In silico signaling modeling to understand cancer pathways and treatment responses

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

[1]  Thomas Dandekar,et al.  The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development , 2016, Database J. Biol. Databases Curation.

[2]  Tzong-Yi Lee,et al.  Identification of potential biomarkers related to glioma survival by gene expression profile analysis , 2019, BMC Medical Genomics.

[3]  Thomas Dandekar,et al.  Convergence behaviour and Control in Non-Linear Biological Networks , 2015, Scientific Reports.

[4]  Giovanni De Micheli,et al.  Dynamic simulation of regulatory networks using SQUAD , 2007, BMC Bioinformatics.

[5]  Tatiana Martins Tilli,et al.  Toward precision medicine of breast cancer , 2016, Theoretical Biology and Medical Modelling.

[6]  Steven J. M. Jones,et al.  Comprehensive Characterization of Cancer Driver Genes and Mutations , 2018, Cell.

[7]  Jongho Kim,et al.  An integrated clinical and genomic information system for cancer precision medicine , 2018, BMC Medical Genomics.

[8]  Sun Kim,et al.  Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data , 2018, Briefings Bioinform..

[9]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[10]  Wylie Burke,et al.  Essential elements of personalized medicine. , 2014, Urologic oncology.

[11]  Shingo Iwami,et al.  Mathematical modeling of multi-drugs therapy: a challenge for determining the optimal combinations of antiviral drugs , 2014, Theoretical Biology and Medical Modelling.

[12]  Thomas Dandekar,et al.  Jimena: efficient computing and system state identification for genetic regulatory networks , 2013, BMC Bioinformatics.

[13]  Lee T. Sam,et al.  Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study , 2011, Science Translational Medicine.

[14]  Laura La Paglia,et al.  Driver mutations and differential sensitivity to targeted therapies: a new approach to the treatment of lung adenocarcinoma. , 2010, Cancer treatment reviews.

[15]  J. Tuszynski,et al.  A Computational Strategy to Select Optimized Protein Targets for Drug Development toward the Control of Cancer Diseases , 2015, PloS one.

[16]  A. Rosenwald,et al.  Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival , 2007, Cancer informatics.

[17]  Heike Walles,et al.  A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds. , 2016, Journal of visualized experiments : JoVE.

[18]  Radhakant Padhi,et al.  A patient-specific therapeutic approach for tumour cell population extinction and drug toxicity reduction using control systems-based dose-profile design , 2013, Theoretical Biology and Medical Modelling.

[19]  Thomas Dandekar,et al.  Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis , 2008, BMC Cancer.

[20]  Laura Tolosi,et al.  Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. , 2009, The Journal of clinical investigation.

[21]  Steven J. M. Jones,et al.  Oncogenic Signaling Pathways in The Cancer Genome Atlas. , 2018, Cell.

[22]  Nallasivam Palanisamy,et al.  Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth. , 2015, JAMA.

[23]  Heike Walles,et al.  Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model , 2013, Molecular oncology.

[24]  Heike Walles,et al.  A combined tissue‐engineered/in silico signature tool patient stratification in lung cancer , 2018, Molecular oncology.

[25]  Roy Kishony,et al.  Networks from drug–drug surfaces , 2007, Molecular systems biology.