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Updated: Oct 21, 2025
A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
Published on: April 18, 2025
Comprehensive molecular profiling to predict clinical outcomes in pancreatic cancer
Jung Yong Hong1, Hee Jin Cho2, Seung Tae Kim1
1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Background:
Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among common cancers. The genomic landscape of PDAC is defined by four mutational pathways: kirsten rat sarcoma virus (KRAS), cellular tumor antigen p53 (TP53), cyclin dependent kinase inhibitor 2A (CDKN2A), and SMAD family member 4 (SMAD4). However, there is a paucity of data on the molecular features associated with clinical outcomes after surgery or chemotherapy.
Methods:
We performed comprehensive molecular characterization of tumor specimens from 83 patients with PDAC who received surgery, using whole-exome sequencing and ribonucleic acid sequencing on tumor and matched normal tissues derived from patients. We also systematically performed integrative analysis, combining genomic, transcriptomic, and clinical features to identify biomarkers and possible therapeutic targets.
Results:
KRAS (75%), TP53 (67%), CDKN2A (12%), SMAD4 (20%), and ring finger protein 43 (RNF43) (13%) were identified as significantly mutated genes. The tumor-specific transcriptome was classified into two clusters (tumor S1 and tumor S2), which resembled the Moffitt tumor classification. Tumor S1 displayed two distinct subclusters (S1-1 and S1-2). The transcriptome of tumor S1-1 overlapped with the exocrine-like (Collisson)/ADEX (Bailey) subtype, while tumor S1-2 mostly consisted of the classical (Collisson)/progenitor (Bailey) subtype. In the analysis of combinatorial gene alterations, concomitant mutations of KRAS with low-density lipoprotein receptor related protein 1B (LRP1B) were associated with significantly worse disease-free survival after surgery (p = 0.034). One patient (1.2%) was an ultrahypermutant with microsatellite instability. We also identified high protein kinase C lota (PRKCI) expression as an overlapping, poor prognostic marker between our dataset and the TCGA dataset.
Conclusion:
We identified potential prognostic biomarkers and therapeutic targets of patients with PDAC. Understanding these molecular aberrations that determine patient outcomes after surgery and chemotherapy has the potential to improve the treatment outcomes of PDAC patients.
Insights
Pancreatic cancer (PDAC) molecular subtypes were identified using genomic and transcriptomic analysis. Specific gene mutations, like KRAS with LRP1B, correlate with poorer survival, guiding potential therapeutic targets.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Pancreatic ductal adenocarcinoma (PDAC) exhibits a poor prognosis among cancers.
- Key genomic pathways in PDAC include KRAS, TP53, CDKN2A, and SMAD4.
- Limited data exists on molecular features impacting PDAC patient outcomes post-surgery or chemotherapy.
Purpose of the Study:
- To perform comprehensive molecular characterization of PDAC tumors.
- To identify molecular biomarkers and therapeutic targets by integrating genomic, transcriptomic, and clinical data.
- To correlate molecular aberrations with clinical outcomes in PDAC patients.
Main Methods:
- Whole-exome and RNA sequencing of tumor and matched normal tissues from 83 PDAC patients undergoing surgery.
- Integrative analysis of genomic, transcriptomic, and clinical data.
- Molecular subtyping of tumor transcriptomes.
Main Results:
- Identified significantly mutated genes: KRAS (75%), TP53 (67%), SMAD4 (20%), CDKN2A (12%), RNF43 (13%).
- Classified tumor transcriptomes into two main clusters (S1, S2) with subtypes resembling known classifications (ADEX, classical/progenitor).
- Concomitant KRAS mutations and LRP1B alterations associated with worse disease-free survival (p=0.034); high PRKCI expression identified as a poor prognostic marker.
Conclusions:
- Identified potential prognostic biomarkers and therapeutic targets for PDAC.
- Understanding molecular aberrations can improve treatment strategies and outcomes for PDAC patients.
- Molecular subtyping provides insights into PDAC heterogeneity and clinical behavior.
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