Metastatic breast cancers have reduced immune cell recruitment but harbor increased macrophages relative to their matched primary tumors

Abstract

The interplay between the immune system and tumor progression is well recognized. However, current human breast cancer immunophenotyping studies are mostly focused on primary tumors with metastatic breast cancer lesions remaining largely understudied. To address this gap, we examined exome-capture RNA sequencing data from 50 primary breast tumors (PBTs) and their patient-matched metastatic tumors (METs) in brain, ovary, bone and gastrointestinal tract. We used gene expression signatures as surrogates for tumor infiltrating lymphocytes (TILs) and compared TIL patterns in PBTs and METs. Enrichment analysis and deconvolution methods both revealed that METs had a significantly lower abundance of total immune cells, including CD8+ T cells, regulatory T cells and dendritic cells. An exception was M2-like macrophages, which were significantly higher in METs across the organ sites examined. Multiplex immunohistochemistry results were consistent with data from the in-silico analysis and showed increased macrophages in METs. We confirmed the finding of a significant reduction in immune cells in brain METs (BRMs) by pathologic assessment of TILs in a set of 49 patient-matched pairs of PBT/BRMs. These findings indicate that METs have an overall lower infiltration of immune cells relative to their matched PBTs, possibly due to immune escape. RNAseq analysis suggests that the relative levels of M2-like macrophages are increased in METs, and their potential role in promoting breast cancer metastasis warrants further study.

Keywords: Breast cancer; M2 macrophages; Macrophages; Metastatic breast cancer.

Fig. 1

Lower immune abundance in metastatic breast tumors (METs) compared to primary breast tumors (PBTs) (a) Total immune score in PBT/MET pairs in Pan-MET dataset, together with the paired changes (MET-PBT). b Paired changes of total immune score removing BRMs in (a). c Total immune score grouped by MET sites. d Stromal tumor infiltrating lymphocytes (sTILs) percentages of 49 pairs of PBT/BRMs in BRM-sTIL dataset. e Spearman’s correlation between sTILs percentages and total immune score for 15 pairs of PBT/BRMs overlapped by Pan-MET and BRM-sTIL. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05 from two-sided Wilcoxon signed rank test in (a-d) and correlation test in (e)

Fig. 2

Paired comparison of the abundance of immune cell population in PBT/MET pairs in Pan-MET. a-b GSVA score changes (MET-PBT) of (a) Davoli signature and (b) Tamborero signature. c Abundance changes estimated by deconvolution method TIMER. d Changes of percentages relative to total immune level estimated by deconvolution method CIBERSORT. e Changes of the ratio of relative percentages of M2 and M1. ****FDR < 0.0001, ***FDR < 0.001, **FDR < 0.01, *FDR < 0.05 by Benjamini-Hochberg correction. Two-sided Wilcoxon signed rank test

Fig. 3

Multispectral immunohistochemical (mIHC) staining of selective pairs in Pan-MET. a mIHC staining images of one pair of PBT/OVMs and PBT/BRMs. b Percentage (by cell) of each immune cell population denoted by markers using mIHC staining. c Relative percentages of corresponding immune cell populations estimated by CIBEROSRT

Fig. 4

Association of immune abundance with clinical variables and survivals. a Association between immune score and sTILs with clinical variables. b Association between survivals and immune score of PBT/BRM pairs in (b) Pan-MET dataset and (c) BRM-sTIL dataset. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05 from Wilcoxon signed rank and Kruskal-Wallis test in (a) and log-rank test in (b)-(c)