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A text-based computational framework for patient -specific modeling for classification of cancers.

Hiroaki Imoto1, Sawa Yamashiro1, Mariko Okada1,2

  • 1Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.

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Patient heterogeneity necessitates personalized cancer treatments. Pasmopy (Patient-Specific Modeling in Python) enables in silico modeling of signaling dynamics for patient stratification, improving prognostic marker discovery and drug response prediction.

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Area of Science:

  • Computational biology
  • Systems biology
  • Cancer research

Background:

  • Patient heterogeneity poses a significant challenge in cancer treatment and drug development.
  • There is an urgent need for methods to identify prognostic markers for individualized cancer therapy.

Purpose of the Study:

  • To introduce Pasmopy, a computational framework for patient stratification using in silico signaling dynamics.
  • To demonstrate the application of Pasmopy in modeling breast cancer signaling networks and predicting patient outcomes.

Main Methods:

  • Pasmopy converts textual descriptions of biochemical systems into executable mathematical models.
  • Developed an ErbB receptor signaling network model trained on cell lines.
  • Performed in silico simulations on 377 breast cancer patients using The Cancer Genome Atlas (TCGA) transcriptome data.

Main Results:

  • Temporal dynamics of Akt, ERK, and c-Myc were simulated for individual patients.
  • The model accurately predicted prognosis differences in triple-negative breast cancer (TNBC).
  • Predicted sensitivity to kinase inhibitors in TNBC patients.

Conclusions:

  • Pasmopy facilitates patient stratification based on in silico signaling dynamics.
  • The framework is applicable to diverse signaling networks.
  • Enables network-based prognostic marker utilization and drug response prediction.