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Systems Medicine in Colorectal Cancer: from a Mathematical Model Toward a New Type of Clinical Trial

Abstract

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 For further resources related to this article, please visit the WIREs website.

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References
1.
OConnell M, Lavery I, Yothers G, Paik S, Clark-Langone K, Lopatin M . Relationship between tumor gene expression and recurrence in four independent studies of patients with stage II/III colon cancer treated with surgery alone or surgery plus adjuvant fluorouracil plus leucovorin. J Clin Oncol. 2010; 28(25):3937-44. PMC: 2940392. DOI: 10.1200/JCO.2010.28.9538. View

2.
Saumet A, Mathelier A, Lecellier C . The potential of microRNAs in personalized medicine against cancers. Biomed Res Int. 2014; 2014:642916. PMC: 4163464. DOI: 10.1155/2014/642916. View

3.
Jiang J, Zheng X, Xu X, Zhou Q, Yan H, Zhang X . Prognostic significance of miR-181b and miR-21 in gastric cancer patients treated with S-1/Oxaliplatin or Doxifluridine/Oxaliplatin. PLoS One. 2011; 6(8):e23271. PMC: 3158077. DOI: 10.1371/journal.pone.0023271. View

4.
Wu J, Wu G, Lv L, Ren Y, Zhang X, Xue Y . MicroRNA-34a inhibits migration and invasion of colon cancer cells via targeting to Fra-1. Carcinogenesis. 2011; 33(3):519-28. DOI: 10.1093/carcin/bgr304. View

5.
Garraway L, Lander E . Lessons from the cancer genome. Cell. 2013; 153(1):17-37. DOI: 10.1016/j.cell.2013.03.002. View