title: Looking beyond the cancer cell for effective drug combinations.
abstract: Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer's pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient's response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs-in terms of both computational and data requirements-that will truly empower such combinations.
authors.affiliation: Oncology Innovative Medicines and Early Development, AstraZeneca, R&D Boston, Waltham, MA, 02451, USA. Jonathan.Dry@astrazeneca.com.
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Dry
authors.affiliation: Rheinisch-Westfälische Technische Hochschule Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, 52057, Germany.
authors.forename: Mi
authors.initials: M
authors.lastname: Yang
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK. saezrodriguez@gmail.com.|Rheinisch-Westfälische Technische Hochschule Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, 52057, Germany. saezrodriguez@gmail.com.
authors.forename: Julio
authors.identifier: 0000-0002-8552-8976
authors.initials: J
authors.lastname: Saez-Rodriguez
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D045744:Cell Line, Tumor; D002986:Clinical Trials as Topic; D019295:Computational Biology / Q000379:methods*; D015195:Drug Design; D019008:Drug Resistance, Neoplasm; D004359:Drug Therapy, Combination; D044127:Epigenesis, Genetic / Q000187:drug effects; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy*; D016019:Survival Analysis; D059016:Tumor Microenvironment / Q000187:drug effects
chemical_list: D000970:Antineoplastic Agents