Work

Modeling tumor dynamics and predicting response to therapies in a murine pancreatic cancer model

Year: 2025

Type: article

Abstract: We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size under the influence of cocktails of treatments. Parameter estimation relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model ( $${{Kras}}^{{\rm{LSL}}-{\rm{G12D}}}\,;\,{{Trp53}}^{{\rm{LSL}}-{\rm{R172H}}}\,;\,{Pdx1}-{\rm{Cre}}$$ K r a s L S L - G 12 D ; T r p 53 L S L - R 172 H ; P d x 1 - C r e ). Our model reproduces tumor growth in all scenarios with an average concordance correlation coefficient (CCC) of 0.99 ± 0.01. We conduct leave-one-out predictions (average CCC = 0.74 ± 0.06), mouse-specific predictions (average CCC = 0.75 ± 0.02), and hybrid, group-informed, mouse-specific predictions (average CCC = 0.85 ± 0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.

Source: npj Systems Biology and Applications

DOI: 10.1038/s41540-025-00593-z