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A convex optimization approach to cancer treatment to address tumor heterogeneity and imperfect drug penetration in physiological compartments

Giordano, Giulia LU ; Rantzer, Anders LU and Jonsson, Vanessa D. (2016) 55th IEEE Conference on Decision and Control 2016 In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 p.2494-2500
Abstract

The clinical success of targeted cancer therapies is limited by the emergence of drug resistance often due to pre-existing tumor genetic heterogeneity and acquired, therapy-induced resistance. Targeted therapies have varied success in addressing metastatic disease, due to their ability to penetrate certain physiological compartments. This paper considers an evolutionary cancer model that incorporates tumor cell growth, mutation and compartmental migration and leverages recent results on the optimal control of monotone and convex systems to synthesize switching treatment strategies where a single drug or a predetermined combination of drugs is used at a given time. The need for switching is motivated by clinical considerations such as... (More)

The clinical success of targeted cancer therapies is limited by the emergence of drug resistance often due to pre-existing tumor genetic heterogeneity and acquired, therapy-induced resistance. Targeted therapies have varied success in addressing metastatic disease, due to their ability to penetrate certain physiological compartments. This paper considers an evolutionary cancer model that incorporates tumor cell growth, mutation and compartmental migration and leverages recent results on the optimal control of monotone and convex systems to synthesize switching treatment strategies where a single drug or a predetermined combination of drugs is used at a given time. The need for switching is motivated by clinical considerations such as the limited effectiveness of any single targeted therapy against multiple resistance mechanisms arising in a single patient and the inability to design drug combinations at effective doses due to toxicity constraints. An optimal and clinically feasible switching therapy is obtained as the solution of a convex optimization problem that exploits the diagonally-dominant structure of the model. We demonstrate that this method yields an effective strategy in mitigating disease evolution in the presence of imperfect drug penetration in two compartments on an experimentally identified model of anaplastic lymphoma kinase (ALK)-rearranged lung carcinoma.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2016 IEEE 55th Conference on Decision and Control, CDC 2016
pages
7 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
55th IEEE Conference on Decision and Control 2016
external identifiers
  • scopus:85010716357
ISBN
9781509018376
DOI
10.1109/CDC.2016.7798636
language
English
LU publication?
yes
id
7ee12aa3-3455-41e2-a859-42f0f96f0cae
date added to LUP
2017-01-10 11:44:02
date last changed
2017-03-28 09:59:02
@inproceedings{7ee12aa3-3455-41e2-a859-42f0f96f0cae,
  abstract     = {<p>The clinical success of targeted cancer therapies is limited by the emergence of drug resistance often due to pre-existing tumor genetic heterogeneity and acquired, therapy-induced resistance. Targeted therapies have varied success in addressing metastatic disease, due to their ability to penetrate certain physiological compartments. This paper considers an evolutionary cancer model that incorporates tumor cell growth, mutation and compartmental migration and leverages recent results on the optimal control of monotone and convex systems to synthesize switching treatment strategies where a single drug or a predetermined combination of drugs is used at a given time. The need for switching is motivated by clinical considerations such as the limited effectiveness of any single targeted therapy against multiple resistance mechanisms arising in a single patient and the inability to design drug combinations at effective doses due to toxicity constraints. An optimal and clinically feasible switching therapy is obtained as the solution of a convex optimization problem that exploits the diagonally-dominant structure of the model. We demonstrate that this method yields an effective strategy in mitigating disease evolution in the presence of imperfect drug penetration in two compartments on an experimentally identified model of anaplastic lymphoma kinase (ALK)-rearranged lung carcinoma.</p>},
  author       = {Giordano, Giulia and Rantzer, Anders and Jonsson, Vanessa D.},
  booktitle    = {2016 IEEE 55th Conference on Decision and Control, CDC 2016},
  isbn         = {9781509018376},
  language     = {eng},
  month        = {12},
  pages        = {2494--2500},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {A convex optimization approach to cancer treatment to address tumor heterogeneity and imperfect drug penetration in physiological compartments},
  url          = {http://dx.doi.org/10.1109/CDC.2016.7798636},
  year         = {2016},
}