Cell Reports (Oct 2019)

Transcriptional Signature Derived from Murine Tumor-Associated Macrophages Correlates with Poor Outcome in Breast Cancer Patients

  • Sander Tuit,
  • Camilla Salvagno,
  • Theodore S. Kapellos,
  • Cheei-Sing Hau,
  • Lea Seep,
  • Marie Oestreich,
  • Kathrin Klee,
  • Karin E. de Visser,
  • Thomas Ulas,
  • Joachim L. Schultze

Journal volume & issue
Vol. 29, no. 5
pp. 1221 – 1235.e5

Abstract

Read online

Summary: Tumor-associated macrophages (TAMs) are frequently the most abundant immune cells in cancers and are associated with poor survival. Here, we generated TAM molecular signatures from K14cre;Cdh1flox/flox;Trp53flox/flox (KEP) and MMTV-NeuT (NeuT) transgenic mice that resemble human invasive lobular carcinoma (ILC) and HER2+ tumors, respectively. Determination of TAM-specific signatures requires comparison with healthy mammary tissue macrophages to avoid overestimation of gene expression differences. TAMs from the two models feature a distinct transcriptomic profile, suggesting that the cancer subtype dictates their phenotype. The KEP-derived signature reliably correlates with poor overall survival in ILC but not in triple-negative breast cancer patients, indicating that translation of murine TAM signatures to patients is cancer subtype dependent. Collectively, we show that a transgenic mouse tumor model can yield a TAM signature relevant for human breast cancer outcome prognosis and provide a generalizable strategy for determining and applying immune cell signatures provided the murine model reflects the human disease. : Tuit et al. show that TAM transcriptomes in murine models of breast cancer are governed mainly by tissue and tumor subtype-specific signals. Clinical translation of murine signatures can be achieved when human and mouse breast tumor subtypes are matched and only upon proper comparison of TAMs with healthy tissue macrophages. Keywords: mouse model, human, co-expression network analysis, transcriptome, innate immunity, breast cancer, tumor-associated macrophage, clinical outcome prediction, invasive lobular carcinoma