particularly \u003cem\u003eCCL2\u003c/em\u003e, a key driver of monocyte chemotaxis and M2 macrophage differentiation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This promotes an inflammatory TME that inhibits natural killer (NK) cell-mediated cytolysis and induces angiogenesis, facilitating tumorigenesis and metastasis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eHAVCR2\u003c/em\u003e (encoding the immune checkpoint \u003cem\u003eTIM-3\u003c/em\u003e) is associated with poor prognosis by suppressing immune responses from macrophages, dendritic cells, NK cells, and Tregs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The \u003cem\u003eGAL9/TIM-3\u003c/em\u003e axis induces cytotoxic T-cell apoptosis and correlates with increased \u003cem\u003ePD-L1\u003c/em\u003e expression, reducing survival [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. High \u003cem\u003eGAL9\u003c/em\u003e levels in aqueous humour further indicate poor prognosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, the inflammatory UM phenotype upregulates \u003cem\u003ePD-1\u003c/em\u003e expression, contributing to resistance to T-cell-mediated tumour destruction [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eCCL28\u003c/em\u003e and its receptor CCR10 enhance immune suppression by recruiting Tregs, cancer-associated fibroblasts, and myeloid-derived suppressor cells (MDSCs) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe propose that FDIM initiation is associated with immune cell recruitment via \u003cem\u003eXIAP, CCL28\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e. Evolution to late FDIM is marked by upregulation of \u003cem\u003eCCL2, BIRC5\u003c/em\u003e, and \u003cem\u003eHAVCR2\u003c/em\u003e, leading to an inflammatory phenotype with inhibition of T-cell-mediated cytolysis, mirroring the malignant behaviour of UM.\u003c/p\u003e \u003cp\u003eRecent advances in immune checkpoint inhibition (ICI), particularly targeting \u003cem\u003ePD-1\u003c/em\u003e and \u003cem\u003eCTLA-4\u003c/em\u003e, have significantly improved outcomes in cutaneous melanoma. However, UM exhibits poor responses to ICI monotherapy due to a low tumour mutational burden and an immune-suppressive TME, which limits T-cell activation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The approval of tebentafusp (Kimmtrak), an immune-mobilising monoclonal T-cell receptor against cancer (ImmTAC), has shown promise in UM [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Tebentafusp functions as a bispecific T-cell engager, directing T cells to lyse tumour cells presenting the melanocyte-specific antigen gp100280-288 via HLA-A02:01. However, this therapy is limited to HLA-A02:01-positive patients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the identified immune landscape modification of FDIM, alternative immune-based therapies should be explored. \u003cem\u003eCCL2\u003c/em\u003e is prognostic for hepatocellular carcinoma (HCC), where targeting TAMs via \u003cem\u003eCCL2/CCR2\u003c/em\u003e blockade effectively reduces tumour growth, reverses the immunosuppressive TME, and enhances cytotoxic T-cell responses [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Combining \u003cem\u003eCCR2\u003c/em\u003e antagonism with anti-PD-1 therapy has demonstrated improved tumour responses in solid tumours resistant to ICI monotherapy [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This suggests a promising therapeutic avenue for overcoming immune evasion in FDIM.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTIM-3\u003c/em\u003e has emerged as a novel ICI target, with its inhibition enhancing antigen-specific T-cell responses. \u003cem\u003eTIM-3\u003c/em\u003e blockade, both as monotherapy and in combination with \u003cem\u003ePD-1/PD-L1\u003c/em\u003e or \u003cem\u003eCTLA-4\u003c/em\u003e inhibitors, has shown promise in human solid tumours, including cutaneous melanoma [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Given \u003cem\u003eHAVCR2\u003c/em\u003e upregulation in FDIM, \u003cem\u003eTIM-3\u003c/em\u003e inhibition may improve immune-mediated tumour control in this disease.\u003c/p\u003e \u003cp\u003eFurthermore, the inhibitor of apoptosis \u003cem\u003eBIRC5\u003c/em\u003e (survivin) is implicated in chemotherapy resistance in UM by reducing apoptosis rates. \u003cem\u003eBIRC5\u003c/em\u003e-targeted therapy has been proposed to sensitize tumours to treatment and inhibit growth [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Vaccination with \u003cem\u003eBIRC5\u003c/em\u003e-derived peptides has shown promise in metastatic cutaneous melanoma, with survivin-based immunotherapy well tolerated in humans and may have a synergistic effect when combined with existing ICIs [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA surprising finding in our study was the downregulation of \u003cem\u003eFOXC1, FOXC2\u003c/em\u003e and \u003cem\u003eSOX11\u003c/em\u003e in late FDIM. \u003cem\u003eFOXC1\u003c/em\u003e and \u003cem\u003eFOXC2\u003c/em\u003e are developmental transcription factors critical for embryogenesis and tissue differentiation, especially neural crest and uveal development [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Specifically, loss-of-function mutations to \u003cem\u003eFOXC1\u003c/em\u003e in humans are associated with Axenfeld-Rieger Syndrome- a condition characterised by anterior segment dysgenesis of the eye [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. While \u003cem\u003eFOXC1\u003c/em\u003e