A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer

Int J Cancer. 2017 Mar 15;140(6):1413-1424. doi: 10.1002/ijc.30556.

Abstract

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16-gene signature capable of stratifying breast cancer patients into four risk levels with intention that low-risk patients would not undergo adjuvant systemic therapy, intermediate-low-risk patients will be treated with hormonal therapy only, and intermediate-high- and high-risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16-gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low-risk group (n = 51) of 205 estrogen receptor-positive and node negative (ER+/node-) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15-year disease-specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e-12) in stratifying ER+/node- subgroup than OncotypeDx (HR = 2.7, p = 1.3e-7), MammaPrint (HR = 2.5, p = 5.8e-7), rorS (HR = 2.4, p = 1.4e-6), and NPI (HR = 2.6, p = 1.2e-6). YMR signature may be developed as a clinical tool to select a subgroup of low-risk ER+/node- patients who do not require any adjuvant hormonal therapy (AHT).

Keywords: Breast cancer; Gene Expression; Signature; Yin Yang.

Publication types

  • Comparative Study
  • Validation Study

MeSH terms

  • Adult
  • Biomarkers, Tumor / analysis
  • Breast / chemistry
  • Breast Neoplasms / chemistry
  • Breast Neoplasms / genetics*
  • Breast Neoplasms / therapy
  • Datasets as Topic / statistics & numerical data
  • Estrogens*
  • Female
  • Genes, Neoplasm*
  • Humans
  • Middle Aged
  • Neoplasm Proteins / biosynthesis
  • Neoplasm Proteins / genetics*
  • Neoplasms, Hormone-Dependent / chemistry
  • Neoplasms, Hormone-Dependent / genetics*
  • Neoplasms, Hormone-Dependent / therapy
  • Prognosis
  • Proportional Hazards Models
  • Receptors, Estrogen / analysis*
  • Transcriptome*
  • Treatment Outcome
  • Yin-Yang

Substances

  • Biomarkers, Tumor
  • Estrogens
  • Neoplasm Proteins
  • Receptors, Estrogen