title: Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems.
abstract: Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy. silvio.parodi@unige.it.
authors.forename: Silvio
authors.initials: S
authors.lastname: Parodi
authors.affiliation: Signals and Interactive Systems lab, Department of Engineering and Information Science, Trento University, Trento, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Riccardi
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Nicoletta
authors.initials: N
authors.lastname: Castagnino
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Tortolina
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Maffei
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Zoppoli
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Alessio
authors.initials: A
authors.lastname: Nencioni
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Ballestrero
authors.affiliation: Department of Internal Medicine (DIMI), Genoa University, Viale Benedetto XV n. 6, 16132, Genoa, Italy.
authors.forename: Franco
authors.initials: F
authors.lastname: Patrone
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030541:Databases, Genetic; D020000:Decision Support Systems, Clinical; D006801:Humans; D008495:Medical Oncology / Q000379:methods; D008954:Models, Biological; D009369:Neoplasms / Q000209:etiology / Q000378:metabolism / Q000628:therapy; D057285:Precision Medicine / Q000379:methods; D012107:Research Design; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods
keywords: Artificial intelligence; Cancer genomics; Decision-support systems; IBM Watson; Individual cancer patient; New clinical trial designs; Oncoprotein inhibitors; Rational associations of targeted inhibitors; Signaling-network pathways; Systems medicine