and \u003cem\u003eFOXC2\u003c/em\u003e are often upregulated in multiple human cancers to drive EMT and metastasis, their downregulation in late FDIM suggests an alternative mechanism in tumorigenesis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. One possibility is that \u003cem\u003eFOXC1/FOXC2\u003c/em\u003e loss results in tumour dedifferentiation, producing a more aggressive and plastic tumour phenotype. A single report has shown that poor \u003cem\u003eFOXC1\u003c/em\u003e expression was associated with decreased survival in UM, suggesting a similar mechanism of action may exist in UM [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe transcription factor \u003cem\u003eSOX11\u003c/em\u003e, has a context-dependent role in cancer, and appears to be upregulated in late FDIM. In certain human cancers, \u003cem\u003eSOX11\u003c/em\u003e acts as a tumour suppressor, whereas in others, such as prostate cancer, it promotes proliferation, migration and invasion [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch of the role of \u003cem\u003eFOXC1, FOXC2\u003c/em\u003e and \u003cem\u003eSOX11\u003c/em\u003e in the context of UM is limited, and further investigation is needed to elucidate the roles of these transcription factors in both FDIM and UM.\u003c/p\u003e \u003cp\u003eIn human UM, a PCR-based test (DecisionDx-UM) analyses the expression of 15 genes and accurately stratifies patients into low risk (Class I) and high-risk (Class II) groups, with minimal RNA obtained from fine-needle biopsy specimens [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Data provided by this study could be used to develop an equivalent assay for FDIM, which could significantly enhance the diagnostic yield of iris biopsy specimens, providing crucial prognostic information and guiding clinical decision-making.\u003c/p\u003e \u003cp\u003eThere were several limitations of the study. The study lacked a control group of normal feline iris melanocytes, for which to compare RNA data. No conclusions can therefore be made as to the gene expression changes from normal melanocytes to iris melanosis.\u003c/p\u003e \u003cp\u003eThe sample size was small, and future studies should seek to recruit increased numbers of patients. Finally, the retrospective nature of the study meant that long-term follow-up data was lacking for some cats included in the study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings offer new insights into the molecular landscape of FDIM, implying that iris melanosis is a malignant precursor lesion with genetic overlap with early FDIM. Based on this, we propose renaming iris melanosis to \u0026lsquo;melanoma in situ.\u0026rsquo;\u003c/p\u003e \u003cp\u003eThis study highlights the molecular complexity of FDIM, revealing mechanisms of tumour initiation via immune modulation and EMT, followed by transcriptional dysregulation in late-stage disease characterised by immune evasion and apoptosis resistance. The downregulation of \u003cem\u003eFOXC1, FOXC2\u003c/em\u003e, and \u003cem\u003eSOX11\u003c/em\u003e suggests these transcription factors may play a role in maintaining tumour differentiation and regulating metastatic behaviour, warranting further investigation. The role of \u003cem\u003eCCL2, HAVCR2\u003c/em\u003e, and \u003cem\u003eBIRC5\u003c/em\u003e in immune modulation and tumour progression present potential therapeutic targets.\u003c/p\u003e \u003cp\u003eFurther research is needed to validate the functional roles of these molecular pathways, particularly within the immune microenvironment. Given the similarities identified between FDIM and human uveal melanoma, these findings have broader implications for understanding ocular melanoma pathogenesis across species.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere gratitude goes to all the caregivers of cats that generously consented to contribute samples to this study. We would also like to thank all the referring veterinary surgeons who provided invaluable help obtaining follow-up information on cases. We would like to extend our thanks to Melanie McMillan, Hana Mlcochova Michael Millar and Scott Maxwell (The University of Edinburgh) for their invaluable expertise and help with the project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRM was funded by the Wellcome Trust [225442/Z/22/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation: K.B.B., B.B. Funding acquisition: K.B.B., D.K. Sample acquisition: D.K., K.B.B., E.S., R.P., G.F., A.S-B., R.T., Data curation: K.B.B., D.K., E.S. Data analysis: D.K., K.D., K.B.B, R.M., R.C., A.M., L.M., A.F., H.B., Supervision: K.B.B., B.B. Writing- original draft: D.K., K.B.B., all authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to David Kayes ([email protected]) and Kelly Bowlt Blacklock [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the Sequence Read Archive (SRA,) National Center for Biotechnology Information (NCBI), project reference: PRJNA1238379, available at:\u0026nbsp;PRJNA1238379 - SRA - NCBI\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for the project was provided by the European College of Veterinary Ophthalmologists (ECVO), British Association of Veterinary Ophthalmologists (BrAVO) and the Easter Bush Collaborative Seed Corn Grant. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDubielzig RR. 