# Immune Profiling the Axilla with Fine Needle Aspiration Is Feasible to Risk-Stratify Breast Cancer

## TL;DR

Fine needle aspiration can sample immune cells from axillary lymph nodes in breast cancer patients, offering a minimally invasive way to assess cancer spread and immune response.

## Contribution

Demonstrates that fine needle aspiration can capture immune-cell profiles from axillary lymph nodes, correlating with tumor burden in breast cancer.

## Key findings

- FNA samples contain diverse leukocyte populations, including T-cell subsets and rare immune cells like NK and pDCs.
- Immune profiles from FNA correlate with axillary tumor burden, showing differences between patients with ≤1 or ≥2 metastatic lymph nodes.
- FNA reveals immune changes, such as decreased NK cell activation receptors and pDCs, in patients with higher tumor burden.

## Abstract

Assessing if breast cancer has spread to the armpit (‘axillary’) lymph nodes (ALN) is key in directing treatment. Fine needle aspiration (FNA) is already used routinely in the clinic to detect if cancer cells are present within ALN, but here, we show that the technique can be used to evaluate the axillary immune response. Using flow cytometry, we were able to collect and characterise diverse white blood cell (‘leukocyte’) populations from FNA patient samples, including abundant T-cell subpopulations and rarer natural killer and plasmacytoid dendritic cell populations. Importantly, the immune-cell profiles obtained via FNA appear to correlate with the amount of tumour that is present within the ALN chain (‘axillary tumour burden’). Thus, FNA-based immunophenotyping is a viable, minimally invasive approach for risk stratification in breast cancer.

Background: Axillary lymph node (ALN) metastasis is a critical prognostic determinant in breast cancer (BC) that informs surgical management. However, surgically clearing the axilla carries morbidity, so less invasive methods of risk-stratifying patients are needed. ALN fine needle aspiration (FNA) is currently used to detect BC metastases, but these samples also contain immune cells. Methods: Cells obtained via FNA from BC-patient-derived ALNs were analysed using flow cytometry. Results: FNA acquires sufficient leukocytes for comprehensive immunophenotyping of reactive, patient-derived ALNs. All CD4+ and CD8+ T-cell subsets (naïve, terminal effector, central memory, and effector memory) and rarer (<2%) natural killer (NK) and plasmacytoid dendritic cell (pDC) populations are represented. Importantly, the immune-cell profile of one reactive ALN appears to reflect the immune status of the patient’s axilla. Furthermore, FNA captures immune differences between patients with ≤1 or ≥2 metastatic ALNs. Increased numbers of naïve CD4+ T cells, but fewer terminal effector, central memory, and effector memory subpopulations, were obtained from patients with ≥2 metastatic ALNs. Moreover, despite their sparse distribution pattern on whole-section immunohistochemistry (WSI), FNA revealed that CD56+ NK cell activation receptors were decreased in patients with ≥2 metastatic ALNs. Finally, FNA captured a decrease in pDCs in patients with ≤1 metastatic ALNs, despite their clustered distribution pattern on WSI. Conclusions: FNA is not only feasible for sampling leukocytes from reactive, patient-derived ALNs, but also identifies immune-cell profiles that reflect axillary tumour burden in BC. Thus, this technique could be used to risk-stratify BC patients in the future.