The authors have declared that no competing interests exist.
Conceived and designed the experiments: AMS JK CS ZoS. Performed the experiments: AMS AE QL ZsS AT AR BS. Analyzed the data: AMS ZsS BG JK CS. Contributed reagents/materials/analysis tools: AK MS. Wrote the paper: AMS JK QL CS AE ZoS.
Quantifying chromosomal instability (CIN) has both prognostic and predictive clinical utility in breast cancer. In order to establish a robust and clinically applicable gene expression-based measure of CIN, we assessed the ability of four qPCR quantified genes selected from the 70-gene Chromosomal Instability (CIN70) expression signature to stratify outcome in patients with grade 2 breast cancer.
AURKA, FOXM1, TOP2A and TPX2 (CIN4), were selected from the CIN70 signature due to their high level of correlation with histological grade and mean CIN70 signature expression
We observed a significant association of tumor proliferation, defined by Ki67 and mitotic index (MI), with both CIN4 expression and aneuploidy. The CIN4 score stratified grade 2 carcinomas into good and poor prognostic cohorts (mean RFS: 83.8±4.9 and 69.4±8.2 months, respectively, p = 0.016) and its predictive power was confirmed by multivariate analysis outperforming MI and Ki67 expression.
The first clinically applicable qPCR derived measure of tumor aneuploidy from FFPE tissue, stratifies grade 2 tumors into good and poor prognosis groups.
Chromosomal instability (CIN) is a key determinant of biological behavior of breast cancer
The potential utility of a gene expression based measure of CIN is further emphasized by its complex relationship with histological grade
All microarray data sets used in this analysis were normalized by robust multi-array average (RMA)
Focusing on histological grade, we evaluated 185 invasive breast carcinomas consisting of 63 grade 1, 62 grade 2 and 60 grade 3 FFPE tissue samples regardless of other pathological features from the Buda MÁV Hospital (1999–2002), diagnosed and graded by a single pathologist (J.K.). Recurrence-free survival (RFS) time was defined either by loco-regional relapse or the appearance of a distant metastasis, and whichever shorter if both applicable. The study was a retrospective analysis. General written consent was obtained from all patients at time of surgery. The samples were anonymised for the study. The study was approved by the Ethical Committee of the Semmelweis University (IKEB #7/2008 and #7-1/2008).
In line with bioinformatics, the clinicopathological properties of the selected 185 breast cancer patients were analyzed. The mean age of patients was 58.8±12.8 years (59.9±11.8 years, 59.2±13.0 years, 56.5±13.7 years in grade 1, 2 and 3 tumor groups, respectively). Among the histological types, invasive ductal carcinoma was the most common overall (65.9%), but less frequent types of cancer were also included in the analysis (8.1%): 1 papillary, 1 tubular, 1 micropapillary and 3 mucinous carcinomas in the grade 1 group; 1 micropapillary and 1 mucionous in grade 2; and 4 with medullary features, 2 metaplastic and 1 micropapillary carcinomas in grade 3 cancers. Tumor size, frequency of vascular invasion, presence of necrosis, Nottingham Prognostic Index (NPI) and number of relapses showed an increase, while ER, PgR expression and RFS decreased with higher grade (
Groups according to grade | 1 | 2 | 3 | All | |
|
63 | 62 | 60 | 185 | |
|
59.9 (35–95) | 59.2 (23–87) | 56.5 (29–87) | 58.8 (23–95) | |
|
IDC | 45 (71.4%) | 37 (59.6%) | 40 (66.6%) | 122 (65.9%) |
ILC | 4 (6.3%) | 3 (4.8%) | 1 (1.6%) | 8 (4.3%) | |
Mixed | 8 (12.6%) | 20 (32.2%) | 12 (20.0%) | 40 (21.6%) | |
Other | 6 (9.5%) | 2 (3.2%) | 7 (11.6%) | 15 (8.1%) | |
20.49±1.10 | 26.81±1.54 | 28.55±1.92 | 25.13±13.87 | ||
|
34 (55.5%) | 46 (74.1%) | 49 (81.6%) | 129 (69.7%) | |
|
8 (12.6%) | 16 (25.8%) | 35 (58.3%) | 59 (31.8%) | |
3.00±0.11 | 4.39±0.13 | 5.58±0.15 | 4.39±1.39 | ||
|
Lum A | 55 (87.3%) | 40 (64.5%) | 22 (36.6%) | 117 (63.2%) |
Lum B | 4 (6.3%) | 8 (12.9%) | 15 (25.0%) | 27 (14.5%) | |
HER2 | 0 | 3 (4.8%) | 7 (11.6%) | 10 (5.4%) | |
TNBC | 4 (6.3%) | 11 (17.7%) | 16 (26.6%) | 31 (16.7%) | |
|
Local relapse | 8 (12.6%) | 5 (8.1%) | 7 (11.6%) | 20 (10.8%) |
Distant metastasis | 8 (12.6%) | 21 (33.8%) | 21 (35.0%) | 50 (27.0%) | |
