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WIREs Syst Biol Med
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Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

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Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro‐RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1‐S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical‐trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314–336. doi: 10.1002/wsbm.1342 This article is categorized under: Biological Mechanisms > Cell Signaling Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Translational Medicine
Flow chart of the different phases of implementation of our dynamic model.
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We introduced, in our dynamic model, (in the abscissae) all the possible permutations of the five mutations (and mutated pathways) present in the organoids of: WNT (APC), MAPK (KRAS), TGF‐β (SMAD4), PI3K, and TP53 pathways. In the ordinates, we show the increased transcription rates of the complex described in Figure . At 4–5 mutations and 20–30 A.U. of mRNAs levels, we appear to cross the cell‐cycle restriction point. At position −1 in the abscissae, we show the strong inhibitory effect of a deleted β‐catenin.
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Response to different inhibitor treatments. Scatter plots of experimental (Y‐axis) versus simulated (X‐axis) values. Notice that only the Y‐axis represents experimental variability. Top graph to the left: ERKPP protein levels. Top graph to the right: AKTP protein levels. Bottom graph to the left: MYC mRNA levels. Bottom graph to the right: CCND1 mRNA levels. The (0;0) origin of the two axes makes reference to a theoretical complete inhibition, both experimental and simulated. Notice the (0;0) origin is never reached, either in experimental or simulated values, even for strong inhibitions. Similarly, the 45° dotted diagonal makes reference to an ideal complete correspondence between simulated and experimental data. We used the following inhibitors and inhibitor combinations—1, 2: Controls; 3: XAV939; 4: PI103; 5: CI1040; 6: Perifosine 20nM; 7: Perifosine 40nM; 8: XAV939 + PI103; 9: XAV939 + CI1040; 10: PI103 + CI1040; 11: Perifosine 20nM + CI1040; 12: Perifosine 40nM + CI1040; 13: XAV939 + PI103 + CI1040; 14: XAV939 + PI103 + CI1040 + Perifosine 20nM; 15: XAV939 + PI103 + CI1040 + Perifosine 40nM. Figure elaborated from results presented in Ref .
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Simplified scheme of a model promoter region regulating MYC and CCND1 transcription. The model promoter region consists of a number of important Transcription Factor Binding Sites, depicted in arbitrary order. Each TF considered to bind to its promoter is a component of the MIM. Schematic representation of the promoter region, with activators (a) and repressors (b) assumed to bind to each TFBS in the model. Arrows indicate the potential for binding at the TFBS. We also show the DNA binding regions of activators (white) and repressors (black). [Reprinted with permission from Ref . Copyright 2008–2015 Impact Journals, LLC]
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We started with a dynamic model of, carrying mutations in: WNT (APC), MAPK (KRAS), TGF‐β (SMAD4), and PI3K pathways. We gave simulated combinations of the following inhibitors: PanErb inhibitor, MEK inhibitor, PI3K inhibitor, AKT inhibitor, and MDM2 inhibitor. We show the effects of the simulated combination of the five inhibitors. We show permutations of 0 (1), 1 (5), 2 (10), 3 (10), 4 (5), and 5 (1) inhibitors.
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Molecular Interaction Map (MIM) including most of the G0–G1 transition in CRC. We surrounded the cartouches (like in hieroglyphics), of mutated/altered signaling‐proteins in the HCT116 line, with a light yellow oval. The oncoprotein inhibitors are without cartouches and an arrow indicates their relative targets. We enlarged this new MIM by about 50% in respect to the MIM we published in 2015. Numbers near the interaction lines refer to an annotation list of interactions (supplementary material of our 2015 article). Grid coordinates (numbers at the left and letters on top of the MIM) help to locate molecular species and interactions present in the MIM.
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