Reinforcement learning-based design of sequential drug treatments targeting evolving tumor landscapes using SequenTx

Machine Learning


  • Parreno, V. et al. Transient loss of polycomb components triggers epigenetic cancer fate. nature 629688–696 (2024).

    Article Google Scholar

  • Brown, R., Curry, E., Magnani, L., Wilhelm-Benartzi, CS, Borley, J. Epigenetic states and acquired drug resistance in cancer. nut. cancer pastor 14747–753 (2014).

    Article Google Scholar

  • Aduri, AK et al. Predict cellular responses to perturbations across a variety of state contexts. preprint in BioRxiv https://doi.org/10.1101/2025.06.26.661135 (2025).

  • Zhao, S. et al. SToFM: A multiscale foundational model for spatial transcriptomics. Preprint available at http://arxiv.org/abs/2507.11588 (2025).

  • Bunne, C. et al. How to build virtual cells using artificial intelligence: Priorities and opportunities. cell 1877045–7063 (2024).

    Article Google Scholar

  • Salvador Barbero, B. Other CDK4/6 inhibitors impede recovery from cytotoxic chemotherapy in pancreatic adenocarcinoma. cancer cells 37340–353.e346 (2020).

    Article Google Scholar

  • Fang, Y. et al. Sequential therapy with PARP and WEE1 inhibitors minimizes toxicity while maintaining efficacy. cancer cells 35851–867.e857 (2019).

    Article Google Scholar

  • Lee, MJ et al. Sequential application of anticancer drugs promotes cell death by rewiring apoptotic signaling networks. cell 149780–794 (2012).

    Article Google Scholar

  • Goldman, A. et al. Transiently sequenced anticancer drugs overcome adaptive resistance by targeting vulnerable phenotypic transformations induced by chemotherapy. nut. common. 66139 (2015).

    Article Google Scholar

  • Liu, Z. et al. The proteomic and phosphoproteomic landscape of KRAS-mutant cancers identifies combination therapies. Mol. cell 814076–4090.e4078 (2021).

    Article Google Scholar

  • Wang, L., Lankhorst, L. & Bernards, R. Harnessing aging for the treatment of cancer. nut. cancer pastor twenty two340–355 (2022).

    Article Google Scholar

  • Wang, C. et al. Induction and exploitation of vulnerabilities in liver cancer treatment. nature 574268–272 (2019).

    Article Google Scholar

  • Lee, F. et al. Inhibiting methionine catabolism induces senescence and creates vulnerability to GSK3 inhibition in liver cancer. nut. cancer 5131–146 (2024).

  • Silver, D. et al. Master Go using deep neural networks and tree search. nature 529484–489 (2016).

    Article Google Scholar

  • Baek, M. et al. Accurate prediction of protein structure and interactions using three-track neural networks. science 373871–876 (2021).

    Article Google Scholar

  • Wang, G. et al. Optimizing glycemic control in type 2 diabetes with reinforcement learning: A proof-of-concept study. nut. medicine. 292633–2642 (2023).

    Article Google Scholar

  • Yara, A. et al. Optimize risk-based breast cancer screening policies using reinforcement learning. nut. medicine. 28136–143 (2022).

    Article Google Scholar

  • Zhang, H. et al. Algorithms for optimized mRNA design improve stability and immunogenicity. nature 621396–403 (2023).

    Article Google Scholar

  • Weaver, DT, King, ES, Maltas, J. & Scott, JG Reinforcement learning provides optimal treatment strategies to limit antibiotic resistance. National Academy of Procedures. Science. united states of america 121e2303165121 (2024).

    Article Google Scholar

  • Jo, K., Sung, I., Lee, D., Jang, H. & Kim, S. Inferring transcriptome cell states and transitions from time-series transcriptome data alone. Science. Member of Parliament 1112566 (2021).

    Article Google Scholar

  • Rockne, R.C. et al. State transition analysis of time-series gene expression identifies key points that predict the development of acute myeloid leukemia. cancer research institute 803157–3169 (2020).

    Article Google Scholar

  • Mnih, V. et al. Human-level control with deep reinforcement learning. nature 518529–533 (2015).

    Article Google Scholar

  • Lotfollahi, M. et al. Prediction of cellular responses to complex perturbations in high-throughput screening. Mol. system. Biol. 19e11517 (2023).

    Article Google Scholar

  • Huang, W. & Liu, H. Prediction of cellular responses of single cells to perturbations using cycle-consistent learning. bioinformatics 40i462–i470 (2024).

    Article Google Scholar

  • Piran, Z., Cohen, N., Hoshen, Y., Nitzan, M. Disentangling single-cell data with biolord. nut. biotechnology. 421678–1683 (2024).

    Article Google Scholar

  • Qi, X et al. Predicting transcriptional responses to novel chemical perturbations using deep generative models for drug discovery. nut. common. 159256 (2024).

