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<p>Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “<i>CanSeer</i>”—a methodology for developing personalized therapeutics. <i>CanSeer</i> employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. <i>CanSeer</i> reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc.</p>

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CanSeer: a translational methodology for developing personalized cancer models and therapeutics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月29日

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  • Rida Nasir Butt,
  • Bibi Amina,
  • Muhammad Umer Sultan,
  • Zain Bin Tanveer,
  • Mahnoor Naseer Gondal,
  • Risham Hussain,
  • Salaar Khan,
  • Rida Akbar,
  • Zainab Nasir,
  • Muhammad Farhan Khalid,
  • Asher Alban Channan-Khan,
  • Amir Faisal,
  • Muhammad Shoaib,
  • Safee Ullah Chaudhary

摘要

Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “CanSeer”—a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc.