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<p>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&amp;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.</p>

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AI-based digital pathology and spatial proteomics enable precision oncology: A case report of recurrent melanoma in a young patient

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Jéssica Guedes,
  • Nicole Woldmar,
  • Eszter Baltas,
  • András Kriston,
  • Ede Migh,
  • Ferenc Kovács,
  • Henriett Oskolás,
  • Matilda Marko-Varga,
  • Roger Appelqvist,
  • Elisabet Wieslander,
  • A. Marcell Szasz,
  • Zoltán Veréb,
  • Rolland Gyulai,
  • István Balázs Németh,
  • Johan Malm,
  • Peter Horvath,
  • Jeovanis Gil,
  • György Marko-Varga

摘要

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.