Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer

1. Introduction

2. Results and Discussion

Figure 1. Construction of the gene-set predictor/gene signature for risk prediction. (A) Gene selection on the published datasets; (B) Gene selection on the merged Gene Expression Omnibus (GEO) datasets; (C) Developing the gene signature.

2.1. Gene Selection on the Published Datasets

We used data deposited in NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/, GEO Series accession number GSE1456 and GSE3494), including 408 breast cancer cases. Files containing raw intensity data of Affymetrix HU133A and HU133B arrays of the two datasets (GSE1456 and GSE3494) were preprocessed using R/Bioconductor (GCRMA package, quantile normalization, median polish summarization). The two data sets were pre-processed together using the supercomputer Michelangelo ( http://www.litbio.org). The candidate genes were selected from the above mentioned datasets as those included in 4 previously proposed signatures: the “70-gene signature” developed by van de Vijver et al. [3] and van’t Veer et al. [2] including 70 genes, the “recurrence-score” developed by Paik et al. [9] including 21 genes, the “two-gene-ratio model” [12] including 2 genes and the “Insulin Resistance” signature including 15 genes [20] (Table 1). Since some genes are present in more than one signature, the final extracted set was made up of 98 genes (194 Affy-probes) (Table 1).

Table 1. Genes selected and also present in other previously published signatures (1 = van’t Veer et al. [2], 2 = Paik et al. [9], 3 = Gennari et al. [20], 4 = 2 Ma et al. [12], 1.5 = van’t Veer et al. [2] with Paik et al. [9]).

2.2. Gene Selection on the Merged GEO Datasets

The 98 genes selected from the published signatures were first tested in univariate analysis for their association with disease free survival (DFS). Forty-eight genes resulted associated with DFS with a p value < 0.01 and were selected for the subsequent step. Using an unsupervised hierarchical clustering algorithm, 20 clusters were selected grouping genes with similar expression profiles. A gene was selected within each cluster using a multivariate Cox model, choosing the one most associated with DFS: the final 20-genes set, all highly associated with DFS, are reported in Table 2.

Table 2. Final 20 genes set, all highly associated with Disease free survival (DFS).

2.3. Tumor Samples

Among 350 consecutive invasive breast cancer patients with full information about tumor, adjuvant treatments, follow up, relapse, death and causes of death, treated between 1998 and 2001, 89 cases (25.4%) were removed from the study because of the low RNA concentration (below 10 ng/μL) or high degradation (Ct values for ACTB and B2M over 34). The remaining 261 cases were split in two biological sample datasets: The training (137 cases) and the validation set (124 cases) by a simple criteria of consecutiveness.

The clinical and demographic characteristics of the patients included in the training and in the validation set are summarized in Table 3 and reported in detail in the supplementary file. Due to a simple criteria of consecutiveness building the sets, the Training set has a longer mean follow up (100.7 months; range 59–123) as compared with the Validation set (89.2; 61–121). Nevertheless, the only significant differences between the two sets was the use of anthracycline-based regimens in the adjuvant setting (Training 16% vs. Validation 32.2%; p = 0.01) and an higher incidence of G3 tumors in the Validation Set (30.6% vs. 19.7, p = 0.04). The lack of information about HER2 Status is related to the temporal context of the selected cases (1998–2001) and it was evaluated “a posteriori” just in 40% of relapsed patients. Any other clinical and biological pattern is similar and reflecting the “real life” picture of the disease in North East of Italy at this time.

Table 3. Characteristics of patients and tumors in the Training and Validation sets.

2.4. Signature Definition on the Training Set

A multivariate stepwise Cox analysis was run on the breast cancer samples including the 20 selected genes. The Cox model selected a final set of five genes independently associated with DFS (Table 4): FGF18 (HR = 1.13, p = 0.05), BCL2 (HR = 0.57, p = 0.001), PRC1 (HR = 1.51, p = 0.001), MMP9 (HR = 1.11, p = 0.08), SERF1a (HR = 0.83, p = 0.007).

Table 4. Genes selected in the five-genes signature. Variables in the Equation.

These five genes were combined into a linear score (signature) weighted according to the coefficients of the Cox model (Table 4), as:

$$\text{Genesignature}=0.125\times FGF18-0.560\times BCL2+0.409\times PRC1+0.104\times MMP9-0.188\times SERF1$$

(1)

This score ranged from −2.95 to 2.91, with a mean value of −0.48 a SD of 1.00. The linear score was highly associated with DFS in the training set: HR = 2.7, 95% CI = 1.9–4.0, p < 0.001.

The score was then categorized in three groups according to the tertiles of its distribution. The DFS according to the three risk groups is reported in Figure 2: Patients with an intermediate risk signature had an HR = 6.03, (95% CI = 1.35–27.0, p = 0.019) and patients with a high risk signature had an HR = 10.8, (95% CI = 2.51–46.64, p = 0.001) as compared to patients with a low risk signature.

Figure 2. Training set: Probability of 5 years relapse: Disease free survival (DFS) according to the risk groups defined by the gene signature in the training set: Low risk group (blue curve), intermediate risk group (green curve), high risk group (red curve). The hazard ratio (HR) of DFS for intermediate risk patients as compared to low risk is 6.0 (95% Confidence Intervals (CI) = 1.35–27.0, p = 0.019 and the HR of DFS for high risk patients as compared to low risk is 10.8 (95% CI = 2.51–46.6, p = 0.001).

