Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Barish, S., Ochs, M., Sontag, E., & Gevertz, J. Proc Natl Acad Sci USA, 114:E6277-E6286, 2017. ![Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper proposes a technique that combines experimental data, mathematical modeling, and statistical analyses for identifying optimal treatment protocols that are robust with respect to individual variability. Experimental data from a small sample population is amplified using bootstrapping to obtain a large number of virtual populations that statistically match the expected heterogeneity. Alternative therapies chosen from among a set of clinically-realizable protocols are then compared and scored according to coverage. As proof of concept, the method is used to evaluate a treatment with oncolytic viruses and dendritic cell vaccines in a mouse model of melanoma. The analysis shows that while every scheduling variant of an experimentally-utilized treatment protocol is fragile (non-robust), there is an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.
@ARTICLE{gevertz2017bootstrap,
AUTHOR = {S. Barish and M.F. Ochs and E.D. Sontag and J.L. Gevertz},
JOURNAL = {Proc Natl Acad Sci USA},
TITLE = {Evaluating optimal therapy robustness by virtual
expansion of a sample population, with a case study in cancer
immunotherapy},
YEAR = {2017},
OPTMONTH = {},
OPTNOTE = {},
OPTNUMBER = {},
PAGES = {E6277-E6286},
VOLUME = {114},
KEYWORDS = {cancer, oncolytic therapy, immunotherapy,
optimal therapy},
URL = {http://www.pnas.org/content/early/2017/07/14/1703355114.abstract},
PDF = {../../FTPDIR/barish_ochs_sontag_gevertz_optimal_therapy_robustness_pnas2017.pdf},
ABSTRACT = {This paper proposes a technique that combines
experimental data, mathematical modeling, and statistical analyses
for identifying optimal treatment protocols that are robust with
respect to individual variability. Experimental data from a small
sample population is amplified using bootstrapping to obtain a large
number of virtual populations that statistically match the expected
heterogeneity. Alternative therapies chosen from among a set of
clinically-realizable protocols are then compared and scored
according to coverage. As proof of concept, the method is used to
evaluate a treatment with oncolytic viruses and dendritic cell
vaccines in a mouse model of melanoma. The analysis shows that while
every scheduling variant of an experimentally-utilized treatment
protocol is fragile (non-robust), there is an alternative region of
dosing space (lower oncolytic virus dose, higher dendritic cell dose)
for which a robust optimal protocol exists.},
DOI = {10.1073/pnas.1703355114}
}
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