Metastatic melanoma presents clinical challenges due to tumor heterogeneity and treatment resistance. Here, we report an integrative workflow combining AI-based digital pathology with spatial proteomics to support personalized treatment strategies in a case of a young patient with recurrent melanoma and multiple metastases. Our AI model trained on H&E images identified two spatially separated cell subpopulations (PT1 and PT2) within the primary lesion, along with metastatic areas and stromal components. MS-based proteomics was used to map the spatial proteome across the clinically relevant regions. Our findings indicate inter-tumor heterogeneity and increased kinases associated with target-drug resistance. Convergent morphological and proteomic signatures identified PT1 as an aggressive melanoma subtype and the likely metastatic driver. Augmented glycolytic signaling and mitochondrial metabolism were identified as drivers of melanoma progression in this patient. Our findings suggest that targeted therapies may provide limited benefit, while the combination with metabolic inhibitors could represent a more effective treatment option for the patient.

ABSTRACT Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent® 780 liquid handling workstation (Tecan ©) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I–II. The automated workflow achieved superior protein identification (6,146 ± 860 vs. 4,941 ± 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure

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.

Women are born with thousands of ovarian follicles, yet less than 1% will ovulate a mature oocyte. What distinguishes the 99% of follicles destined for demise from the rare 1% that succeed? Here, we leveraged a unique clinical opportunity, paired follicular fluid samples from small antral follicles containing either a viable or an atretic oocyte within the same patients, to uncover the proteomic determinants governing oocyte fate. Our analysis identified nearly 1,500 proteins. Follicular fluid from healthy follicles was enriched with proteins crucial for energy metabolism, antioxidative defense, structural integrity, and robust intercellular signaling networks, supporting oocyte survival and maturation. In contrast, follicular fluid from atretic oocytes exhibited a distinct inflammatory signature characterized by acute-phase and immune activation proteins, indicative of active degeneration. These novel proteomic insights deepen our understanding of human follicle biology and open new avenues for biomarker development, optimization of assisted reproductive technologies, and targeted therapeutic strategies to preserve and enhance female fertility.

Abstract Mucinous colorectal carcinoma (CRC) is a distinct histomorphological subtype characterized by abundant extracellular mucin that may promote immune evasion and chemoresistance. We describe a metastatic mucinous CRC case integrating digital pathology and proteomics to investigate disease progression and therapy resistance. Formalin-fixed paraffin-embedded samples from the primary tumor, peritoneal metastasis, and hepatoduodenal ligament metastasis of a 56-year-old patient were analyzed. Whole-slide imaging with QuPath-based AI enabled detailed histological annotation, while data-independent acquisition mass spectrometry identified over 6,000 proteins. Digital pathology revealed extensive mucin pools, architectural evolution from heterogeneous glandular patterns in the primary tumor to cribriform morphology in advanced metastases, and immune cell exclusion from mucin-rich regions. Proteomics revealed metabolic reprogramming, suppressed antigen presentation, and stage-specific activation of inflammatory, angiogenic, EMT, and PI3K/AKT/mTOR–MYC signaling pathways, consistent with proliferative and therapy-resistant phenotypes.Integration of AI-assisted histopathology with spatial proteomics highlighted the mucin barrier as a key mediator of immune evasion and chemoresistance. These findings support a personalized therapeutic framework targeting mucin-associated mechanisms alongside pathway-directed inhibitors, suggesting that spatial multi-omics may guide precision management strategies for aggressive mucinous colorectal cancer.