2.5. Signature Evaluation on the Validation Set

The signature defined on the training set was evaluated on the independent set of data of the 124 patients included in the validation set. The discrimination ability of the signature was assessed on the validation set by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set.

The score resulted highly associated with DFS also in the validation set (p < 0.001) (Figure 3). Patients with an “intermediate risk” signature had an HR = 2.1 (95% CI = 0.72–6.2, p = 0.17) and patients with a high risk signature had an HR = 5.4 (95% CI = 2.0–14.4, p = 0.001) as compared to patients with a low risk signature.

Figure 3. Validation set: Probability of 5 years relapse. Disease free survival (DFS) according to the risk groups defined by the gene signature in the validation set: low risk group (blue curve), intermediate risk group (green curve), high risk group (red curve). The hazard ratio (HR) of DFS for intermediate risk patients as compared to low risk is 2.1 (95% Confidence Intervals (CI) = 0.72–6.2, p = 0.17) and the HR of DFS for high risk patients as compared to low risk is 5.4 (95% CI = 2.0–14.4, p = 0.001).

2.7. Univariate Analysis

In the Univariate Analysis variables significantly related to DFS were Nodal Status (p = 0.0000001), T Size (p = 0.000002), the five gene Signature (i = 0.000043), Ki67 (p = 0.0007) and Grading (p = 0.027) (Table 5).

Table 5. Univariate analysis.

2.8. Multivariate Analysis

The Multivariate Analysis (Cox Regression) indicates that Nodal Status (p = 0.00001), T Size (p = 0.0002) and the five-gene Signature (p = 0.0004) are significantly related to DFS, while Ki67 (cut off: 14%), Grading and Chemo- or Endocrine Adjuvant Treatments are not (Table 6). The five-gene Signature HR is slightly affected by adjuvant treatments: Table 7 summarized data about the five-gene signature in presence or absence of Adjuvant treatment.

Table 6. Multivariate Cox regression analysis.

Table 7. Hazard Ratio Longrank (Cox-Mantel) for five-gene signature in presence or absence of adjuvant treatments.

3. Experimental Section

3.3. Gene Expression Analysis on Breast Cancer Samples

3.3.1. RNA Isolation

Paraffin-embedded tumor material obtained from the 20 μm thick sections was de-paraffinized in xilene at 50 °C for 3 min and rinsed twice in absolute ethanol at room temperature. Total RNA was extracted using the RecoverAll kit (Ambion, Austin, TX, USA), including a DNase step according to the manufacturer’s recommended protocol. RNA concentration was measured by Quant-iT™ RNA kit (Invitrogen, Carlsbad, CA, USA).

3.3.2. Primers Design

Primers were designed using Primer3 software ( http://simgene.com/Primer3) and are described in Table 8. Amplicons were tested by MFOLD ( http://mfold.rna.albany.edu/?q=mfold) in order to avoid secondary structures within primer positions and they were tested by repeatmasker ( http://www.repeatmasker.org) and primer-BLAST ( http://www.ncbi.nlm.nih.gov/tools/primer-blast) for primer specificity.

Table 8. Primer sequences, slope, PCR efficiency and RSq of each of the 20 genes + 2 housekeeping genes.

3.3.3. Two Step RTqPCR Analysis

Fourteen μL of total RNA was subjected to reverse transcription using SuperScript® VILO™ cDNA Synthesis kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s recommended protocol. One microlitres of cDNA was amplified in duplicate adding 10 picomoles of each primer (see Table 8 for sequence details) to the 1x QuantiFast™ SYBR® Green PCR solution (Qiagen, Hilden, Germany) in a final volume of 25 μL.

Cycling conditions consisted of 5 min at 95 °C, 10 s at 95 °C, 30 s at 60 °C for a total of 40 cycles, using Stratagene Mx3000™ or ABI SDS 7000™ instruments. Plate reading was performed during the 60 °C step.

For each primer set, standard curves made from serial dilutions of cDNA from MCF7 cell lines (see Table 2) were used to estimate PCR reaction efficiency (E) using the formula: E (%) = (10 ^[−1/slope] − 1) × 100. The expression levels of each of the 20 genes selected were normalized by GeNorm [35] using 2 housekeeping genes (B2M e ACTB) and the relative quantification was calculated by the statistical computing language R. The human breast cancer cell line MCF7 was purchased from American Type Culture Collection (ATCC HTB22; derived from a human breast adenocarcinoma). Cells were maintained in minimal essential medium (MEM) (Invitrogen/Life technologies, Villebon-sur-Yvette, France) supplemented with 2 mM l-glutamine, 1.5 g/L sodium bicarbonate, 0.1 mM nonessential aa, 1 mM pyruvate sodium, 0.01 mg/mL bovine insulin, and 10% fetal bovine serum (Thermo Scientific, Waltham, MA, USA) at 37 °C in a humidified atmosphere of 5% CO₂.

4. Conclusions

Abbreviation

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