    Article Google Scholar

  • Lotfollahi, M., Wolf, FA, Theis, FJ scGen predicts single cell perturbation responses. nut. method 16715–721 (2019).

    Article Google Scholar

  • Szalai, B. et al. Characterizing cell death and proliferation in perturbed transcriptomics data – from confounders to effective prediction. Nucleic acid research institute 4710010–10026 (2019).

    Article Google Scholar

  • Patwardhan, GA et al. Treatment schedule influences the evolution of drug resistance in a heterogeneous cancer cell population. NPJ breast cancer 760 (2021).

    Article Google Scholar

  • Johnson, TI et al. Quantifying cell cycle-dependent drug sensitivity in cancer using a high-throughput synchronization and screening approach. E-biomedicine 68103396 (2021).

    Article Google Scholar

  • Vijayaraghavalu, S., Dermawan, JK, Cheriyath, V. & Labhasetwar, V. Highly synergistic effects of sequential treatment with epigenetic drugs and anticancer drugs to overcome drug resistance in breast cancer cells are mediated through activation of p21 gene expression leading to G2/M cycle arrest. Mol. pharmacy. 10337–352 (2013).

    Article Google Scholar

  • Easwaran, H., Tsai, H.-C. & Baylin, SB Cancer epigenetics: tumor heterogeneity, stem-like plasticity, and drug resistance. Mol. cell 54716–727 (2014).

    Article Google Scholar

  • Bloom, SI, Islam, MT, Rezniewski, LA and Donato, AJ Mechanisms and consequences of endothelial cell aging. nut. Pastor Cardiol. 2038–51 (2023).

    Article Google Scholar

  • Sutton, RS, Barth, AG Reinforcement Learning: Introduction (MIT Press, 2018).

  • Subramanian, A. et al. Next generation connectivity map: L1000 platform and first 1,000,000 profiles. cell 1711437–1452. e1417 (2017).

    Article Google Scholar

  • Barrett, T. et al. NCBI GEO: Functional Genomics Data Set Archive – Updated. Nucleic acid research institute 41D991–D995 (2012).

    Article Google Scholar

  • RDKit: Open source cheminformatics; https://www.rdkit.org

  • Enache, OM et al. GCTx format and cmap {Py, R, M, J} package: resources for optimized storage and unified traversal of annotated dense matrices. bioinformatics 351427–1429 (2019).

    Article Google Scholar

  • Kingma, DP & Ba, J. Adam: Stochastic optimization techniques. Preprint available at http://arxiv.org/abs/1412.6980 (2014).

  • Prechelt, L. Neural networks: trading tips 55–69 (Springer, 2002).

  • Paszke, A. et al. PyTorch: An imperative-style high-performance deep learning library. in 33rd Neural Information Processing Systems Conference (NeurIPS 2019) https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf (2019).

  • Seashore-Ludlow, B. et al. Harnessing the connectivity of large-scale small molecule sensitivity datasets. Discovery of cancer. 51210–1223 (2015).

    Article Google Scholar

  • Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. learn. resolution 122825–2830 (2011).

    MathSciNet Google Scholar

  • Brownlee, J. A gentle introduction to rectified linear units (ReLU). Mach. learn. mastery 6 (2019).

  • Yang, L., Liu, S., Tsoka, S., Papageorgiou, LG Mathematical programming for piecewise linear regression analysis. expert system. application 44156–167 (2016).

    Article Google Scholar

  • Wagenmaker, E.-J. & Farrell, S. AIC model selection using Akaike weights. Saikon. Bull. pastor 11192–196 (2004).

    Article Google Scholar

  • Wu, T. et al. clusterProfiler 4.0: A general-purpose enrichment tool for interpreting omics data. innovation 2100141 (2021).

  • Wishart, DS et al. DrugBank 5.0: Major update to the 2018 DrugBank database. Nucleic acid research institute 46D1074–D1082 (2018).

    Article Google Scholar

  • Virtanen, P. et al. SciPy 1.0: Basic algorithms for scientific computing in Python. nut. method 17261–272 (2020).

    Article Google Scholar

  • Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB Net J. 1710–12 (2011).

    Article Google Scholar

  • Dobin, A. & Gingeras, TR Mapping RNA-seq reads using STAR. car. Protok. Bioinform. 5111.14. November 14th – November 14th. 19 (2015).

    Article Google Scholar

  • Li, B. & Dewey, CN RSEM: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC bioinf. 12323 (2011).

    Article Google Scholar

  • Love, MI, Huber, W. & Anders, S. Reasonable estimation of fold change and variance of RNA-seq data using DESeq2. Genome Biol. 15550 (2014).

    Article Google Scholar

  • Brockman, G. et al. OpenAI Gym. Preprint available at http://arxiv.org/abs/1606.01540 (2016).

  • Weng, J. et al. Tiansho: A highly modular deep reinforcement learning library. J. Mach. learn. resolution twenty three1–6 (2022).

    MathSciNet Google Scholar



  • Source link