|
85.9 (12–122) | 75.9 (0–123) | 74.1 (0–119) | 78.8 (0–123) |
Five to ten 5 µm thick sections were used from each case for RNA purification after assessment of cellularity on HE stained slides (minimum of 70% tumor cell content was required). RNA was extracted with Qiagen FFPE RNeasy kit according to the manufacturer's protocol (Qiagen, Venlo, The Netherlands). High Capacity RNA-to-cDNA kit was used to reverse transcribe 1000 ng of RNA (Applied Biosystems, Foster City, CA, USA). The Eppendorf epMotion 5070 robotic system was used to transfer samples and reagents to 384-well full-skirted white plates (Eppendorf, Hamburg, Germany). The qPCR was performed in duplicates with Taqman® Assays (
Flow cytometry was performed for the analysis of ploidy. Briefly, a 50 µm section was cut from all the FFPE blocks. A scroll of tissue was placed in a microcentrifuge tube and xylene was added to remove the paraffin wax. The tissue was then serially re-hydrated through 100%, 95%, 70% and 50% ethanol for 5 minutes respectively at room temperature. The tissue was washed twice with distilled water. A suspension of nuclei was made by incubating the tissue in a 0.5% pepsin solution (Sigma-Aldrich, Dorset, UK) prepared in 0.9% saline at pH 1.5. Incubation was carried out at 37°C for 30 minutes. The nuclei were washed once with PBS, stained with propidium-iodine and analyzed using the Calibur 1 FACS mashine and CellQuest software (Beckman Coulter, Buckinghamshire, UK). DNA index was assigned as follows: diploids were ‘1.0’, a tumor with a DNA index greater than 1.10 was classified as aneuploid
The assignment of each patient into two cohorts using the CIN4 expression signature was performed in the R statistical environment (R, version 2.10.1) using the package Prediction Analysis for Microarrays (PAM, version 2.19) as described previously
In order to select a more limited set of genes that optimally reflect the CIN70 signature we retrieved expression profiles for the CIN70 signature genes from 10 publicly available breast cancer datasets
The CIN70 genes were then ranked by Pearson's correlation coefficients to the CIN score (mean CIN70 expression) within these breast cancer microarray cohorts [supplementary references]. In order to derive a clinically applicable qPCR expression signature for use in FFPE tissue, containing fewer genes with equivalent information reflecting mean CIN70 expression
For 5 of the 10 breast cancer microarray cohorts histological grade was also available. The above listed 4 genes were also highly correlated with histological grade (Pearson correlation coefficient above 0.7).
Next, we assessed the association individually on data sets between the mean-expression level of CIN4 and clinical outcome across the same 10 cohorts containing expression data from 1928 breast cancers. We observed significant discriminative power (p<0.05, for all) by CIN4 for the stratification of good from poor clinical outcome in all of the breast cancer cohorts [supplementary references]. Therefore, the expression of CIN4 appears almost as efficient at predicting cancer outcome as the extended CIN25 and CIN70 signatures. We were able to compare the performance in silico of the CIN4 signature to a number of previously published predictors of outcome such as CIN25, CIN70
B4GALT3, SLC9A3R2 and PUM1 were previously chosen based on their low variability in gene expression datasets described previously
Expression of CIN4 was assessed in a retrospective cohort of 185 patients for which we had FFPE primary breast cancer samples available from Buda MÁV hospital, treated between 1999 and 2002 (
In order to define a threshold for CIN4 expression for distinct outcome groups, we trained the PAM algorithm using the continuous CIN4 gene expression signature to discriminate clinical outcome of 63 patients with grade 1 breast cancer compared to the poorer outcome associated with 60 grade 3 breast cancers from within this cohort of 185 patients. Using this CIN4 expression threshold that best distinguished grade 1 compared to grade 3 breast cancers, the PAM defined CIN4 score was established, and we assessed the ability of this CIN4 score to stratify cancer outcome in the remaining 62 patients with grade 2 breast cancer from this 185 patient cohort.
