A critical gap in current efficiency in melanoma patient treatment is the lack of a fully integrated, functional understanding of tumor evolution over time. Recent advances have fundamentally reshaped our understanding of melanoma biology, while increasing clinical complexity has highlighted the need for more comprehensive and biologically informed clinical decision-support frameworks. We propose the implementation of a multimodal disease profiling framework as a core clinical decision-support asset, enhancing treatment optimization across the full disease course in melanoma patients. By integrating proteogenomics, AI-driven digital image analysis, and structured longitudinal clinical metadata, multimodal disease profiling could provide a comprehensive and dynamically evolving view of each patient's disease. Proteogenomics reveals tumor signaling activity, protein complex dynamics, and emerging therapeutic vulnerabilities that may drive progression and resistance. In parallel, AI-enabled digital pathology analysis characterizes tumor morphology, clonal heterogeneity, and immune context, capturing spatial and functional changes associated with metastatic transition. When combined with longitudinal clinical data, these layers enable patient-specific models tracking tumor evolution, metastasis, and treatment exposure. Leveraging one of the largest melanoma biobank and database resources at the European Cancer Moonshot Center in Lund, our strategy directly addresses the recurrent transition from primary tumors to metastatic disease. This strategy positions multimodal disease profiling as a critical enabler of precision melanoma care by providing biologically grounded, evidence-based decision support, facilitating rapid and structured case assessment through multimodal insights, enabling prediction of treatment response, resistance, and disease trajectory, and supporting adaptive, evidence-informed therapeutic decision-making.

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we identified 274 proteins showing significant differences among the sample types. Further analysis of these 274 proteins in lymph node metastatic samples from a third cohort revealed that 45 proteins exhibited a significant effect on patient survival. The three most significant proteins were HP (HR = 4.67, p = 2.8e-06), LGALS7 (HR = 3.83, p = 2.9e-05), and UBQLN1 (HR = 3.2, p = 4.8e-05). The user-friendly interactive web platform, accessible at https://www.tnmplot.com/melanoma, provides an interactive interface for the analysis of proteomic and clinical data. The MEL-PLOT platform, through its interactive capabilities, streamlines the creation of a comprehensive knowledge base, empowering hypothesis formulation and diligent monitoring of the most recent advancements in the domains of biomedical research and drug development.

BACKGROUND:Melanoma, the deadliest form of skin cancer, exhibits resistance to conventional therapies, particularly in advanced and metastatic stages. Mitochondrial pathways, including oxidative phosphorylation and mitochondrial translation, have emerged as critical drivers of melanoma progression and therapy resistance. This study investigates the mitochondrial proteome in melanoma to uncover novel therapeutic vulnerabilities. METHODS:Quantitative proteomics was performed on 151 melanoma-related samples from a prospective cohort and postmortem tissues. Differential expression analysis identified mitochondrial proteins linked to disease aggression and treatment resistance. Functional enrichment analyses and in vitro validation using mitochondrial inhibitors were conducted to evaluate therapeutic potential. RESULTS:Mitochondrial translation and oxidative phosphorylation (OXPHOS) were significantly upregulated in aggressive melanomas, particularly in BRAF-mutant and metastatic tumors. Inhibition of mitochondrial pathways using antibiotics (doxycycline, tigecycline, and azithromycin) and OXPHOS inhibitors (VLX600, IACS-010759, and BAY 87-2243) demonstrated dose-dependent antiproliferative effects in melanoma cell lines, sparing noncancerous melanocytes. These treatments disrupted mitochondrial function, suppressed key metabolic pathways, and induced apoptosis, highlighting the clinical relevance of targeting these pathways. CONCLUSIONS:This study reveals mitochondrial pathways as critical drivers of melanoma progression and resistance, providing a rationale for targeting mitochondrial translation and OXPHOS in advanced melanoma. Combining mitochondrial inhibitors with existing therapies could overcome treatment resistance and improve patient outcomes.

