Building patient‐specific models for receptor tyrosine kinase signaling networks

Abstract

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Systems Biology in ELIXIR: modelling in the spotlight

“…Populating systems biological models with personal data can yield highly individualised models that can help simulate disease evolution and response to therapy with high sensitivity and specificity (Barrette et al, 2018;Béal et al, 2021;Bhinder & Elemento, 2017;Crawford et al, 2018;Eduati et al, 2020;Fey et al, 2015;Hastings et al, 2020). These systems medicine models are knowledge-based and thus able to offer insight into the disease and drug response mechanisms of a patient (Ebata et al, 2022;Hutter & Zenklusen, 2018). For example, a personalised model of the JNK stressresponse network resulted in refined patient-stratification for neuroblastoma, a common childhood cancer, and revealed an impairment of the JNK apoptotic switch in high-risk cases (Fey et al, 2015).…”

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

“…ODE models are attractive because they can incorporate not only imaging data, but also multidimensional omics data (e.g., genomic, proteomic, transcriptomic, and metabolomic [148]), which makes such models a practical paradigm for accounting for multi-scale mechanisms. For example, intracellular signaling pathways and metabolic networks are commonly represented by coupled ODEs describing the temporal dynamics of entire signaling pathways [136,137,[149][150][151]. Inter-cellular interactions and transformations, such as communication between tumor-immune cells, and epithelial-mesenchymal transition can also be represented by ODEs [152,153].…”

Systems Biology in ELIXIR: modelling in the spotlight

“…Populating systems biological models with personal data can yield highly individualised models that can help simulate disease evolution and response to therapy with high sensitivity and specificity ( Barrette et al , 2018 ; Béal et al , 2021 ; Bhinder & Elemento, 2017 ; Crawford et al , 2018 ; Eduati et al , 2020 ; Fey et al , 2015 ; Hastings et al , 2020 ). These systems medicine models are knowledge-based and thus able to offer insight into the disease and drug response mechanisms of a patient ( Hutter & Zenklusen, 2018 ; Ebata et al , 2022 ). For example, a personalised model of the JNK stress-response network resulted in refined patient-stratification for neuroblastoma, a common childhood cancer, and revealed an impairment of the JNK apoptotic switch in high-risk cases ( Fey et al , 2015 ).…”