- A machine learning-assisted mathematical model identified genes in the tumor microenvironment (TME) that are strongly associated with prognosis in patients with stage III, estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer.
Why this matters
- Stroma in the TME is known to affect prognosis and therapeutic response; however, there are only a few prognostic mathematical models based on mRNA expressivity in the TME.
- Using 50 cycles of machine learning, the model categorized 98 patients with ER+, HER- breast cancer from the Cancer Genome Atlas Program into high- and low-risk groups based on mRNA expression of 26 gene groups.
- The gene groups comprised 191 genes enriched in cellular and noncellular elements of the TME.
- Funding: None disclosed.
- 15 genes, namely CD8A, CD8B, FCRL3, GZMK, CD3E, CCL5, TP53, ICAM3, CD247, IFNG, IFNGR1, ICAM4, SHH, HLA-DOB, and CXCR3 were associated with good prognosis.
- 5 genes, namely LOXL2, PHEX, ACTA2, MEGF9, and TNFSF4 were associated with poor prognosis.
- There was a significant survival difference between the high- and low-risk groups (HR, 2.878; P=.05).
- The high-risk group had a higher expression of genes associated with desmoplastic reaction, neutrophils, and immunosuppressive cytokines, whereas the low-risk group had a higher expression of genes associated with immune system activation (P<.05>
- Other types of breast cancer were not assessed.