Comparative analysis of cell parameter groups for breast cancer detection

Area of Science:

• Biophysics
• Immunology
• Computational Biology

Background:

• Accurate breast cancer diagnosis is crucial for effective treatment.
• Existing diagnostic methods can be invasive or lack specificity.
• Novel biomarkers and analytical approaches are needed for early detection.

Purpose of the Study:

• To develop and validate a novel method for comparative analysis of biological parameters correlated with disease.
• To assess the diagnostic potential of fluorescence polarization (FP) in distinguishing breast cancer patients from healthy individuals.
• To identify specific FP parameters and stimulation conditions most indicative of breast cancer.

Main Methods:

• Utilized Information Theory and Nonparametric Statistics for parameter correlation analysis.
• Employed normalized mutual information for quantifying parameter correlation.
• Measured fluorescence polarization (FP) in fluorescein diacetate (FDA)-stained peripheral blood mononuclear cells (PBMCs) from healthy subjects and breast cancer patients.
• Stimulated PBMCs with tumor tissue, phytohemagglutinin (PHA), or no stimulant.

Main Results:

• Established a method for grouping FP parameters based on their correlation with breast cancer.
• Identified PHA stimulation as yielding the greatest difference in FP parameters between breast cancer patients and healthy subjects.
• Parameter P1, derived from the PHA test, demonstrated the highest diagnostic potential.

Conclusions:

• The developed method effectively identifies disease-correlated parameters using Information Theory.
• Fluorescence polarization of PBMCs, particularly under PHA stimulation, serves as a promising biomarker for breast cancer detection.
• This approach offers a potential non-invasive method for breast cancer diagnostics.