BACKGROUND:Metastatic melanoma is a highly aggressive disease with poor survival rates despite recent therapeutic advancements with immunotherapy. The proteomic landscape of advanced melanoma remains poorly understood, especially regarding proteomic heterogeneity across metastases within patients. METHODS:We collected 83 melanoma metastases from 19 different metastatic sites in 24 patients with advanced metastatic melanoma almost exclusively from the pre-immunotherapy era, using semi-rapid autopsies. The metastases were subjected to histopathological evaluation, RNA-sequencing and mass spectrometry-based proteomics for protein quantitation and non-reference peptide (NRP) sequence detection using a proteogenomic data integration approach. RESULTS:NRPs associated with mutations frequently occurred in proteins related to focal adhesion, vesicle-mediated transport, MAPK signalling and immune response pathways across the cohort. Intrapatient heterogeneity was negligible when considering morphology and driver gene mutation status but was substantial at the proteogenomic level. This heterogeneity was not driven by metastasis location, albeit liver metastases exhibited distinct proteogenomic patterns, including upregulation of metabolic pathways. Cluster analysis outlined four proteomic clusters (C1-4) of the metastases, characterised by the upregulation of cell cycle and RNA-splicing (C1), mitochondrial processes (C3), extracellular matrix (ECM) and immune pathways (C2) and ECM and vesicle-mediated transport pathways (C4). Around two-thirds of patients had metastases that had strongly distinct phenotypes. Patients in our cohort whose metastases were primarily assigned to clusters C1 and C3 exhibited shorter overall survival than patients whose metastases were categorised mainly into the C2 and C4 clusters. CONCLUSION:Our unique multi-metastasis cohort captured the proteogenomic heterogeneity of immunotherapy-naïve melanoma distant metastases, establishing a foundation for future studies aimed at identifying novel therapeutic targets to complement current immunotherapies. KEY POINTS:Comprehensive proteogenomic profiling of post-mortem melanoma metastases, collected primarily before the immunotherapy era. Description of 1177 protein sequence variants predicted by RNA-Seq and validated via mass spectrometry-based proteomics. Empirical evidence of prominent intrapatient heterogeneity, driven by heterogeneous protein expression related to cell cycle- and mitochondrial processes, immune system and extracellular matrix organization.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.

Background: Melanoma is a highly heterogeneous disease, and a deeper molecular classification is essential for improving patient stratification and treatment approaches. Here, we describe the histopathology-driven proteogenomic landscape of 142 treatment-naïve metastatic melanoma samples to uncover molecular subtypes and clinically relevant biomarkers. Methods: We performed an integrative proteogenomic analysis to identify proteomic subtypes, assess the impact of BRAF V600 mutations, and study the molecular profiles and cellular composition of the tumor microenvironment. Clinical and histopathological data were used to support findings related to tissue morphology, disease progression, and patient outcomes. Results: Our analysis revealed five distinct proteomic subtypes that integrate immune and stromal microenvironment components and correlate with clinical and histopathological parameters. We demonstrated that BRAF V600-mutated melanomas exhibit biological heterogeneity, where an oncogene-induced senescence-like phenotype is associated with improved survival. This led to a proposed mortality risk-based stratification that may contribute to more personalized treatment strategies. Furthermore, tumor microenvironment composition strongly correlated with disease progression and patient outcomes, highlighting a histopathological connective tissue-to-tumor ratio assessment as a potential decision-making tool. We identified a melanoma-associated SAAV signature linked to extracellular matrix remodeling and SAAV-derived neoantigens as potential targets for anti-tumor immune responses. Conclusions: This study provides a comprehensive stratification of metastatic melanoma, integrating proteogenomic insights with histopathological features. The findings may aid in the development of tailored diagnostic and therapeutic strategies, improving patient management and outcomes.

IntroductionWhile Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.MethodsEvaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.ResultsSix proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.DiscussionOur study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Overview of the study workflow (S1); Hematoxylin & eosin stainings of samples in the study (S2); FACS ploidy histogram of Mm1702 (S3); Mutation-plots demonstrating the subclonality analysis in different regions of tumor Mm1641 (S4); Mutation-plots demonstrating the subclonality analysis in different regions of tumors Mm1641, Mm1702 and Mm1749 (S5); Mutation-plots demonstrating the subclonality analysis in different regions of tumor Mm1749 (S6); Mutation-plots demonstrating the subclonality analysis in different regions of tumors Mm1749, Mm1765 and Mm1767 (S7); Mutation-plots demonstrating the subclonality analysis in different regions of tumors Mm1767, Mm1772 and Mm1837 (S8); Correlations of trunk and non-trunk mutations to signatures from Alexandrov et al Nature 2013 (S9); Difference in expression levels between tumor regions harboring non-trunk mutations and those missing the corresponding mutations (S10); Correlation plots between DNA and RNA VAF in individual regions in tumors Mm1641 and Mm1702 (S11); Correlation plots between DNA and RNA VAF in individual regions in tumors Mm1702, Mm1742 and Mm1749 (S12); Correlation plots between DNA and RNA VAF in individual regions in tumors Mm1749 and Mm1765 (S13); Correlation plots between DNA and RNA VAF in individual regions in tumors Mm1767 and Mm1772 (S14); Correlation plots between DNA and RNA VAF in individual regions in tumor Mm1837 (S15).

