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Individualized Cancer Mapping: How can AI-based digital pathology and spatial proteomics guide precision oncology?

Abstract #327 Posted on28 March 20259 May 2025 9 May 2025 14h13

Author(s)

Jéssica Guedes (Presenting Author) | Clinical Protein Science and Imaging | Lund University - European Cancer Moonshot Lund Center, 221 84, Lund, Sweden
Nicole Woldmar | Clinical Protein Science and Imaging | Lund University - European Cancer Moonshot Lund Center, 221 84, Lund, Sweden
András Kriston | Peter Horvath Laboratory | Biological Research Centre, 6726, Szeged, Hungary
Ede Migh | Peter Horvath Laboratory | Biological Research Centre, 6726, Szeged, Hungary
Ferenc Kovacsv | Peter Horvath Laboratory | Biological Research Centre, 6726, Szeged, Hungary
Henriett Oskolás | Clinical Protein Science and Imaging | Lund University - European Cancer Moonshot Lund Center, 221 84, Lund, Sweden
István Németh | Marys Cancer Laboratory | Department of Dermatology and Allergology, 6720, Szeged, Hungary
Peter Horvath | Peter Horvath Laboratory | Biological Research Centre, 6726, Szeged, Hungary
Gyulai Rolland | Department of Dermatology and Allergology | Department of Dermatology and Allergology, 6720, Szeged, Hungary
Esters Baltas | Department of Dermatology and Allergology | Department of Dermatology and Allergology, 6720, Szeged, Hungary
Jeovanis Gil | Clinical Protein Science and Imaging | Lund University - European Cancer Moonshot Lund Center, 221 84, Lund, Sweden
György Marko-Varga | Clinical Protein Science and Imaging | Lund University - European Cancer Moonshot Lund Center, 221 84, Lund, Sweden

Abstract

Despite advances in cancer therapy, treating late-stage cases remains challenging, mainly due to resistance and non-responsiveness treatment. Comprehensive profiling of individual tumors can identify actionable targets, enabling the selection of the most effective therapy for each patient. In this study, we developed an integrative workflow to support treatment decision-making for late-stage cancer patients by combining AI-driven morphological analysis, laser microdissection of tumor and stromal regions, including specific subclones, and in-depth molecular profiling through gene mutation panels and spatial proteomics. Applying this workflow to multiple metastatic patients uncovered novel therapeutic opportunities. The AI-based analyses identified distinct cell subpopulations within primary and metastatic lesions, while spatial proteomics revealed significant inter-tumor heterogeneity, highlighting markers of treatment resistance and upregulation of cancer-associated pathways, including PI3K/AKT/MTOR, glycolytic signaling, and mitochondrial metabolism, which were identified as drivers of cancer progression in the analyzed patients. Our findings support alternative treatments based on individual molecular profiles, showing that combining molecular and morphological characterization enables personalized care. This approach sets the ground for omics analysis inside clinical surroundings, enhances treatment response prediction and has the potential to extend patient survival.

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