Using this threshold, the CIN4 score was able to stratify patients with grade 2 breast cancers into good (44 patients) and poor (18 patients) prognostic groups (mean, relapse-free survival: 83.8 months [95%CI: 73.6–94.2] and 69.4 months [95%CI: 55.1–90.6], respectively [p = 0.016], HR = 2.155[1.007–4.612]) (
A) The 4-gene signature based, PAM designated CIN4 score showing discrimination between grade 2 good and poor prognostic groups plotted on Kaplan-Meier curve in the validation group (p = 0.017): 45 cases (38 ER+, 7 ER−) in low CIN4 score group and 17 cases (10 ER+, 7 ER−) in high CIN4 score group. B) The CIN4 score performing in ER+ tumors only: 38 cases in low CIN4 score group and 10 cases in high CIN4 score group (p = 0.009).
For the identified genes that have been previously described to be of prognostic value, we have evaluated the prognostic power of AURKA, FOXM1, TOP2A, and TPX2 separately. Although, in public breast cancer datasets (grade 2, follow-up: 10 years, n = 497, split at median) all the genes showed strong predictive potential (AURKA: p = 0.047, HR = 1.33[1.00–1.78]; FOXM1: p<0.001, HR = 1.69[1.26–2.27]; TOP2A: p<0.001, HR = 1.68[1.26–2.25]; TPX2: p<0.001, HR = 1.64[1.23–2.18] for relapse-free survival (
We assessed the performance of the CIN4 score in ER+/HER2− tumors classified by immunohistochemistry. This cohort was also stratified into good and poor prognostic groups with reasonable confidence (p = 0.009, HR = 2.269[1.117–5.486],
We assessed tumor DNA index through flow cytometry, classifying cancers into aneuploid and diploid categories according to standard threshold (threshold = 1.10
A) Regression curve showing relation of CIN4 expression signature and DNA index (p = 0.036). B) Bar graph showing numbers of diploid and aneuploid cases grouped according to histological grade: grade 3 tumors are relatively enriched in aneuploid cancers.
Using regression analysis, considering all 185 patients, the CIN4 expression signature was significantly correlated with mitotic index (p = 0.001,
Relation of CIN4 and DNA index to A) and B) mitotic index, C) and D) Ki67 expression, E) and F) Nottingham Prognostic Index and G) and H) tumor size displayed with regression curves (coefficients and p-values on graphs).
