He goal is to make cancer cells like normal cells, namely
He goal is to make cancer cells like normal cells, namely, cured but not killed; e.g. Gleevec usage is an instance of this form of the indirect approach, and it appears that Gleevec does not kill BCR-ABL leukemic stem cells [54]. When using normal tissue counterpart guides in the indirect approach, an infinite clamp is called for once the target state region has been reached, since there are no risks of treatment interactions given that the standard regimen is null in this case. Thus, for the indirect approach, normal tissue guides yield lifelong cancer treatment solutions. In contrast, since cured cancer guides are killed by standard therapies, the PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28154141 cancer cells of the non-curable patients should follow suit, i.e. using cured cancer guides in the indirect approach should yield finite treatment solutions. This is an expected advantage of using bona fide solved cancer counterparts rather than normal tissues as indirect approach guides. A logical research sequence in systems cancer biology is to: a) select a relatively well-understood cancer and a treatment strategy (preferably the direct approach); b) build an initial model of processes that are most pertinent to the selected cancer; c) iteratively improve the model through experimental validations and model expansions; and d) develop model-based optimal control solutions to the corresponding optimal control LCZ696 custom synthesis problems. Currently, we are focusing on steps a) and b) for MMR- cancers, though we are also interested in BCR-ABL leukemias.ConclusionBecause the scope of a model is determined by its intended uses, models should be developed with their ultimate uses in mind. As biological knowledge, the number of anti-cancer agents, and the number of possible measurements, continues to grow, mathematical models of cancer relevant processes will find uses in the design of state feedback-based clinical trials. Such model-based, systems and control oriented therapies will be individualized via polymorphism-based model parameter perturbations and patient differences in initial states (see Figure 1). Conceptual frameworks such as those presented here are needed to accomplish these goals.Competing interestsThe author(s) declare that they have no competing interests.Page 7 of(page number not for citation purposes)BMC Cancer 2006, 6:http://www.biomedcentral.com/1471-2407/6/Authors’ contributionsTR performed the computations and wrote the first two drafts, KAL wrote sections requiring control system expertise, RCJ contributed the cell cycle inhibition example in the Discussion, and WDS improved the writing in all of the sections.18.19. 20.AcknowledgementsWe thank Dr. James W. Jacobberger for his critical review of the manuscript. This work was supported by the Comprehensive Cancer Center of Case Western Reserve University and University Hospitals of Cleveland (P30 CA43703), the American Cancer Society (IRG-91-022-09), the National Cancer Institute’s Integrative Cancer Biology Program (CA112963) and NIH grants K25 CA104791 and R01 CA101983. 21. 22.23.
BMC CancerResearch articleBioMed CentralOpen AccessTranslating microarray data for diagnostic testing in childhood leukaemiaKatrin Hoffmann*1, Martin J Firth2, Alex H Beesley1, Nicholas H de Klerk2 and Ursula R KeesAddress: 1Division of Children’s Leukaemia and Cancer Research, Telethon Institute for Child Health Research and Centre for Child Health Research, The University of Western Australia, Perth, Australia and 2Division of Biostatistics and Genetic Epidemio.