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<p>Tumour cells evolve dynamically during cancer progression and treatment. Characterizing such complex cellular dynamics and accordingly developing sequential therapy that targets the evolving landscape of the tumour present the fundamental requirements for tumour therapy. Here we introduce SequenTx, a proof-of-concept artificial-intelligence-virtual-cell-inspired computational framework that integrates a tumour cell model with reinforcement learning to design sequential drug treatments across diverse drugs and tumour types considering dynamic-therapy-induced transitions in tumour cellular states with transcriptome-based therapeutic perturbation data. Large-scale in vitro experiments across various solid tumour types confirmed the effectiveness of SequenTx, achieving a 33% success rate (34 out of 102). Extending these findings in vivo, bromodomain and extra-terminal motif inhibitor pretreatment enhanced oxaliplatin sensitivity in a melanoma xenograft model. Mechanistic analysis via transcriptome data indicated that the initially administered drugs induced continuous alterations in the cancer cell transcriptome, leading to enhanced responses to subsequent treatments to achieve synergy. Additionally, SequenTx revealed a rationale for sequential therapy involving epigenetic inhibitors followed by other drugs, which unlocks the full therapeutic potential of these epigenetic drugs, thereby enhancing their clinical feasibility in cancer treatment. Overall, SequenTx provides a proof-of-concept framework, inspired by the artificial intelligence virtual cell paradigm, to rationally design effective sequential drug treatments for tumours, offering computational insights into tumour therapy to overcome transcription-dependent tumour evolution, resistance and heterogeneity.</p>

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Reinforcement learning-based design of sequential drug treatment targeting the evolving tumour landscape with SequenTx

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Xiaohan Chen,
  • Yue Deng,
  • Xingbo Yang,
  • Ying Zhang,
  • Yuting Huang,
  • Shuxian Li,
  • Hong Yang,
  • Qiongyu Shi,
  • Siqi Wu,
  • Shaoqi Chen,
  • Xuan Jin,
  • Feiyang Xing,
  • Chen Zhang,
  • Yicheng Gao,
  • Degang Yang,
  • Guohui Chuai,
  • Jian Ding,
  • Xun Huang,
  • Qi Liu

摘要

Tumour cells evolve dynamically during cancer progression and treatment. Characterizing such complex cellular dynamics and accordingly developing sequential therapy that targets the evolving landscape of the tumour present the fundamental requirements for tumour therapy. Here we introduce SequenTx, a proof-of-concept artificial-intelligence-virtual-cell-inspired computational framework that integrates a tumour cell model with reinforcement learning to design sequential drug treatments across diverse drugs and tumour types considering dynamic-therapy-induced transitions in tumour cellular states with transcriptome-based therapeutic perturbation data. Large-scale in vitro experiments across various solid tumour types confirmed the effectiveness of SequenTx, achieving a 33% success rate (34 out of 102). Extending these findings in vivo, bromodomain and extra-terminal motif inhibitor pretreatment enhanced oxaliplatin sensitivity in a melanoma xenograft model. Mechanistic analysis via transcriptome data indicated that the initially administered drugs induced continuous alterations in the cancer cell transcriptome, leading to enhanced responses to subsequent treatments to achieve synergy. Additionally, SequenTx revealed a rationale for sequential therapy involving epigenetic inhibitors followed by other drugs, which unlocks the full therapeutic potential of these epigenetic drugs, thereby enhancing their clinical feasibility in cancer treatment. Overall, SequenTx provides a proof-of-concept framework, inspired by the artificial intelligence virtual cell paradigm, to rationally design effective sequential drug treatments for tumours, offering computational insights into tumour therapy to overcome transcription-dependent tumour evolution, resistance and heterogeneity.