Cargando…
Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes
BACKGROUND: Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239688/ https://www.ncbi.nlm.nih.gov/pubmed/35762577 http://dx.doi.org/10.7554/eLife.76284 |
_version_ | 1784737361207230464 |
---|---|
author | Zhang, Jingsong Cunningham, Jessica Brown, Joel Gatenby, Robert |
author_facet | Zhang, Jingsong Cunningham, Jessica Brown, Joel Gatenby, Robert |
author_sort | Zhang, Jingsong |
collection | PubMed |
description | BACKGROUND: Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance. METHODS: In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing. RESULTS: 17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53–70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20–0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (α(RS)/α(SR)) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for α(RS)/α(SR,) cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction. CONCLUSIONS: Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes. FUNDING: Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network). |
format | Online Article Text |
id | pubmed-9239688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-92396882022-06-29 Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes Zhang, Jingsong Cunningham, Jessica Brown, Joel Gatenby, Robert eLife Evolutionary Biology BACKGROUND: Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance. METHODS: In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing. RESULTS: 17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53–70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20–0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (α(RS)/α(SR)) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for α(RS)/α(SR,) cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction. CONCLUSIONS: Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes. FUNDING: Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network). eLife Sciences Publications, Ltd 2022-06-28 /pmc/articles/PMC9239688/ /pubmed/35762577 http://dx.doi.org/10.7554/eLife.76284 Text en © 2022, Zhang et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Evolutionary Biology Zhang, Jingsong Cunningham, Jessica Brown, Joel Gatenby, Robert Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title | Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title_full | Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title_fullStr | Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title_full_unstemmed | Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title_short | Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
title_sort | evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes |
topic | Evolutionary Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239688/ https://www.ncbi.nlm.nih.gov/pubmed/35762577 http://dx.doi.org/10.7554/eLife.76284 |
work_keys_str_mv | AT zhangjingsong evolutionbasedmathematicalmodelssignificantlyprolongresponsetoabirateroneinmetastaticcastrateresistantprostatecancerandidentifystrategiestofurtherimproveoutcomes AT cunninghamjessica evolutionbasedmathematicalmodelssignificantlyprolongresponsetoabirateroneinmetastaticcastrateresistantprostatecancerandidentifystrategiestofurtherimproveoutcomes AT brownjoel evolutionbasedmathematicalmodelssignificantlyprolongresponsetoabirateroneinmetastaticcastrateresistantprostatecancerandidentifystrategiestofurtherimproveoutcomes AT gatenbyrobert evolutionbasedmathematicalmodelssignificantlyprolongresponsetoabirateroneinmetastaticcastrateresistantprostatecancerandidentifystrategiestofurtherimproveoutcomes |