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Oncogene 2017;36(28):3957-3963, doi:10.1038/onc.2017.48\u003c/li\u003e\n\u003cli\u003eYang Z, Jiang S, Cheng Y, et al. FOXC1 in cancer development and therapy: deciphering its emerging and divergent roles. Therapeutic Advances in Medical Oncology 2017;9(12):797-816, doi:10.1177/1758834017742576\u003c/li\u003e\n\u003cli\u003eWang T, Zheng L, Wang Q, et al. Emerging roles and mechanisms of FOXC2 in cancer. Clin Chim Acta 2018;479(84-93, doi:10.1016/j.cca.2018.01.019\u003c/li\u003e\n\u003cli\u003eBakalian S, Faingold D, Zoroquiain P, et al. The expression of FoxC1 in Uveal Melanoma. Investigative Ophthalmology \u0026amp; Visual Science 2014;55(13):5057-5057\u003c/li\u003e\n\u003cli\u003eYang Z, Jiang S, Lu C, et al. SOX11: friend or foe in tumor prevention and carcinogenesis? Ther Adv Med Oncol 2019;11(1758835919853449, doi:10.1177/1758835919853449\u003c/li\u003e\n\u003cli\u003eYao Z, Sun B, Hong Q, et al. The role of tumor suppressor gene SOX11 in prostate cancer. 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Journal of Oncology 2016;2016(1-9, doi:10.1155/2016/5325762\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Feline diffuse iris melanoma, melanoma metastasis, transcriptome, gene expression, uveal melanoma","lastPublishedDoi":"10.21203/rs.3.rs-6228571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6228571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFeline diffuse iris melanoma (FDIM) is the most common primary ocular tumour in cats, with high metastatic potential. Greater intraocular invasion correlates with increased mortality. No effective therapeutics exist for metastatic disease, partly due to a lack of known molecular targets associated with aggressive tumour behaviour.\u003c/p\u003e \u003cp\u003eHere, we define the transcriptomic landscape of FDIM in treatment-na\u0026iuml;ve cats using bulk RNA sequencing on laser capture microdissection and core biopsy specimens from formalin-fixed paraffin-embedded tissue. Samples included \u0026lsquo;iris melanosis\u0026rsquo; (dysplastic melanocytes confined to the anterior iris; n\u0026thinsp;=\u0026thinsp;7), \u0026lsquo;early FDIM\u0026rsquo; (neoplastic melanocytes confined to the iris stroma; n\u0026thinsp;=\u0026thinsp;13), and \u0026lsquo;late FDIM\u0026rsquo; (neoplastic infiltration into the ciliary body and sclera; n\u0026thinsp;=\u0026thinsp;13). Iris melanosis exhibited genetic overlap with early FDIM, supporting its reclassification as \u0026lsquo;melanoma in situ.\u0026rsquo;\u003c/p\u003e \u003cp\u003eEarly FDIM showed upregulation of genes linked to tumour initiation, immune recruitment, and motility (e.g., \u003cem\u003eSTOX1, PEG3, XIAP, MCAM, VIM\u003c/em\u003e). Late FDIM exhibited immune microenvironment remodelling, immune evasion, and apoptosis inhibition (e.g., \u003cem\u003eBIRC2, BIRC5, CCL2, HAVCR2\u003c/em\u003e), with downregulation of \u003cem\u003eFOX1, FOXC2\u003c/em\u003e, and \u003cem\u003eSOX11\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThese results provide critical biomarkers of disease progression, which may aid in the development of more accurate prognostic tests and more effective targeted therapies for FDIM.\u003c/p\u003e","manuscriptTitle":"Unravelling the Molecular Pathways of Feline Diffuse Iris Melanoma Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 08:01:32","doi":"10.21203/rs.3.rs-6228571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-05T04:31:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T08:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107047940272351110128752645117021415070","date":"2025-05-23T10:52:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T14:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295971276983428361927058378443853788337","date":"2025-04-14T02:33:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192397965208980104308919115006417747907","date":"2025-04-03T06:34:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T03:04:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T02:47:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-31T17:10:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T08:56:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-14T18:16:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1abac707-a867-475a-b1d4-1278b86ed15d","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46351671,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":46351672,"name":"Biological sciences/Cancer/Eye cancer"},{"id":46351673,"name":"Biological sciences/Cancer/Metastases"},{"id":46351674,"name":"Biological sciences/Cancer/Oncogenes"},{"id":46351675,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":46351676,"name":"Biological sciences/Cancer/Tumour immunology"},{"id":46351677,"name":"Health sciences/Oncology"},{"id":46351678,"name":"Health sciences/Diseases"},{"id":46351679,"name":"Health sciences/Diseases/Eye diseases"},{"id":46351680,"name":"Biological sciences/Genetics/Cancer genetics"},{"id":46351681,"name":"Biological sciences/Genetics/Gene expression"},{"id":46351682,"name":"Biological sciences/Genetics/Genetic markers"}],"tags":[],"updatedAt":"2025-07-24T07:39:06+00:00","versionOfRecord":{"articleIdentity":"rs-6228571","link":"https://doi.org/10.1038/s41598-025-09632-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-19 16:04:53","publishedOnDateReadable":"July 19th, 2025"},"versionCreatedAt":"2025-03-31 08:01:32","video":"","vorDoi":"10.1038/s41598-025-09632-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09632-5","workflowStages":[]},"version":"v1","identity":"rs-6228571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6228571","identity":"rs-6228571","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}