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BCG-Mediated Bladder Cancer Immunotherapy: Identifying Determinants of Treatment Response Using a Calibrated Mathematical Model

Overview of attention for article published in PLOS ONE, February 2013
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Title
BCG-Mediated Bladder Cancer Immunotherapy: Identifying Determinants of Treatment Response Using a Calibrated Mathematical Model
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0056327
Pubmed ID
Authors

Cyrill A. Rentsch, Claire Biot, Joël R. Gsponer, Alexander Bachmann, Matthew L. Albert, Romulus Breban

Abstract

Intravesical Bacillus Calmette Guérin (BCG) immunotherapy is considered the standard of care for treatment of non-muscle invasive bladder cancer; however the treatment parameters were established empirically. In order to evaluate potential optimization of clinical parameters of BCG induction therapy, we constructed and queried a new mathematical model. Specifically, we assessed the impact of (1) duration between resection and the first instillation; (2) BCG dose; (3) indwelling time; and (4) treatment interval of induction therapy - using cure rate as the primary endpoint. Based on available clinical and in vitro experimental data, we constructed and parameterized a stochastic mathematical model describing the interactions between BCG, the immune system, the bladder mucosa and tumor cells. The primary endpoint of the model was the probability of tumor extinction following BCG induction therapy in patients with high risk for tumor recurrence. We theoretically demonstrate that extending the duration between the resection and the first BCG instillation negatively influences treatment outcome. Simulations of higher BCG doses and longer indwelling times both improved the probability of tumor extinction. A remarkable finding was that an inter-instillation interval two times longer than the seven-day interval used in the current standard of care would substantially improve treatment outcome. We provide insight into relevant clinical questions using a novel mathematical model of BCG immunotherapy. Our model predicts an altered regimen that may decrease side effects of treatment while improving response to therapy.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 18%
Student > Master 5 15%
Other 3 9%
Professor 3 9%
Student > Bachelor 3 9%
Other 10 29%
Unknown 4 12%
Readers by discipline Count As %
Medicine and Dentistry 10 29%
Agricultural and Biological Sciences 7 21%
Immunology and Microbiology 3 9%
Mathematics 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 5 15%
Unknown 5 15%