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

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.