Toward Precision Chemotherapy for Pancreatic Cancer Guided by Transcriptomic Signatures

Area of Science:

• Oncology
• Genomics
• Translational Medicine

Background:

• Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with chemotherapy as the primary treatment.
• Current chemotherapy regimens (e.g., FOLFIRINOX, gemcitabine) are chosen empirically, lacking biomarkers for personalized selection.
• Existing personalization strategies like organoids and molecular classifiers have limited clinical utility.

Purpose of the Study:

• To evaluate the clinical applicability of predictive transcriptomic signatures for guiding chemotherapy selection in PDAC.
• To assess the role of these signatures in optimizing treatment strategies and improving patient survival.
• To explore the potential of transcriptomic profiling for precision chemotherapy in PDAC.

Main Methods:

• Development and validation of predictive transcriptomic signatures (e.g., GemPred, GemCore, Pancreas-View).
• Integration of AI and multi-drug predictors within signatures.
• Retrospective validation in large patient cohorts and clinical trials.

Main Results:

• Transcriptomic signatures consistently predict patient response to specific chemotherapies (gemcitabine, FOLFIRINOX components).
• Predicted drug sensitivity correlates with improved survival outcomes.
• Signatures facilitate treatment de-escalation, optimize first-line therapy choices, and identify resistant tumors.

Conclusions:

• Predictive transcriptomic signatures are practical tools for personalizing PDAC chemotherapy.
• These signatures support precision medicine by enabling informed treatment selection and de-escalation.
• Ongoing trials will confirm the feasibility of transcriptomic profiling for precision chemotherapy in PDAC.