Systematic Analysis of Network-driven Adaptive Resistance to CDK4/6 and Estrogen Receptor Inhibition using Meta-Dynamic Network Modelling Article Swipe
Anthony Hart
,
Sung‐Young Shin
,
Lan K. Nguyen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.87710.1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.87710.1
Drug resistance inevitably emerges during the treatment of cancer by targeted therapy. Adaptive resistance is a major form of drug resistance, wherein the rewiring of protein signalling networks in response to drug perturbation allows the drug-targeted protein’s activity to recover, despite the continuous presence of the drug, enabling the cells to survive/grow. Simultaneously, molecular heterogeneity enables the selection of drug-resistant cancer clones that can survive an initial drug insult, proliferate, and eventually cause disease relapse. Despite their importance, the link between heterogeneity and adaptive resistance, specifically how heterogeneity influences protein signalling dynamics to drive adaptive resistance, remains poorly understood. Here, we have explored the relationship between heterogeneity, protein signalling dynamics and adaptive resistance through the development of a novel modelling technique coined Meta Dynamic Network (MDN) modelling. We use MDN modelling to characterise how heterogeneity influences the drug-response signalling dynamics of the proteins that regulate early cell cycle progression and demonstrate that heterogeneity can robustly facilitate adaptive resistance associated dynamics for key cell cycle regulators. We determined the influence of heterogeneity at the level of both protein interactions and protein expression and show that protein interactions are a much stronger driver of adaptive resistance. Owing to the mechanistic nature of the underpinning ODE framework, we then identified a full spectrum of subnetworks that drive adaptive resistance dynamics in the key early cell cycle regulators. Finally, we show that single-cell dynamic data supports the validity of our MDN modelling technique and a comparison between our predicted resistance mechanisms and known CDK4/6 and Estrogen Receptor inhibitor resistance mechanisms suggests MDN can be deployed to robustly predict network-level resistance mechanisms for novel drugs and additional protein signalling networks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.87710.1
- OA Status
- gold
- References
- 91
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- 10
- OpenAlex ID
- https://openalex.org/W4386369243
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https://openalex.org/W4386369243Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.87710.1Digital Object Identifier
- Title
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Systematic Analysis of Network-driven Adaptive Resistance to CDK4/6 and Estrogen Receptor Inhibition using Meta-Dynamic Network ModellingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-01Full publication date if available
- Authors
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Anthony Hart, Sung‐Young Shin, Lan K. NguyenList of authors in order
- Landing page
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https://doi.org/10.7554/elife.87710.1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.7554/elife.87710.1Direct OA link when available
- Concepts
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Biology, Drug resistance, Signalling, Adaptive response, Systems biology, Computational biology, Cell biology, GeneticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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91Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.remains | 97 |
| abstract_inverted_index.survive | 65 |
| abstract_inverted_index.through | 114 |
| abstract_inverted_index.wherein | 22 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Adaptive | 13 |
| abstract_inverted_index.Estrogen | 252 |
| abstract_inverted_index.Finally, | 225 |
| abstract_inverted_index.Receptor | 253 |
| abstract_inverted_index.activity | 38 |
| abstract_inverted_index.adaptive | 84, 95, 112, 157, 193, 215 |
| abstract_inverted_index.deployed | 261 |
| abstract_inverted_index.dynamics | 92, 110, 140, 160, 217 |
| abstract_inverted_index.enabling | 48 |
| abstract_inverted_index.explored | 103 |
| abstract_inverted_index.networks | 28 |
| abstract_inverted_index.presence | 44 |
| abstract_inverted_index.proteins | 143 |
| abstract_inverted_index.recover, | 40 |
| abstract_inverted_index.regulate | 145 |
| abstract_inverted_index.relapse. | 75 |
| abstract_inverted_index.response | 30 |
| abstract_inverted_index.rewiring | 24 |
| abstract_inverted_index.robustly | 155, 263 |
| abstract_inverted_index.spectrum | 210 |
| abstract_inverted_index.stronger | 190 |
| abstract_inverted_index.suggests | 257 |
| abstract_inverted_index.supports | 232 |
| abstract_inverted_index.targeted | 11 |
| abstract_inverted_index.therapy. | 12 |
| abstract_inverted_index.validity | 234 |
| abstract_inverted_index.influence | 169 |
| abstract_inverted_index.inhibitor | 254 |
| abstract_inverted_index.modelling | 120, 131, 238 |
| abstract_inverted_index.molecular | 54 |
| abstract_inverted_index.networks. | 275 |
| abstract_inverted_index.predicted | 245 |
| abstract_inverted_index.selection | 58 |
| abstract_inverted_index.technique | 121, 239 |
| abstract_inverted_index.treatment | 7 |
| abstract_inverted_index.additional | 272 |
| abstract_inverted_index.associated | 159 |
| abstract_inverted_index.comparison | 242 |
| abstract_inverted_index.continuous | 43 |
| abstract_inverted_index.determined | 167 |
| abstract_inverted_index.eventually | 72 |
| abstract_inverted_index.expression | 181 |
| abstract_inverted_index.facilitate | 156 |
| abstract_inverted_index.framework, | 204 |
| abstract_inverted_index.identified | 207 |
| abstract_inverted_index.inevitably | 3 |
| abstract_inverted_index.influences | 89, 136 |
| abstract_inverted_index.mechanisms | 247, 256, 267 |
| abstract_inverted_index.modelling. | 127 |
| abstract_inverted_index.resistance | 2, 14, 113, 158, 216, 246, 255, 266 |
| abstract_inverted_index.signalling | 27, 91, 109, 139, 274 |
| abstract_inverted_index.demonstrate | 151 |
| abstract_inverted_index.development | 116 |
| abstract_inverted_index.importance, | 78 |
| abstract_inverted_index.mechanistic | 198 |
| abstract_inverted_index.progression | 149 |
| abstract_inverted_index.protein’s | 37 |
| abstract_inverted_index.regulators. | 165, 224 |
| abstract_inverted_index.resistance, | 21, 85, 96 |
| abstract_inverted_index.resistance. | 194 |
| abstract_inverted_index.single-cell | 229 |
| abstract_inverted_index.subnetworks | 212 |
| abstract_inverted_index.understood. | 99 |
| abstract_inverted_index.characterise | 133 |
| abstract_inverted_index.interactions | 178, 186 |
| abstract_inverted_index.perturbation | 33 |
| abstract_inverted_index.proliferate, | 70 |
| abstract_inverted_index.relationship | 105 |
| abstract_inverted_index.specifically | 86 |
| abstract_inverted_index.underpinning | 202 |
| abstract_inverted_index.drug-response | 138 |
| abstract_inverted_index.drug-targeted | 36 |
| abstract_inverted_index.heterogeneity | 55, 82, 88, 135, 153, 171 |
| abstract_inverted_index.network-level | 265 |
| abstract_inverted_index.survive/grow. | 52 |
| abstract_inverted_index.drug-resistant | 60 |
| abstract_inverted_index.heterogeneity, | 107 |
| abstract_inverted_index.Simultaneously, | 53 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.34096845 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |