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bioRxiv · 10.64898/2026.08.12.744404

From Routine Pathology to Precision Oncology: Automated FFPE Tissue Processing for Large-Scale Molecular Studies

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(R) 780 liquid handling workstation (Tecan (C)) 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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a3ea5dorg.highwire.dtl.DTLVardef@6fb8edorg.highwire.dtl.DTLVardef@d60a4dorg.highwire.dtl.DTLVardef@3e47b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

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BibTeXRIS

Guedes, J., Sliwa-Gonzalez, A., Szadai, L., Geiger, P., Woldmar, N., Reyes, M. A., Bastida, R. A., Coto, D. L. F., Oskolas, H., Marko-Varga, M., Schultz, L., Appelqvist, R., Wieslander, E., Malm, J., Marko-Varga, G., Gil, J.. 2026-08-17. From Routine Pathology to Precision Oncology: Automated FFPE Tissue Processing for Large-Scale Molecular Studies. https://doi.org/10.64898/2026.08.12.744404

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