Biomarkers from biosimulations: Transcriptome-To-Reactome™ Technology for individualized medicine.

Area of Science:

• Biochemistry
• Bioinformatics
• Oncology

Background:

• The TGF-β signaling pathway plays a critical role in cellular processes, including cancer development.
• Accurate identification of breast cancer biomarkers is crucial for early diagnosis and effective treatment.

Purpose of the Study:

• To validate a biosimulation model of the TGF-β signaling pathway.
• To identify novel biomarkers for breast cancer detection and diagnosis using patient gene expression data.

Main Methods:

• Utilized Reactome database for pathway reactions.
• Employed a proprietary technique to parameterize the model using individual patient gene expression profiles.
• Trained and validated the model on gene expression data from 45 women with normal, benign, or malignant breast conditions.

Main Results:

• Identified a membrane signaling marker with 80% sensitivity and 60% specificity.
• Identified a nuclear transcription factor marker with 80% sensitivity and 90% specificity for predicting malignancy.
• Biosimulation results, particularly for the nuclear marker, significantly improved diagnostic probability when used with Fagan's nomogram (from 7.5% to 39%).

Conclusions:

• The validated biosimulation model effectively identifies predictive breast cancer biomarkers.
• The nuclear transcription factor marker demonstrates high potential for clinical application in breast cancer diagnosis.
• This technology offers a promising tool for biomarker discovery and clinical decision support in oncology.