High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study
Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/16/4033 |
| _version_ | 1797410979221340160 |
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| author | William Meade Allison Weber Tin Phan Emily Hampston Laura Figueroa Resa John Nagy Yang Kuang |
| author_facet | William Meade Allison Weber Tin Phan Emily Hampston Laura Figueroa Resa John Nagy Yang Kuang |
| author_sort | William Meade |
| collection | DOAJ |
| description | Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer. |
| first_indexed | 2024-03-09T04:38:21Z |
| format | Article |
| id | doaj.art-af72ece4c9d841e094ebb5419b384dd9 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T04:38:21Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-af72ece4c9d841e094ebb5419b384dd92023-12-03T13:25:39ZengMDPI AGCancers2072-66942022-08-011416403310.3390/cancers14164033High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling StudyWilliam Meade0Allison Weber1Tin Phan2Emily Hampston3Laura Figueroa Resa4John Nagy5Yang Kuang6School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USASchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USATheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USADepartment of Mathematics, State University of New York, Buffalo, NY 14260, USASchool of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USASchool of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USASchool of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85281, USAProstate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.https://www.mdpi.com/2072-6694/14/16/4033mechanistic model of prostate cancerpredictive modelingevolutionary cell quota frameworkadaptive cancer managementdynamic indicator of treatment failure |
| spellingShingle | William Meade Allison Weber Tin Phan Emily Hampston Laura Figueroa Resa John Nagy Yang Kuang High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study Cancers mechanistic model of prostate cancer predictive modeling evolutionary cell quota framework adaptive cancer management dynamic indicator of treatment failure |
| title | High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study |
| title_full | High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study |
| title_fullStr | High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study |
| title_full_unstemmed | High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study |
| title_short | High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study |
| title_sort | high accuracy indicators of androgen suppression therapy failure for prostate cancer a modeling study |
| topic | mechanistic model of prostate cancer predictive modeling evolutionary cell quota framework adaptive cancer management dynamic indicator of treatment failure |
| url | https://www.mdpi.com/2072-6694/14/16/4033 |
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