Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies.

Lorenzo Tortolina,Silvio Parodi,Elia M Biganzoli,Boris N Kholodenko,Franco Patrone,Alberto Ballestrero,Alessandro Verri,Massimo Maffei,Aimée M Carmody,Annalisa Barla,Gabriele Zoppoli,Nicoletta Castagnino,David J Duffy,Cristina De Ambrosi,Alessio Nencioni,Walter Kolch

doi:10.18632/oncotarget.3238

Lorenzo Tortolina, Silvio Parodi + Show 14 more

Open Access

https://doi.org/10.18632/oncotarget.3238

Copy DOI

Abstract

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

Highlights

Colorectal cancer (CRC) can be characterized according to the genomic landscapes of individual CRC patients [1]
The Molecular Interaction Map (MIM) reconstructed and used in our simulations are shown in Fig. 1 and Supplementary Fig. 1.3, for HCT116 and HT29 cancer lines, respectively
We started from a signaling-network model without cancer mutations (a “physiologic model”) and generated models including specific mutations/alterations, concordant with those present in the HCT116 and HT29 colon cancer lines

Summary

Introduction

Colorectal cancer (CRC) can be characterized according to the genomic landscapes of individual CRC patients [1]. Vogelstein et al have recently reported [1] a model featuring around 2-5 major driver mutations per individual CRC tumor. In the COSMIC release of June 2nd 2014 [2], the curators have estimated that an individual cancer can be caused by 5 -10 driver mutations (not identical in different tumors), against a background of more than 10,000 passenger mutations per tumor. We will encounter most often the most frequent driver mutations/ alterations, from the perspective of the individual tumor of a specific patient, we could have had a Darwinian evolution to cancer, through a constellation of (at least in part) much less frequent driver mutations/alterations. An uncommon mutation / somatically inheritable alteration could make a specific patient differently sensitive or resistant to a specific inhibitor

Methods

Results

Conclusion