Breast cancer detection by Michaelis-Menten constants via linear programming.

Area of Science:

• Biochemistry
• Oncology
• Medical Diagnostics

Background:

• Breast cancer diagnosis relies on various methods, but early and accurate detection remains crucial.
• Enzymatic activity in peripheral blood mononuclear cells (PBMC) can reflect cellular health and disease states.

Purpose of the Study:

• To evaluate the efficacy of Michaelis-Menten constants (K(m) and V(max)) derived from enzymatic hydrolysis of fluorescein diacetate (FDA) for breast cancer detection.
• To develop a diagnostic model for classifying subjects as diseased, not diseased, or uncertain.

Main Methods:

• Assessed the enzymatic hydrolysis rate of FDA in PBMCs from healthy individuals and breast cancer patients.
• Utilized linear programming to determine Michaelis-Menten constants (K(m), V(max)).
• Measured fluorescence intensity (FI) in individual cells after incubation with phytohemagglutinin (PHA) or tumor tissue.

Main Results:

• The model correctly diagnosed 44 out of 50 subjects.
• Five cases resulted in an uncertain diagnosis.
• One subject was incorrectly diagnosed.

Conclusions:

• Michaelis-Menten kinetics applied to PBMC enzymatic activity show potential for breast cancer diagnosis.
• The proposed model demonstrates high accuracy in distinguishing between healthy and diseased subjects.
• Further validation is warranted to refine the diagnostic capabilities and reduce uncertainty.