Breast cancers that look alike can behave very differently. This study uses light to find a key cancer marker on individual cells—and then reveals a surprising change that remains even after that marker is silenced. Breast cancer is the most frequent malignancy among women in developed countries and the leading cause of cancer-related death.

That makes knowing what kind of breast cancer a person has more than a label—it can shape what happens next. But breast cancer is a heterogeneous disease: patients with apparently similar clinical and pathological characteristics may face different clinical courses.

In other words, similar-looking cases can follow very different paths. The study focuses on a marker called HER2, a feature of some breast cancer cells that can be measured and linked to how the cells behave. The combined light-based approach measures HER2 and connects it with the cell's internal chemical changes.

The same approach was also used after HER2 was silenced in cell clones. That lets the changes linked with HER2 be examined while reducing interference from the receptor that cells naturally produce. The test uses tiny gold nanoparticles carrying a Raman reporter and a specific antibody designed to recognize HER2, creating a light-readable probe for detecting the marker.

The selected Trastuzumab antibody targets and binds HER2, allowing this antibody-based test to identify cells where that receptor is present. The antibody used for recognition is trastuzumab, a treatment antibody that targets and binds HER2 and inhibits signals that promote cell growth.

It is already standard care for patients whose breast cancers have high HER2. The signal outlining each cell tracks how much of the HER2 marker sits on its surface. It is barely visible in one cell type, stronger in another, and concentrated around the membrane in the third, showing that this measurement can distinguish cells by their HER2 level.

The cells also carried broader chemical patterns that distinguished the different breast cell lines. A computer trained on those patterns classified the breast cancer cell lines with 100 percent overall accuracy and a mean prediction error below five percent.

Similar results were achieved using only one spectrum per cell and checking it on an independent batch of cells. That suggests the pattern was not limited to combining many readings from the same source. To test whether the HER2 signal itself was driving the distinction, the study compared two otherwise matched cell lines that differed only in HER2 expression.

The light-based method separated them and measured HER2 in individual cells. In cells with HER2, the receptor was abundant at the cell boundaries. After HER2 was silenced, it was completely absent, and the light signal fell by 93 percent.

The cells’ chemical fingerprints separate clearly after the growth signal is silenced: their overall spectral patterns shift, and a fourth summary measure places the two groups apart with a probability below two point two times ten to the power of minus sixteen. Here is the surprise: cells with HER2 silenced showed an increase in lipid content.

The cells' ability to multiply, linked to high HER2, appeared to be separate from these metabolic changes, especially the changes involving fats. The finding suggests that fat content and fatty-acid production may be controlled through pathways independent of HER2, because they persisted or even increased after HER2 was silenced.

The connection still needs to be clarified. The study concludes that combining gene silencing with the two light-based measurements can classify breast cancers at the level of individual cells and describe both shared and distinctive chemical changes.

The results provide a basis for testing the approach on patient biopsies or tumor tissue, where it could support diagnosis or help follow a person's response to treatment. The combined light-based test identified HER2 levels and cell differences, while showing that increased fat production can continue without HER2.

That could eventually help doctors diagnose tumors and track treatment more precisely.