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Author Spotlight: Reprogramming Cancer Cells to iPSCs to Study Disease Progression and Treatment Targets
Published on: February 2, 2024
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.
Abstract:
Precision-guided therapy is imperative in the battle against pancreatic ductal adenocarcinoma (PDAC), one of the most lethal solid malignancies with limited improvements in survival despite advances in molecular profiling and systemic therapy. While oncogenic drivers such as KRAS, TP53, CDKN2A, and SMAD4 are nearly ubiquitous, their translation into effective targeted therapies has been constrained by profound tumor heterogeneity, a therapy-resistant tumor microenvironment (TME), and a paucity of predictive biomarkers. In parallel, clinical outcomes are increasingly shaped by extrinsic modifiers, including metabolic disease, chronic inflammation, and microbiome dysregulation, which remain under-integrated into current treatment paradigms. Recent progress in precision oncology has enabled regulatory approval of biomarker-defined therapies for select PDAC subsets, including immune checkpoint inhibitors for mismatch repair-deficient tumors, PARP inhibitors for BRCA1/2-mutant disease, and combination cytotoxic regimens such as NALIRIFOX in the metastatic setting. However, these advances benefit only a minority of patients, underscoring the urgent need for improved patient stratification and rational combination strategies. Emerging clinical and translational studies highlight the promise of integrating multi-omic profiling, liquid biopsies, functional precision models (organoids and patient-derived xenografts), and artificial intelligence-driven analytics to uncover actionable vulnerabilities, monitor response, and guide adaptive trial design. In this review, we critically evaluate the clinical relevance of molecular, metabolic, and microenvironmental determinants of PDAC progression and therapeutic resistance. We focus on translational bottlenecks that have limited clinical success to date and highlight biomarker-driven strategies, ongoing clinical trials, and emerging technologies poised to shift treatment from uniform algorithms toward biologically informed, patient-specific therapeutic approaches in pancreatic cancer.
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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