ArticleEBioMedicine2019
Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed, 100 citations in OpenAlex.
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- Mining nucleic acid "omics" to boost liquid biopsy in cancer.Cell reports. Medicine · 2024Review
- To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.Cell systems · 2024Article
- Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.Cancer research · 2024Article
- Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies.NPJ systems biology and applications · 2024Article
- Modeling Melanoma Heterogeneity In Vitro: Redox, Resistance and Pigmentation Profiles.Antioxidants (Basel, Switzerland) · 2024Article
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Corrections and comments
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundMelanoma is a heterogeneous tumour, but the impact of this heterogeneity upon therapeutic response is not well understood.
methodsSingle cell mRNA analysis was used to define the transcriptional heterogeneity of melanoma and its dynamic response to BRAF inhibitor therapy and treatment holidays. Discrete transcriptional states were defined in cell lines and melanoma patient specimens that predicted initial sensitivity to BRAF inhibition and the potential for effective re-challenge following resistance. A mathematical model was developed to maintain competition between the drug-sensitive and resistant states, which was validated in vivo.
findingsOur analyses showed melanoma cell lines and patient specimens to be composed of >3 transcriptionally distinct states. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy and drug holidays. Transcriptional state composition predicted for therapy response. The differences in fitness between the different transcriptional states were leveraged to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation demonstrated that the personalized adaptive dosing schedules outperformed continuous or fixed intermittent BRAF inhibitor schedules.
interpretationOur study provides the first evidence that transcriptional heterogeneity at the single cell level predicts for initial BRAF inhibitor sensitivity. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules can delay the time to resistance.
fundingThis work was funded by the National Institutes of Health. The funder played no role in assembly of the manuscript.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.