Radiation and PD-(L)1 treatment combinations : immune response and dose optimization via a predictive systems model
BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.
METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.
RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.
CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
Journal for immunotherapy of cancer - 6(2018), 1 vom: 27. Feb., Seite 17 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kosinsky, Yuri [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.10.2019 Date Revised 12.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s40425-018-0327-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM281447284 |
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| 041 | |a eng | ||
| 100 | 1 | |a Kosinsky, Yuri |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiation and PD-(L)1 treatment combinations |b immune response and dose optimization via a predictive systems model |
| 264 | 1 | |c 2018 | |
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| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies | ||
| 520 | |a METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx | ||
| 520 | |a RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1 | ||
| 520 | |a CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a CT26 tumors | |
| 650 | 4 | |a Cancer immunity cycle | |
| 650 | 4 | |a Checkpoint inhibitors | |
| 650 | 4 | |a Dose sequencing and scheduling | |
| 650 | 4 | |a Immuno-activation | |
| 650 | 4 | |a Immuno-oncology (IO) | |
| 650 | 4 | |a Immuno-suppression | |
| 650 | 4 | |a PD-1 | |
| 650 | 4 | |a PD-L1 | |
| 650 | 4 | |a Quantitative systems pharmacology | |
| 650 | 4 | |a Radiation therapy | |
| 650 | 7 | |a Antineoplastic Agents, Immunological |2 NLM | |
| 650 | 7 | |a B7-H1 Antigen |2 NLM | |
| 650 | 7 | |a Cd274 protein, mouse |2 NLM | |
| 700 | 1 | |a Dovedi, Simon J |e verfasserin |4 aut | |
| 700 | 1 | |a Peskov, Kirill |e verfasserin |4 aut | |
| 700 | 1 | |a Voronova, Veronika |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Lulu |e verfasserin |4 aut | |
| 700 | 1 | |a Tomkinson, Helen |e verfasserin |4 aut | |
| 700 | 1 | |a Al-Huniti, Nidal |e verfasserin |4 aut | |
| 700 | 1 | |a Stanski, Donald R |e verfasserin |4 aut | |
| 700 | 1 | |a Helmlinger, Gabriel |e verfasserin |4 aut | |
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