Formalin-fixed, paraffin-embedded (FFPE) archives underpin dermatopathology and translational oncology, enabling clinically annotated melanoma cohorts, but cohort-scale proteomics remains limited by labor and variability in upstream processing. We established a plate-scale, acoustic FFPE proteomics workflow and integrated it with AI-assisted digital pathology to support composition-aware molecular profiling from routine sections. The optimized pipeline reduces handling steps, is designed to improve reproducibility, and supports rapid parallel processing in a 96-well format. Deep data-independent acquisition MS of 40 primary melanomas spanning acral lentiginous, lentigo maligna, superficial spreading, and nodular subtypes quantified more than 8200 protein groups and a mean of 5200 proteins per tumor. Proteome profiles resolved melanoma subtypes and defined an acral lentiginous melanoma program enriched for translation/biogenesis and extracellular matrix/adhesion processes with relative depletion of lipid and fatty-acid metabolism and peroxisomal pathways. Supervised feature selection highlighted tenascin, periostin, eukaryotic initiation factor 4A-I, and ADP-ribosylation factor 4 and uncovered selective depletion of ATPase inhibitor, mitochondrial (ATP5IF1), a regulator of mitochondrial ATP synthase, in acral lentiginous melanoma. ATP5IF1 depletion persisted after adjustment for mitochondrial proxies and QuPath-derived tumor content, and coincided with higher glycolysis relative to Complex V. This pathology-integrated, high-throughput FFPE proteomics framework enables scalable retrospective discovery and suggests subtype-specific metabolic alterations consistent with mitochondrial remodeling in an underrepresented melanoma subtype.

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC, and AKT1, across five distinct MM subtypes. These proteins are potential drug targets applicable to one or multiple MM subtypes. Additionally, by integrating proteogenomic profiles obtained from MM subtypes with MM cell line dependency and drug sensitivity data, we identified a total of 162 potentially targetable genes. Lastly, we identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Employing these unbiased approaches, we have uncovered compounds targeting ferroptosis demonstrating a striking 30× fold difference in sensitivity among different subtypes. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy.

Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative designed to address these clinical challenges by integrating advanced proteogenomic profiling, clinical metadata, with AI-driven digital pathology and machine learning analytics, aiming to enhance personalized treatment strategies and improve patient outcomes. Between 2013 and 2022, a cohort of 1653 melanoma patients each contributed a primary tumor sample, with 361 providing 819 metastatic tumor samples. Clinical data collection for this cohort continued until May 2023. Comprehensive analyses using high-resolution mass spectrometry, optimized workflows for formalin-fixed paraffin-embedded tissues, and advanced digital pathology platforms enabled precise mapping of the tumor microenvironment, identification of metabolic reprogramming, and characterization of immune evasion signatures. The European Cancer Moonshot Lund Center's MEGA-Study, under the academic umbrella of Lund and Szeged universities, marks a significant advancement in its collaborative efforts with the National Institutes of Health (NIH) under the Cancer Moonshot partnership. This initiative exemplifies the center's dedication to pioneering cancer research and underscores the strength of its international collaborations. SIGNIFICANCE: The significance of this study lies in its pioneering integration of high-resolution proteomics, AI-driven digital pathology, and comprehensive clinical annotation to unravel the complex molecular landscape of melanoma. By leveraging a robust, population-based cohort of 1653 patients, including extensive analyses of both primary and metastatic tumor specimens, our approach provides unprecedented insights into the proteogenomic alterations that underpin tumor progression, immune evasion, and therapeutic resistance. The preliminary application of advanced mass spectrometry techniques to formalin-fixed paraffin-embedded tissues, combined with state-of-the-art digital pathology and machine learning, has enabled the identification of novel protein biomarkers and metabolic signatures that hold promise for refining patient stratification and informing personalized treatment strategies. This integrative framework not only deepens our understanding of melanoma biology but also establishes a scalable model for precision oncology that can be extended to other complex malignancies. Ultimately, our findings have the potential to transform clinical practice by facilitating earlier risk stratification, improving prognostication, and guiding the development of targeted therapeutic interventions for this highly aggressive cancer.

