ArticleEBioMedicine2021
Quantifying cell cycle-dependent drug sensitivities in cancer using a high throughput synchronisation and screening approach.
Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
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- Computational decoding of cell-cycle phase effects on cancer hallmarks across breast cancer subtypes.Breast cancer research : BCR · 2025Article
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
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- Molecular mechanisms altering cell identity in cancer.Oncogene · 2025Review
- Uncovering cell cycle-dependent effects on cell survival in near-infrared photoimmunotherapy.Experimental cell research · 2025Article
- Reversible and effective cell cycle synchronization method for studying stage-specific processes.Life science alliance · 2025Article
- Reversible and effective cell cycle synchronization method for studying stage-specific investigations.bioRxiv : the preprint server for biology · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.PLoS computational biology · 2024Article
- Cardiac glycoside ouabain efficiently targets leukemic stem cell apoptotic machinery independent of cell differentiation status.Cell communication and signaling : CCS · 2023Article
- Cyclohexene oxide CA, a derivative of zeylenone, exhibits anti-cancer activity in glioblastoma by inducing G0/G1 phase arrest through interference with EZH2.Frontiers in pharmacology · 2023Article
- A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity.Communications biology · 2022Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundChemotherapy and targeted agent anti-cancer efficacy is largely dependent on the proliferative state of tumours, as exemplified by agents that target DNA synthesis/replication or mitosis. As a result, cell cycle specificities of a number of cancer drugs are well known. However, they are yet to be described in a quantifiable manner.
methodsA scalable cell synchronisation protocol used to screen a library of 235 anti-cancer compounds exposed over six hours in G1 or S/G2 accumulated AsPC-1 cells to generate a cell cycle specificity (CCS) score.
findingsThe synchronisation method was associated with reduced method-related cytotoxicity compared to nocodazole, delivering sufficient cell cycle purity and cell numbers to run high-throughput drug library screens. Compounds were identified with G1 and S/G2-associated specificities that, overall, functionally matched with a compound's target/mechanism of action. This annotation was used to describe a synergistic schedule using the CDK4/6 inhibitor, palbociclib, prior to gemcitabine/AZD6738 as well as describe the correlation between the CCS score and published synergistic/antagonistic drug schedules.
interpretationThis is the first highly quantitative description of cell cycle-dependent drug sensitivities that utilised a tractable and tolerated method with potential uses outside the present study. Drug treatments such as those shown to be G1 or S/G2 associated may benefit from scheduling considerations such as after CDK4/6 inhibitors and being first in drug sequences respectively.
fundingCancer Research UK (CRUK) Institute core grants C14303/A17197 and C9545/A29580. The Li Ka Shing Centre where this work was performed was generously funded by CK Hutchison Holdings Limited, the University of Cambridge, CRUK, The Atlantic Philanthropies and others.
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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.