Triple Negative Breast Cancer currently accounts for around 15% of all breast cancer cases in women, yet is more difficult to treat and carries a higher mortality rate. It has also proven harder to diagnose, particularly due to its lack of surface receptors typically used for binding with conventional cancer-detecting antigens.
By using three different microscopic imaging modalities, our aim is to differentiate four samples of TNBC against a non-triple negative control subtype. Backed with research on biological relevance of the measured data, we hope to form a more cohesive classification of TNBC for earlier diagnosis and easier treatment.
Principal Aim:
To acquire and quantify subcellular properties across TNBC samples with three different imaging modalities
Properties:
Protein and Lipid Concentration (Stimulated Raman Scattering)
NADH/FADH Redox Ratio (Two-Photon Fluorescence)
Collagen Curvature (Second Harmonic Generation)
Secondary Aim:
To establish correlations between observed TNBC properties and their respective subtypes using ImageJ / Python
Made by Josh Lariosa