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<p>Deciphering patient-specific mechanisms of cancer cell reprogramming remains a crucial challenge in systems oncology, as it is key to improving patient diagnosis and treatment. For this reason, comprehensive and patient-specific multi-omic characterization of tumor specimens has become increasingly common in clinical practice. Here, we developed <i>PatientProfiler</i>, a computational workflow that integrates proteogenomic data with curated causal interaction networks to generate mechanistic models of signal transduction for individual patients. <i>PatientProfiler</i> allows multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. We successfully benchmarked the tool on proteogenomic and clinical data derived from 122 biopsies of treatment-naïve breast cancer, available through the CPTAC portal. We identified patient-specific mechanistic models that recapitulate oncogenic signaling pathways. In-depth topological exploration of these networks revealed seven subgroups of patients, associated with unique transcriptomic signatures and distinct prognostic values. We identified well-known Basal-like 1 and 2 subtypes, while also highlighting distinct mechanistic drivers such as the MYC–CDK4/6 axis or NF-kappaB-mediated inflammatory programs. Beyond breast cancer, <i>PatientProfiler</i> offers a generalizable framework to transform cohort-level multi-omic data into interpretable mechanistic models, making it applicable across diverse cancer types and other complex diseases.</p>

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PatientProfiler: building patient-specific signaling models from proteogenomic data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Veronica Lombardi,
  • Lorenzo Di Rocco,
  • Eleonora Meo,
  • Veronica Venafra,
  • Elena Di Nisio,
  • Valerio Perticaroli,
  • Mihail Lorentz Nicolaeasa,
  • Chiara Cencioni,
  • Francesco Spallotta,
  • Rodolfo Negri,
  • Francesca Sacco,
  • Livia Perfetto

摘要

Deciphering patient-specific mechanisms of cancer cell reprogramming remains a crucial challenge in systems oncology, as it is key to improving patient diagnosis and treatment. For this reason, comprehensive and patient-specific multi-omic characterization of tumor specimens has become increasingly common in clinical practice. Here, we developed PatientProfiler, a computational workflow that integrates proteogenomic data with curated causal interaction networks to generate mechanistic models of signal transduction for individual patients. PatientProfiler allows multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. We successfully benchmarked the tool on proteogenomic and clinical data derived from 122 biopsies of treatment-naïve breast cancer, available through the CPTAC portal. We identified patient-specific mechanistic models that recapitulate oncogenic signaling pathways. In-depth topological exploration of these networks revealed seven subgroups of patients, associated with unique transcriptomic signatures and distinct prognostic values. We identified well-known Basal-like 1 and 2 subtypes, while also highlighting distinct mechanistic drivers such as the MYC–CDK4/6 axis or NF-kappaB-mediated inflammatory programs. Beyond breast cancer, PatientProfiler offers a generalizable framework to transform cohort-level multi-omic data into interpretable mechanistic models, making it applicable across diverse cancer types and other complex diseases.