In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways

Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B, 50, 289 Campillos, 2008, Drug target identification using side-effect similarity, Science., 321, 263, 10.1126/science.1158140 Cava, 2013, 104 Cava, 2013, Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2013, 608 Cava, 2015, Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential, BMC Syst. Biol., 9, 62, 10.1186/s12918-015-0211-x Cava, 2016, How interacting pathways are regulated by miRNAs in breast cancer subtypes, BMC Bioinf., 17, 348, 10.1186/s12859-016-1196-1 Cava C, Colaprico A, Bertoli G, Graudenzi A, Silva TC, Olsen C, Noushmehr H, Bontempi G, Mauri G, Castiglioni I. SpidermiR: an R/Bioconductor package for integrative analysis with miRNA data. Int. J. Mol. Sci. 2017 Jan 27;18(2). pii: E274. doi:https://doi.org/10.3390/ijms18020274 Cava, 2018, Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis, BMC Genomics, 19, 25, 10.1186/s12864-017-4423-x Cava, 2018, In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition, J. Transl. Med., 16, 154, 10.1186/s12967-018-1535-2 Chipitsyna, 2004, HIV-1 tat increases cell survival in response to cisplatin by stimulating Rad51 gene expression, Oncogene., 23, 2664, 10.1038/sj.onc.1207417 Choudhury, 2009, Targeting homologous recombination using imatinib results in enhanced tumor cell chemosensitivity and radiosensitivity, Mol. Cancer Ther., 8, 203, 10.1158/1535-7163.MCT-08-0959 Colaprico, 2015, Integrative analysis with monte carlo cross-validation reveals mirnas regulating pathways cross-talk in aggressive breast cancer, Biomed. Res. Int., 2015, 831314, 10.1155/2015/831314 Csermely, 2005, The efficiency of multi-target drugs: the network approach might help drug design, Trends Pharmacol. Sci., 26, 178, 10.1016/j.tips.2005.02.007 Dehghanifard, 2018, Various signaling pathways in multiple myeloma cells and effects of treatment on these pathways, Clin. Lymphoma Myeloma Leuk., 18, 311, 10.1016/j.clml.2018.03.007 Dong, 2016, LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights, Sci. Rep., 6 Fang, 2012, A network-based gene-weighting approach for pathway analysis, Cell Res., 22, 565, 10.1038/cr.2011.149 Goh, 2007, The human disease network, Proc. Natl. Acad. Sci. U. S. A., 104, 8685, 10.1073/pnas.0701361104 Grulich, 2007, Incidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis, Lancet, 370, 59, 10.1016/S0140-6736(07)61050-2 Gu, 2013, Platelet aggregation pathway network-based approach for evaluating compounds efficacy, Evid. Based Complement. Alternat. Med., 2013, 10.1155/2013/425707 Guney, 2016, Network-based in silico drug efficacy screening, Nat. Commun., 7, 10.1038/ncomms10331 Hofman, 1993, Exogenous tat protein activates human endothelial cells, Blood, 82, 2774, 10.1182/blood.V82.9.2774.2774 Howard, 2016, Preventing and managing toxicities of high-dose methotrexate, Oncologist, 21, 1471, 10.1634/theoncologist.2015-0164 Jaeger, 2017, Quantification of pathway cross-talk reveals novel synergistic drug combinations for breast Cancer, Cancer Res., 77, 459, 10.1158/0008-5472.CAN-16-0097 Jeong, 2018, Insulin receptor substrate 2: a bridge between Hippo and AKT pathways, BMB Rep., 51, 209, 10.5483/BMBRep.2018.51.5.095 Juan-Blanco T, Duran-Frigola M, Aloy P. Rationalizing drug response in cancer cell lines. J. Mol. Biol. 2018 Apr 4. pii: S0022-2836(18)30170-0. doi:https://doi.org/10.1016/j.jmb.2018.03.021 Ko, 2008, Role of repair protein Rad51 in regulating the response to gefitinib in human non-small cell lung cancer cells, Mol. Cancer Ther., 7, 3632, 10.1158/1535-7163.MCT-08-0578 Larsen, 2016, Dexamethasone and high-dose methotrexate improve outcome for children and young adults with high-risk B-acute lymphoblastic leukemia: a report from Children's oncology group study AALL0232, J. Clin. Oncol., 34, 2380, 10.1200/JCO.2015.62.4544 Lehár, 2007, Chemical combination effects predict connectivity in biological systems, Mol. Syst. Biol., 3, 80, 10.1038/msb4100116 Lehár, 2008, High-order combination effects and biological robustness, Mol. Syst. Biol., 4, 10.1038/msb.2008.51 Li, 2011, A network-based multi-target computational estimation scheme for anticoagulant activities of compounds, PLoS One, 6, e14774, 10.1371/journal.pone.0014774 Liaw, 2002, Classification and regression by randomforest, R News., 2, 18 Menche, 2015, Disease networks. Uncovering disease-disease relationships through the incomplete interactome, Science., 347, 10.1126/science.1257601 Nagurney A, Qiang Q. A network efficiency measure for congested networks. Epl-Europhys. Lett. 2007;79(3):38005. National Cancer Institute: http://www.cancer.gov/about-cancer/treatment/ Access 28 May 2018. Pataer, 2018, Major pathologic response and RAD51 predict survival in lung cancer patients receiving neoadjuvant chemotherapy, Cancer Med., 10.1002/cam4.1505 Roila, 2010, Guideline update for MASCC and ESMO in the prevention of chemotherapy- and radiotherapy-induced nausea and vomiting: results of the Perugia consensus conference, Ann. Oncol., 21, v232, 10.1093/annonc/mdq194 Rolland, 2014, A proteome-scale map of the human interactome network, Cell., 159, 1212, 10.1016/j.cell.2014.10.050 Serra-Musach, 2016, Cancer network activity associated with therapeutic response and synergism, Genome Med., 8, 88, 10.1186/s13073-016-0340-x Tenenbaum, 2016 Wagner, 2016, DGIdb 2.0: mining clinically relevant drug-gene interactions, Nucleic Acids Res., 44, D1036, 10.1093/nar/gkv1165 Way, 2018, Machine learning detects Pan-cancer Ras pathway activation in the Cancer genome atlas, Cell Rep., 23, 172, 10.1016/j.celrep.2018.03.046 Yamanishi, 2010, Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework, Bioinformatics., 26, i246, 10.1093/bioinformatics/btq176 Zuberi, 2013, GeneMANIA prediction server 2013 update, Nucleic Acids Res., 41, W115, 10.1093/nar/gkt533