BackgroundProstate cancer therapy with surgical or chemical castration with gonadotropin-releasing hormone (GnRH) agonists has been linked to elevated follicle-stimulating hormone (FSH) levels, which may contribute to secondary health disorders, including atherosclerosis and diabetes. Although recent findings suggest a role for FSH beyond the reproductive system, its metabolic impact remains unclear and difficult to disentangle from that of androgens. In this study, we examined the metabolic changes induced by FSH and distinguished them from those caused by testosterone.MethodsPlasma samples from temporarily medically castrated young men (n = 33) treated with FSH and/or testosterone were characterized by proteomics and metabolomics approaches. All subjects received GnRH antagonists. Sixteen men were randomized to recombinant FSH (300 IU 3 times/week) for 5 weeks, while seventeen men served as controls. After 3 weeks, all men received 1000 mg intramuscular testosterone undecanoate. Blood samples were collected at the start, after 3 weeks and after 5 weeks. The proteome and metabolome signatures were characterized in all samples.ResultsFSH significantly upregulates key proteins involved in the modulation of inflammatory response and innate immune system (P ≤ 0.03) and dysregulates lipid metabolism, evidenced by downregulation of multiple apolipoproteins (P ≤ 0.04) and increased levels of cholesterol and glycerophospholipids (P ≤ 0.03). In addition, low FSH levels were correlated with a reduction in the active form of vitamin D (P < 0.02). These results highlight the short-term metabolic impacts of FSH in males.Conclusions and Clinical ImplicationsOur findings underlined the FSH effect on extragonadal systems and its connection to metabolic disorders often seen as secondary effects of prostate cancer treatment.

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.

Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable responses to immunotherapy, and the high cost of treatment (illustrated by immune checkpoint inhibitors like pembrolizumab and nivolumab, totaling roughly $191,000 per year) remains a formidable global challenge. The convergence of digital pathology, high-throughput molecular profiling, and advanced computational strategies has the potential to transform cancer research. By integrating high-resolution morphological data with proteomic, transcriptomic, and spatial molecular insights, we can elucidate the complex interplay between tumor cells and their microenvironment. In this perspective, we review how emerging techniques, from AI-driven image analysis to deep visual proteomics, can accelerate biomarker discovery, refine patient stratification, and ultimately improve clinical outcomes. We illustrate these principles with a case study in melanoma, where the integration of digital pathology and deep proteomic profiling uncovered a molecular signature predictive of recurrence in early-stage disease. As these technologies evolve, we foresee a future of precision oncology characterized by the seamless integration of morphological, clinical, and molecular data enabled by AI-driven analytics. SIGNIFICANCE: This perspective represents a pivotal step toward transforming cancer research by bridging the gap between traditional histopathological evaluation and modern molecular analytics. By integrating digital pathology with spatial proteomics and advanced AI-driven analytics, our approach provides a multidimensional view of tumor biology that captures both morphological nuances and molecular heterogeneity. This comprehensive framework not only enhances our understanding of the tumor microenvironment but also facilitates the discovery of robust biomarkers for disease recurrence and therapeutic response. Ultimately, our findings underscore the potential of precision oncology to tailor treatment strategies to individual patient profiles, thereby improving clinical outcomes and guiding the next generation of personalized cancer care.

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent to which different organs and cells impact the plasma proteome in healthy and diseased conditions, we developed a mass-spectrometry-based proteomics strategy to infer the tissue origin of proteins detected in human plasma. We first constructed an extensive human proteome atlas from 18 vascularized organs and the 8 most abundant cell types in blood. The atlas was interfaced with previous RNA and protein atlases to objectively define proteome-wide protein-organ associations to infer the origin and enable the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in six separate patient cohorts, including sepsis, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

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.

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent different organs and cells contribute to the plasma proteome composition, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in human plasma in health and disease. First, we constructed an extensive human proteome atlas from 18 vascularized organs and the most abundant cell types in blood. Second, the atlas was interfaced with previous RNA/protein atlases to objectively define proteome wide protein-organ associations to enable both the inference of origin and the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease specific quantitative changes of organ-enriched protein panels in three separate patient cohorts with infection, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

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.

BackgroundThe utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem.ObjectiveHere, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance.MethodsLeveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC, and AKT1, across five distinct MM subtypes. These proteins are potential drug targets applicable to one or multiple MM subtypes. Additionally, by integrating proteogenomic profiles obtained from MM subtypes with MM cell line dependency and drug sensitivity data, we identified a total of 162 potentially targetable genes. Lastly, we identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype.ResultsEmploying these unbiased approaches, we have uncovered compounds targeting ferroptosis demonstrating a striking 30x fold difference in sensitivity among different subtypes.ConclusionsOur results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy.