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Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation
SIMPLE SUMMARY: The intra-competition among tumor subpopulations is a promising target to modify and control the outgrowth of the resistant subpopulation. Adaptive therapy lives up to this principle well, but the gain of tumors with an aggressive resistant subpopulation is not superior to maximum to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582524/ https://www.ncbi.nlm.nih.gov/pubmed/34771426 http://dx.doi.org/10.3390/cancers13215262 |
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author | Wang, Jiali Zhang, Yixuan Liu, Xiaoquan Liu, Haochen |
author_facet | Wang, Jiali Zhang, Yixuan Liu, Xiaoquan Liu, Haochen |
author_sort | Wang, Jiali |
collection | PubMed |
description | SIMPLE SUMMARY: The intra-competition among tumor subpopulations is a promising target to modify and control the outgrowth of the resistant subpopulation. Adaptive therapy lives up to this principle well, but the gain of tumors with an aggressive resistant subpopulation is not superior to maximum tolerated dose therapy (MTD). How to integrate these two therapies to maximize the outcome? According to the model and system reachability, the ‘restore index’ is proposed to evaluate the timing of the transition from the treatment cycle of adaptive therapy to high-frequency administration, and to juggle the benefits of intra-competition and killing of the sensitive subpopulation. Based on the simulation and animal experiment, the effectiveness of this method in treating tumors with an aggressive resistant subpopulation has been confirmed. ABSTRACT: Adaptive therapy exploits the self-organization of tumor cells to delay the outgrowth of resistant subpopulations successfully. When the tumor has aggressive resistant subpopulations, the outcome of adaptive therapy was not superior to maximum tolerated dose therapy (MTD). To explore methods to improve the adaptive therapy’s performance of this case, the tumor system was constructed by osimertinib-sensitive and resistant cell lines and illustrated by the Lotka-Volterra model in this study. Restore index proposed to assess the system reachability can predict the duration of each treatment cycle. Then the threshold of the restore index was estimated to evaluate the timing of interrupting the treatment cycle and switching to high-frequency administration. The introduced reachability-based adaptive therapy and classic adaptive therapy were compared through simulation and animal experiments. The results suggested that reachability-based adaptive therapy showed advantages when the tumor has an aggressive resistant subpopulation. This study provides a feasible method for evaluating whether to continue the adaptive therapy treatment cycle or switch to high-frequency administration. This method improves the gain of adaptive therapy by taking into account the benefits of tumor intra-competition and the tumor control of killing sensitive subpopulation. |
format | Online Article Text |
id | pubmed-8582524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85825242021-11-12 Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation Wang, Jiali Zhang, Yixuan Liu, Xiaoquan Liu, Haochen Cancers (Basel) Article SIMPLE SUMMARY: The intra-competition among tumor subpopulations is a promising target to modify and control the outgrowth of the resistant subpopulation. Adaptive therapy lives up to this principle well, but the gain of tumors with an aggressive resistant subpopulation is not superior to maximum tolerated dose therapy (MTD). How to integrate these two therapies to maximize the outcome? According to the model and system reachability, the ‘restore index’ is proposed to evaluate the timing of the transition from the treatment cycle of adaptive therapy to high-frequency administration, and to juggle the benefits of intra-competition and killing of the sensitive subpopulation. Based on the simulation and animal experiment, the effectiveness of this method in treating tumors with an aggressive resistant subpopulation has been confirmed. ABSTRACT: Adaptive therapy exploits the self-organization of tumor cells to delay the outgrowth of resistant subpopulations successfully. When the tumor has aggressive resistant subpopulations, the outcome of adaptive therapy was not superior to maximum tolerated dose therapy (MTD). To explore methods to improve the adaptive therapy’s performance of this case, the tumor system was constructed by osimertinib-sensitive and resistant cell lines and illustrated by the Lotka-Volterra model in this study. Restore index proposed to assess the system reachability can predict the duration of each treatment cycle. Then the threshold of the restore index was estimated to evaluate the timing of interrupting the treatment cycle and switching to high-frequency administration. The introduced reachability-based adaptive therapy and classic adaptive therapy were compared through simulation and animal experiments. The results suggested that reachability-based adaptive therapy showed advantages when the tumor has an aggressive resistant subpopulation. This study provides a feasible method for evaluating whether to continue the adaptive therapy treatment cycle or switch to high-frequency administration. This method improves the gain of adaptive therapy by taking into account the benefits of tumor intra-competition and the tumor control of killing sensitive subpopulation. MDPI 2021-10-20 /pmc/articles/PMC8582524/ /pubmed/34771426 http://dx.doi.org/10.3390/cancers13215262 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Jiali Zhang, Yixuan Liu, Xiaoquan Liu, Haochen Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title | Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title_full | Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title_fullStr | Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title_full_unstemmed | Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title_short | Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation |
title_sort | optimizing adaptive therapy based on the reachability to tumor resistant subpopulation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582524/ https://www.ncbi.nlm.nih.gov/pubmed/34771426 http://dx.doi.org/10.3390/cancers13215262 |
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