ABSTRACT Here we describe the histopathology-driven proteogenomic landscape of 142 treatment-naïve metastatic melanoma samples. We identified five proteomic subtypes that integrate the immune and stroma microenvironment components, and associate with clinical and histopathological parameters, providing foundations for an in-depth molecular classification of melanoma. Our study shows that BRAF V600 mutated melanomas display heterogeneous biology, where the presence of an oncogene-induced senescence-like phenotype improves patient survival. Therefore, we propose a mortality-risk-based stratification, which may contribute to a more personalized approach to patient treatment. We also found a strong association between tumor microenvironment composition, disease progression, and patient outcome supported by single-cell omic signatures that point to straightforward histopathological connective tissue-to-tumor ratio assessment for better informed medical decisions. A melanoma-associated signature of single amino acid variants (SAAV) responsible for remodeling the extracellular matrix was uncovered together with SAAV-derived neoantigen candidates as targets of anti-tumor immune responses. Overall, this study offers comprehensive stratifications of melanoma metastases that may help develop tailored strategies for diagnosing and treating the disease.

159930_1_supp_0_d72rhn.doc from Multiregion Whole-Exome Sequencing Uncovers the Genetic Evolution and Mutational Heterogeneity of Early-Stage Metastatic Melanoma

Analysis + 1 Figure and 3 Tables

159930_1_supp_0_t72r9t.jpg from Multiregion Whole-Exome Sequencing Uncovers the Genetic Evolution and Mutational Heterogeneity of Early-Stage Metastatic Melanoma

Description of additional methods and procedures used in the study. Also includes Supplementary References.

Summary The dynamics of more than 1900 mitochondrial proteins was explored through quantitative proteomics in 151 melanoma-related tissue samples of both surgical and autopsy origin. Dysregulation of mitochondrial pathways in primary tumors, metastases, and peritumoral tissues was correlated with age and survival of patients, as well as with tumor cell proliferation and the BRAF mutation status of the tumors. The outlined proteomic landscape confirmed the central role of a pathologically upregulated mitochondrial translation machinery and oxidative phosphorylation (OXPHOS) in the development, proliferation, and progression of melanomas. Our results from different melanoma cell lines confirmed our findings and we could document that treatments with selected OXPHOS inhibitors and antibiotics successfully impaired tumor cell proliferation. In addition, we provided proteomic evidence on the mechanism-of-action of the different treatments. These observations could contribute to the development of therapeutic approaches targeting the mitochondrial pathology in melanoma. TOC figure Highlights Mitochondrial proteome landscape outlined in 151 melanoma-related samples Mitochondrial Translation and OXPHOS impact disease severity and survival BRAF V600E mutation correlates with upregulation of mitochondrial energy production Targeting the mitochondrial OXPHOS and ribosomes impairs tumor cell proliferation Therapeutic opportunities complementary to the standard of care are proposed In brief Mitochondrial proteome profiling of melanomas reveals dysregulation in major metabolic pathways, suggesting a central role of the mitochondria within the development and progression of melanoma. Targeting mitochondrial pathways has the potential to impact the course of the disease, which provides opportunities for complementary drug interventions.

The discovery of novel protein biomarkers in melanoma is crucial. Our introduction of formalin-fixed paraffin-embedded (FFPE) tumor protocol provides new opportunities to understand the progression of melanoma and open the possibility to screen thousands of FFPE samples deposited in tumor biobanks and available at hospital pathology departments. In our retrospective biobank pilot study, 90 FFPE samples from 77 patients were processed. Protein quantitation was performed by high-resolution mass spectrometry and validated by histopathologic analysis. The global protein expression formed six sample clusters. Proteins such as TRAF6 and ARMC10 were upregulated in clusters with enrichment for shorter survival, and proteins such as AIFI1 were upregulated in clusters with enrichment for longer survival. The cohort's heterogeneity was addressed by comparing primary and metastasis samples, as well comparing clinical stages. Within immunotherapy and targeted therapy subgroups, the upregulation of the VEGFA-VEGFR2 pathway, RNA splicing, increased activity of immune cells, extracellular matrix, and metabolic pathways were positively associated with patient outcome. To summarize, we were able to (i) link global protein expression profiles to survival, and they proved to be an independent prognostic indicator, as well as (ii) identify proteins that are potential predictors of a patient's response to immunotherapy and targeted therapy, suggesting new opportunities for precision medicine developments.

Abstract The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in‐depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients.

