Advancing precision medicine in pancreatic cancer using novel biomarkers and clinical targets

Divya Murthy1, Attila A Seyhan2, Petra den Hollander1

  • 1Legorreta Cancer Center, Warren Alpert Medical School, Brown University, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, United States.

Insights

Precision therapy for pancreatic cancer (PDAC) faces challenges from tumor heterogeneity and resistance. Integrating multi-omics, AI, and biomarkers offers new hope for personalized treatment strategies.

Area of Science:

  • Oncology
  • Cancer Biology
  • Precision Medicine

Background:

  • Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with poor survival rates.
  • Despite molecular profiling, targeted therapy success is limited by tumor heterogeneity, a resistant tumor microenvironment (TME), and lack of predictive biomarkers.
  • Extrinsic factors like metabolic disease and inflammation are under-integrated into PDAC treatment.

Purpose of the Study:

  • To critically evaluate molecular, metabolic, and microenvironmental factors influencing PDAC progression and therapeutic resistance.
  • To identify translational bottlenecks hindering clinical success in PDAC.
  • To highlight biomarker-driven strategies and emerging technologies for personalized PDAC treatment.

Main Methods:

  • Review of clinical and translational studies.
  • Analysis of multi-omic profiling, liquid biopsies, and functional precision models (organoids, PDXs).
  • Evaluation of artificial intelligence (AI)-driven analytics for identifying therapeutic vulnerabilities.

Main Results:

  • Current precision therapies benefit only a subset of PDAC patients.
  • Advances in biomarker-defined therapies (e.g., PARP inhibitors, immune checkpoint inhibitors) show promise for specific PDAC subsets.
  • Emerging technologies offer potential for improved patient stratification and response monitoring.

Conclusions:

  • There is an urgent need for better patient stratification and combination strategies in PDAC.
  • Integrating multi-modal data (multi-omics, liquid biopsies, AI) can uncover actionable vulnerabilities.
  • The future of PDAC treatment lies in shifting towards biologically informed, patient-specific therapeutic approaches.

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