{
  "id": 3752666,
  "title": "Systematic analysis of network-driven adaptive resistance to CDK4/6 and oestrogen receptor inhibition using meta-dynamic network modelling",
  "url": "https://urgent.news/2026/08/27/systematic-analysis-of-network-driven-adaptive-resistance-to-cdk4-6",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-08-27T00:00:00.000Z",
  "source": {
    "name": "eLife",
    "slug": "elife",
    "url": "https://elifesciences.org/articles/87710"
  },
  "original_language": "en",
  "account": "A significant challenge in cancer treatment arises from adaptive resistance, a phenomenon whereby targeted therapy elicits drug resistance due to the rewiring of protein signalling networks. This can happen even when the drug is continuously present, allowing cells to evade the drug's effects and continue to grow. The exact mechanisms linking molecular heterogeneity to adaptive resistance, specifically how heterogeneity impacts protein signalling dynamics, have not been thoroughly explored.\n\nIn a novel study, researchers have developed a modelling technique called Meta Dynamic Network (MDN) modelling to understand the relationship between heterogeneity, protein signalling dynamics, and adaptive resistance. This technique allows them to examine how heterogeneity influences the drug-response signalling dynamics of proteins that regulate early cell cycle progression. The results indicate that heterogeneity can significantly facilitate adaptive resistance associated with key cell cycle regulators.\n\nTo further investigate this relationship, the researchers determined the influence of heterogeneity at two levels: reaction coefficients and protein abundance. They found that reaction coefficients are a much stronger driver of adaptive resistance than protein abundance. By employing an ordinary differential equation framework, the team identified a range of subnetworks capable of driving adaptive resistance dynamics in the early cell cycle regulators.\n\nThe researchers validated their MDN modelling technique using single-cell dynamic data, confirming its effectiveness. Additionally, they compared the predicted resistance mechanisms with known CDK4/6 and oestrogen receptor inhibitor resistance mechanisms. The findings suggest that MDN modelling can be effectively used to predict network-level resistance mechanisms for new drugs and various protein signalling networks.",
  "summary": "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 drug-targeted protein activity to recover. This can occur in the continuous presence of the drug and enables cells to survive/grow. Simultaneously, molecular…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}