Abstract P3-03-19: Tumor cell detection and immune profiling of lymph nodes from breast cancer patients by mass cytometry
Authors: Russnes HG , Rye IH , Huse K , Schlichting E , Garred O , Mykelbust JH 
Published: May 2019

Abstract
Introduction:
A sentinel lymph node (SN) is the primary node draining the tumor and is assumed to be affected early in the metastatic process. Detection of metastases in SN is a standard procedure in breast cancer diagnostics based on microscopic evaluation (morphology and immunohistochemistry), determining the need for removal of all axillary glands for inspection which again is crucial for tailoring adjuvant therapy. The identification by microscopy is time-consuming and has a risk for false negative results.
We hypothesize that the immune profile of SN changes with the presence of tumor cells, even at very low frequencies (micrometastases). By using a multi marker approach to characterize millions of cells from sentinel lymph nodes with and without metastases we aimed at identifying both tumor cells but also characterize a tumor specific immune response. This dual approach might provide an opportunity for a more sensitive test for SN diagnostics.
Material and Methods:
We established a mass cytometry assay containing 38 markers (antibodies) using CyTOF technology to combine immune profiling with identification of breast cancer cells. Cell suspensions from 14 metastatic axillary lymph nodes (ALNmet), 16 metastatic sentinel lymph nodes (Snmet) and 14 non-metastatic sentinel lymph nodes (SN) from breast cancer patients from the clinical observational trial Oslo2 (early, operable breast cancer patients representing all subtypes) were successfully analyzed by the multimarker panel (single cell resolution).
Results:
By using mass cytometry, we detected tumor cells (gated as PanKeratin+/CD45- cells) in 86% (26/30) metastatic lymph nodes (ALNmet and Snmet) and in 14% (2/14) non-metastatic lymph nodes (SN). Further, the leukocyte population, identified as CD45+ cells, was gated into 15 subpopulations, mainly comprising different subsets of B and T cells, monocytes and NK cells. By comparing the leukocyte composition in the ALNmet with those in SN samples we identified a significant increase in the abundance of CD8+ memory phenotype, TFH and TCRγδ cells and a decrease in the CD4+ subpopulation in ALNmet compared to the SN samples. The Snmet samples had smaller deposits of tumor cells than the ALNmet samples, and we found no significant differences in leukocyte composition between Snmet and SN samples.
Interestingly, when looking at the activation marker CD56, we observed a significant higher expression in the CD4RO, CD8RO, TFH and Treg subpopulations of Snmet samples compared to SN samples.
Conclusion:
In this study we identified a significant difference in immune cell composition in lymph nodes with and without metastases (ALNmet compared to SN samples). We also identified activation markers unique for subpopulations of lymphocytes in Snmet, but not in negative lymph nodes (SN). We were also able to detect and identify micrometastases in most lymph nodes where morphological examination had identified them, but in addition found tumor cells in two samples scored as negative. The results will be validated in a larger sample series.
Citation Format: Russnes HG, Rye IH, Huse K, Schlichting E, Garred O, Mykelbust JH. Tumor cell detection and immune profiling of lymph nodes from breast cancer patients by mass cytometry [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-03-19.