Zabaleta, Jovanny

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival rate of 13.3%. First-line treatment relies on two chemotherapy regimens, FOLFIRINOX (FOLFNX) or gemcitabine plus nab-paclitaxel (GEMPAC). However, direct clinical comparisons between these regimens have yielded inconsistent results across survival and toxicity endpoints, and the molecular basis of heterogeneous treatment responses remains poorly defined. To investigate regimen-specific tumor-cell-intrinsic mechanisms, we performed quantitative proteomic profiling of a primary PDAC-derived MIA PaCa-2 cell line following treatment with FOLFNX or GEMPAC. Differentially expressed proteins were analyzed using Gene Ontology, KEGG, and Ingenuity Pathway Analysis to define pathway-level alterations, and findings were contextualized using TCGA transcriptomic data. Proteomic analyses revealed that FOLFNX and GEMPAC engage in distinct cytotoxic programs. FOLFNX predominantly suppressed ribosome biogenesis and mitochondrial translation, consistent with sustained metabolic and biosynthetic stress, whereas GEMPAC preferentially disrupted mitotic cytokinesis and phosphatidylinositol phosphate biosynthesis, consistent with mitotic failure. Integration with TCGA data showed that FOLFNX-altered proteins aligned with favorable prognostic expression signatures, whereas GEMPAC-associated proteins were enriched among adverse profiles, reflecting engagement of distinct tumor-intrinsic programs. Together, these findings provide mechanistic insight into differential chemotherapy responses and establish a foundation for proteomics-based biomarkers to guide personalized chemotherapy selection in PDAC.

Abstract Motivation Testing SNP–SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP–SNP interactions are underdeveloped. Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP–SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/. Supplementary information Supplementary data are available at Bioinformatics online.