Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification

Abstract

Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy that lacks reliable biomarkers to guide treatment decisions. Effective prognostic tools are needed to improve its clinical management. We conducted a comprehensive proteomic analysis on 115 PDA patient samples with matched adjacent normal tissue. A 20-protein diagnostic panel was identified (LGALS1, ANXA2, LGALS3BP, CTSD, S100P, COL12A1, SFN, THBS2, CTHRC1, THBS1, SERPINB5, LAMC2, POSTN, CEACAM6, CTSE, PLEC, PKM, S100A11, TAGLN2, ALDOA). Consensus clustering analysis identified four prognostic proteomic subtypes. Subtypes with poorer prognoses exhibited upregulation of neutrophil degranulation, extracellular matrix remodeling, focal adhesion, Mesenchymal Epithelial Transition, collagen formation, and PI3K-Akt-mTOR-related pathways, indicating a predominance of basal-like and activated stromal features. In tumors with homologous recombination deficiency or Catalogue of Somatic Mutations in Cancer Signature-3, several immune-related proteins were enriched. An 18-protein (PURB, SDCBP2, CD2BP2, GALM, SERPINA3, OAS3, FAN1, ZPR1, KRT2, NUDT2, SMNDC1, SERPINA4, CUTA, WDR36, POSTN, CLEC11A, PEX14, and PI4KA) risk score was developed and validated using multicox regression analyses with LASSO regularization. The risk score demonstrated independent prognostic significance for overall survival and recurrence, and was validated in an independent proteomic dataset generated using a different proteomic technology. This study thus introduces four novel prognostic PDA subtypes, and an 18-protein risk score validated in an independent dataset, which shows promise for improving survival prediction and could serve as a valuable tool for personalized treatment guidance.

Significance: The findings from this study have significant implications for the future of pancreatic cancer management. By identifying a 20-protein panel with diagnostic and screening potential, this research provides a foundation for developing early detection tools for PDA, an aggressive cancer with limited treatment options. The classification of PDA into four proteomic subtypes with distinct prognostic outcomes paves the way for subtype-specific therapeutic approaches, allowing clinicians to better stratify patients based on their risk profiles. Additionally, the validated 18-protein risk score, which enhances survival prediction and operates independently of existing clinical variables, represents a promising tool for personalized prognostic assessments. Incorporating these proteomic-based biomarkers into clinical practice could improve diagnostic accuracy, guide individualized treatment decisions, and ultimately enhance patient outcomes in PDA. This study underscores the potential of proteomic profiling to improve cancer treatment by providing targeted, actionable insights into tumor biology.

Figure 1.

A schematic representing (A) data collection from 30-μm sections of 115 PDA and 61 adjacent normal fresh-frozen tissues that were prepared (19) for (B) DIA-MS. C, Protein quantification from DIA-MS data files using DIA-NN and MaxLFQ software for data normalization, QC, and peptide-to-protein inference and (D) the downstream analyses of the protein data. FC, fold change.

Figure 2.

DAPs and dysregulated pathways in PDA tumors compared with adjacent normal tissue. A, DAPs (n = 395; P < 0.05) between PDA and adjacent normal tissue. B, Top enriched pathway proteins upregulated in PDA tissue. KEGG, Kyoto Encyclopedia of Genes and Genomes. C, Gene linkage network of enriched pathways from Gene Ontology biological processes database for upregulated proteins in PDA tumors. FC, fold change.

Figure 3.

Proteomic subtypes of PDA. A, A clustered heatmap displaying the highly variable protein intensities across the four clusters detected within this cohort. B, Kaplan–Meier plots of the survival rates of the four clusters. C, Kaplan–Meier plot of the survival rates of the clusters combined according to prognosis (cluster 1 with 3 and cluster 2 with 4).

Figure 4.

Proteomic risk score for mortality. A, Forest plot detailing the multivariable HRs for the overall survival of each of the proteins used to build the proteomic risk score. B, Kaplan–Meier curve displaying the overall survival probability of the low- and high-risk groups within our cohort. C, Kaplan–Meier curve displaying the survival probability of the low- and high-risk groups within the CPTAC validation dataset.