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@ARTICLE{Patil:306596,
      author       = {S. Patil$^*$ and A. Ahmed and Y. Viossat and R. Noble},
      title        = {{P}reventing evolutionary rescue in cancer using two-strike
                      therapy.},
      journal      = {Genetics},
      volume       = {nn},
      issn         = {0016-6731},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-02635},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:B086# / epub},
      abstract     = {First-line cancer treatment frequently fails due to
                      initially rare therapeutic resistance. An important clinical
                      question is then how to schedule subsequent treatments to
                      maximize the probability of tumour eradication. Here, we
                      provide a theoretical solution to this problem by using
                      mathematical analysis and extensive stochastic simulations
                      within the framework of evolutionary rescue theory to
                      determine how best to exploit the vulnerability of small
                      tumours to stochastic extinction. Whereas standard clinical
                      practice is to wait for evidence of relapse, we confirm a
                      recent hypothesis that the optimal time to switch to a
                      second treatment is when the tumour is close to its minimum
                      size before relapse, when it is likely undetectable. This
                      optimum can lie slightly before or slightly after the nadir,
                      depending on tumour parameters. Given that this exact time
                      point may be difficult to determine in practice, we study
                      windows of high extinction probability that lie around the
                      optimal switching point, showing that switching after the
                      relapse has begun is typically better than switching too
                      early. We further reveal how treatment efficacy and tumour
                      demographic and evolutionary parameters influence the
                      predicted clinical outcome, and we determine how best to
                      schedule drugs of unequal efficacy. Our work establishes a
                      foundation for further experimental and clinical
                      investigation of this evolutionarily-informed multi-strike
                      treatment strategy.},
      keywords     = {cancer treatment (Other) / evolutionary rescue (Other) /
                      evolutionary therapy (Other) / extinction therapy (Other) /
                      mathematical oncology (Other) / therapeutic resistance
                      (Other)},
      cin          = {B086},
      ddc          = {570},
      cid          = {I:(DE-He78)B086-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41296747},
      doi          = {10.1093/genetics/iyaf255},
      url          = {https://inrepo02.dkfz.de/record/306596},
}