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<p>Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, cell cycle phase, area and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.</p>

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A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Pragyesh Dixit,
  • Ilyas Djafer-Cherif,
  • Saumil Shah,
  • Karolina Drabik,
  • Arne Traulsen,
  • Bartlomiej Waclaw

摘要

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, cell cycle phase, area and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.