Quantification of hypoxia-related gene expression as a potential approach for clinical outcome prediction in breast cancer

Abstract

Breast cancers are solid tumors frequently characterized by regions with low oxygen concentrations. Cellular adaptations to hypoxia are mainly determined by "hypoxia inducible factors" that mediate transcriptional modifications involved in drug resistance and tumor progression leading to metastasis and relapse occurrence. In this study, we investigated the prognostic value of hypoxia-related gene expression in breast cancer. A systematic review was conducted to select a set of 45 genes involved in hypoxia signaling pathways and breast tumor progression. Gene expression was quantified by RT-qPCR in a retrospective series of 32 patients with invasive ductal carcinoma. Data were analyzed in relation to classical clinicopathological criteria and relapse occurrence. Coordinated overexpression of selected genes was observed in high-grade and HER2+ tumors. Hierarchical cluster analysis of gene expression significantly segregated relapsed patients (p = 0.008, Chi2 test). All genes (except one) were up-regulated and six markers were significantly expressed in tumors from recurrent patients. The expression of this 6-gene set was used to develop a basic algorithm for identifying recurrent patients according to a risk score of relapse. Analysis of Kaplan-Meier relapse-free survival curves allowed the definition of a threshold score of 2 (p = 0.021, Mantel-Haenszel test). The risk of recurrence was increased by 40% in patients with a high score. In addition to classical prognostic factors, we showed that hypoxic markers have potential prognostic value for outcome and late recurrence prediction, leading to improved treatment decision-making for patients with early-stage invasive breast cancer. It will be necessary to validate the clinical relevance of this prognostic approach through independent studies including larger prospective patient cohorts.

Fig 1. Hypoxia-related gene expression profiles according to clinicopathological data.

Gene expression was determined using quantitative real-time PCR as described in the Materials and Methods. The results are presented as the fold induction of relative quantification by classification in ascending order. A positive fold change of 1 indicated 2-fold up-regulation, and a negative fold change of -1 indicated 2-fold down-regulation. A comparative analysis was performed between (A) high tumor stage vs low tumor stage, (B) high mSBR grades vs low mSBR grades, (C) HER2+ status vs HER2- status, and (D) recurrent patients vs non-recurrent patients. Statistical analysis was performed between groups using Student’s t or Kruskal Wallis tests (red bar: p < 0.05; black bars: p < 0.10).

Fig 2. Profile of hypoxia-related gene expression in 32 tumors from patients with early-stage breast cancer.

Data are presented in heat map format combined with hierarchical clustering using ΔCt values of gene expression. Each row represents a gene, and each column represents a patient. Gene expression is relative to the median of ΔCt values. Genes in red and green indicate expression above and below the median, respectively. (A) Hierarchical cluster analysis using all selected genes. (B) Hierarchical cluster analysis using the 6 differentially expressed genes with statistical significance between the recurrent group and non-recurrent group.

Fig 3. Kaplan-Meier relapse-free survival curves according to the risk score of relapse.

Curve 1: 15 patients with score ≤ 2. Curve 2: 17 patients with score ≥ 3. The 14 recurrent patients were in curve 2 (p = 0.021, Mantel-Haenszel test).

Fig 4. Analysis of the internal consistency of the 6 genes differentially expressed between recurrent and non-recurrent patients.

Cronbach’s alpha coefficient was calculated to measure internal consistency (alpha = 0.90).