AbstractThe MM500 meta‐study aims to establish a knowledge basis of the tumor proteome to serve as a complement to genome and transcriptome studies. Somatic mutations and their effect on the transcriptome have been extensively characterized in melanoma. However, the effects of these genetic changes on the proteomic landscape and the impact on cellular processes in melanoma remain poorly understood. In this study, the quantitative mass‐spectrometry‐based proteomic analysis is interfaced with pathological tumor characterization, and associated with clinical data. The melanoma proteome landscape, obtained by the analysis of 505 well‐annotated melanoma tumor samples, is defined based on almost 16 000 proteins, including mutated proteoforms of driver genes. More than 50 million MS/MS spectra were analyzed, resulting in approximately 13,6 million peptide spectrum matches (PSMs). Altogether 13 176 protein‐coding genes, represented by 366 172 peptides, in addition to 52 000 phosphorylation sites, and 4 400 acetylation sites were successfully annotated. This data covers 65% and 74% of the predicted and identified human proteome, respectively. A high degree of correlation (Pearson, up to 0.54) with the melanoma transcriptome of the TCGA repository, with an overlap of 12 751 gene products, was found.Mapping of the expressed proteins with quantitation, spatiotemporal localization, mutations, splice isoforms, and PTM variants was proven not to be predicted by genome sequencing alone. The melanoma tumor molecular map was complemented by analysis of blood protein expression, including data on proteins regulated after immunotherapy. By adding these key proteomic pillars, the MM500 study expands the knowledge on melanoma disease.

SummaryMalignant melanoma (MM) develops from the melanocytes and in its advanced stage is the most aggressive type of skin cancer. Here we report a comprehensive analysis on a prospective cohort study, including non-tumor, primary and metastasis tissues (n=77) with the corresponding plasma samples (n=56) from patients with malignant melanoma. The tumors and surrounding tissues were characterized with a combination of high-throughput analyses including quantitative proteomics, phosphoproteomics, acetylomics, and whole exome sequencing (WES) combined with in-depth histopathology analysis. Melanoma cell proliferation highly correlates with dysregulation at the proteome, at the posttranslational- and at the transcriptome level. Some of the changes were also verified in the plasma proteome. The metabolic reprogramming in melanoma includes upregulation of the glycolysis and the oxidative phosphorylation, and an increase in glutamine consumption, while downregulated proteins involved in the degradation of amino acids, fatty acids, and the extracellular matrix (ECM) receptor interaction. The pathways most dysregulated in MM including the MAP kinases-, the PI3K-AKT signaling, and the calcium homeostasis, are among the most affected by mutations, thus, dysregulation in these pathways can be manifested as drivers in melanoma development and progression.The phosphoproteome analysis combined with target-based prediction mapped 75% of the human kinome. Melanoma cell proliferation was driven by two key factors: i) metabolic reprogramming leading to upregulation of the glycolysis and oxidative phosphorylation, supported by HIF-1 signaling pathway and mitochondrial translation; and ii) a dysregulation of the immune system response, which was mirrored by immune system processes in the plasma proteome. Regulation of the melanoma acetylome and expression of deacetylase enzymes discriminated between groups based on tissue origin and proliferation, indicating a way to guide the successful use of HDAC inhibitors in melanoma. The disease progression toward metastasis is driven by the downregulation of the immune system response, including MHC class I and II, which allows tumors to evade immune surveillance. Altogether, new evidence is provided at different molecular levels to allow improved understanding of the melanoma progression, ultimately contributing to better treatment strategies.TOC figure

Malignant melanoma is among the most aggressive skin cancers and it has among the highest metastatic potentials. Although surgery to remove the primary tumor is the gold standard treatment, once melanoma progresses and metastasizes to the lymph nodes and distal organs, i.e., metastatic melanoma (MM), the usual outcome is decreased survival. To improve survival rates and life span, advanced treatments have focused on the success of targeted therapies in the MAPK pathway that are based on BRAF (BRAF V600E) and MEK. The majority of patients with tumors that have higher expression of BRAF V600E show poorer prognosis than patients with a lower level of the mutated protein. Based on the molecular basis of melanoma, these findings are supported by distinct tumor phenotypes determined from differences in tumor heterogeneity and protein expression profiles. With these aspects in mind, continued challenges are to: (1) deconvolute the complexity and heterogeneity of MM; (2) identify the signaling pathways involved; and (3) determine protein expression to develop targeted therapies. Here, we provide an overview of the results from protein expression in MM and the link to disease presentation in a variety of tumor phenotypes and how these will overcome the challenges of clinical problems and suggest new promising approaches in metastatic melanoma and cancer therapy.