Personalized management of pancreatic ductal adenocarcinoma patients through computational modeling

Kimiyo N. Yamamoto, Shinichi Yachida, Akira Nakamura, Atsushi Niida, Minoru Oshima, Subhajyoti De, Lauren M. Rosati, Joseph M. Herman, Christine A. Iacobuzio-Donahue, Hiroshi Haeno

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Phenotypic diversity in pancreatic ductal adenocarcinoma (PDAC) results in a variety of treatment responses. Rapid autopsy studies have revealed a subgroup of PDAC patients with a lower propensity to develop metastatic disease, challenging the common perception that all patients die of widely metastatic disease, but questions remain about root causes of this difference and the potential impact on treatment strategies. In this study, we addressed these questions through the development of a mathematical model of PDAC progression that incorporates the major alteration status of specific genes with predictive utility. The model successfully reproduced clinical outcomes regarding metastatic patterns and the genetic alteration status of patients from two independent cohorts from the United States and Japan. Using this model, we defined a candidate predictive signature in patients with low metastatic propensity. If a primary tumor contained a small fraction of cells with KRAS and additional alterations to CDKN2A, TP53, or SMAD4 genes, the patient was likely to exhibit low metastatic propensity. By using this predictive signature, we computationally simulated a set of clinical trials to model whether this subgroup would benefit from locally intensive therapies such as surgery or radiation therapy. The largest overall survival benefit resulted from complete resection, followed by adjuvant chemoradiation therapy and salvage therapies for isolated recurrence. While requiring prospective validation in a clinical trial, our results suggest a new tool to help personalize care in PDAC patients in seeking the most effective therapeutic modality for each individual.

Original languageEnglish
Pages (from-to)3325-3335
Number of pages11
JournalCancer Research
Volume77
Issue number12
DOIs
Publication statusPublished - Jun 15 2017

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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