The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with limited long-term survival despite advances in surgery and systemic therapy. PATIENTS: The population of interest comprises patients with PDAC characterized by targetable molecular alterations and biologically distinct transcriptomic subtypes. METHODS: We performed a narrative review of landmark and contemporary clinical trials, translational studies, and emerging molecular-classification platforms relevant to precision oncology in PDAC. RESULTS: Growing understanding of PDAC molecular biology has identified putative genetic mutations, including homologous recombination repair deficiency, mismatch repair deficiency, and mutated KRAS, enabling the development of targeted therapies and precision treatment strategies. Concurrently, transcriptomic profiling has revealed biologically distinct molecular subtypes associated with differences in prognosis and therapeutic response. Emerging tools such as molecular classifiers, deep learning models, and multiomic platforms may further refine patient selection and treatment personalization. CONCLUSIONS: This review highlights contemporary efforts of novel targeted therapies, ongoing advances in molecular subtyping, and the evolving role of precision oncology in improving outcomes for patients with PDAC.

Authors

Institutions

Publication Details

Journal
Annals of Surgical Oncology
Published
2026-09-12
DOI
https://doi.org/10.1245/s10434-026-20366-0
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer

McKenzie L. Schaefer, Alex B. Blair, Susan Tsai
Annals of Surgical Oncology
Pancreatic and Hepatic Oncology Research
article

The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer

McKenzie L. Schaefer, Alex B. Blair, Susan Tsai
article en

Abstract

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy with limited long-term survival despite advances in surgery and systemic therapy. PATIENTS: The population of interest comprises patients with PDAC characterized by targetable molecular alterations and biologically distinct transcriptomic subtypes. METHODS: We performed a narrative review of landmark and contemporary clinical trials, translational studies, and emerging molecular-classification platforms relevant to precision oncology in PDAC. RESULTS: Growing understanding of PDAC molecular biology has identified putative genetic mutations, including homologous recombination repair deficiency, mismatch repair deficiency, and mutated KRAS, enabling the development of targeted therapies and precision treatment strategies. Concurrently, transcriptomic profiling has revealed biologically distinct molecular subtypes associated with differences in prognosis and therapeutic response. Emerging tools such as molecular classifiers, deep learning models, and multiomic platforms may further refine patient selection and treatment personalization. CONCLUSIONS: This review highlights contemporary efforts of novel targeted therapies, ongoing advances in molecular subtyping, and the evolving role of precision oncology in improving outcomes for patients with PDAC.

Annals of Surgical Oncology
The Ohio State University Wexner Medical Center (US)
Central Surgical Association
Good health and well-being
Openalex Percentile: Top 14%
Pancreatic and Hepatic Oncology Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.