Estrogen and progesterone receptor expression as measured by IHC inversely correlated with CIN4 expression (p = 0.012, p = 0.017, respectively,
CIN4 score and clinicopathological variables were tested for their correlation in grade 2 cancers (
(1a) | (1b) | (2a) | (2b) | (3a) | (3b) | |||||||||||||
p-value | HR | CI | p-value | HR | CI | p-value | HR | CI | p-value | HR | CI | p-value | HR | CI | p-value | HR | CI | |
|
0.820 | 0.87 | 0.36–3.64 | 0.991 | 0.10 | 0.32–3.13 | ||||||||||||
|
0.339 | 0.98 | 0.92–1.02 | 0.493 | 1.02 | 0.93–1.03 | ||||||||||||
|
0.816 | 0.96 | 0.65–1.39 | 0.914 | 0.98 | 0.70–1.47 | ||||||||||||
|
0.114 | 1.03 | 0.94–1.00 | 0.168 | 1.02 | 0.94–1.01 | ||||||||||||
|
0.714 | 1.20 | 0.31–2.18 | 0.799 | 1.13 | 0.34–2.27 | ||||||||||||
|
0.067 | 0.43 | 0.94–5.88 | 0.183 | 0.55 | 0.75–4.30 | ||||||||||||
|
0.430 | 1.07 | 0.78–1.10 | 0.625 | 1.04 | 0.81–1.13 | ||||||||||||
|
0.332 | 1.04 | 0.90–1.03 | 0.658 | 1.02 | 0.92–1.06 | ||||||||||||
|
0.847 | 1.00 | 0.98–1.03 | 0.903 | 1.00 | 0.98–1.03 | 0.690 | 1.01 | 0.97–1.03 | 0.919 | 1.00 | 0.97–1.02 | ||||||
|
0.028 | 0.38 | 1.11–6.21 | 0.011 | 0.33 | 1.28–6.99 | 0.041 | 2.41 | 1.03–5.59 | |||||||||
|
63 | 63 | 64 | 64 | 63 | 63 | ||||||||||||
|
0.624 | 0.500 | 0.664 | 0.609 | 0.611 | 0.501 | ||||||||||||
|
0.073 | 0.005 | 0.131 | 0.046 | 0.070 | 0.010 | ||||||||||||
|
0.950 | 0.950 | 0.953 | 0.953 | 0.950 | 0.950 | ||||||||||||
|
−91.920 | −94.155 | −93.619 | −96.598 | −92.009 | −93.973 | ||||||||||||
|
5.070 (df = 4) | 0.280 (df = 3) | 8.820 (df = 5) | 3.040 (df = 4) | 4.760 (df = 4) | 0.560 (df = 3) | ||||||||||||
|
4.760 (df = 4) | 0.289 (df = 3) | 8.988 (df = 5) | 3.028 (df = 4) | 4.580 (df = 4) | 0.653 (df = 3) | ||||||||||||
|
5.306 (df = 4) | 0.278 (df = 3) | 9.437 (df = 5) | 3.052 (df = 4) | 5.010 (df = 4) | 0.570 (df = 3) |
Multiple variables tested in separate runs when CIN4 was included (1–3a) or excluded (1–3b) from the comparison.
In this study, we developed a 4 gene-based measure of CIN applicable to FFPE material demonstrating clinical utility as a fairly robust marker of breast cancer prognosis. Aneuploidy determined by FACS-based DNA index correlated with the CIN4 signature, indicating that the CIN4 signature may in fact serve as a surrogate method to determine tumor aneuploidy status.
These data derived from a qPCR assessment of CIN4 mRNA expression from FFPE breast cancers, seems to confirm our previous analysis from microarray expression datasets
Directly or indirectly quantifying CIN in human tumor biopsies may hold significant potential for clinical practice
The CIN4 signature performed comparably to previously published gene signatures in similar cohorts. For example, the previously published Genomic Grade Index was further simplified and converted into a qPCR-based test from formalin-fixed, paraffin-embedded tissues
Interestingly, in the study we identified a group of samples with a diploid DNA index which have a wide range of CIN scores. There might be several explanations behind this phenomenon including tumor heterogeneity or the presence of yet unknown other compensatory mechanisms
While establishing a qPCR-based simple test to improve histological classification is an important clinical goal, – following further validation – the CIN4 signature as a quantifier of CIN is expected to serve as a potential marker of other clinical characteristics as well. Most prominently, the putative role of CIN in determining taxane response would suggest that the predictive role of CIN4 should be tested in the neoadjuvant setting when comparing response to therapy with or without taxanes in the treatment of hormone receptor negative breast cancer.
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The authors thank Márta Jäckel MD, PhD for providing the tissue blocks, the assistance of Erzsébet Azumah, Csilla Jaczó, Magdolna Pekár, and Erika Samodai in tissue handling. Distinguished thanks to Miklós Szendrői MD, PhD, DSc for both his personal and professional support to this project. Financial support: Legacy of Szőllős András Péterné, ETT-029/2009, ETT-088/2009, MKOT-GSK-2010, TÁMOP-4.2.1.B-09/1/KMR-2010-0001 and the TÁMOP-4.2.2/B-10/1-2010-0013 grant. B.G. was supported by the OTKA PD 83154 grant. C.S. was supported by the Breast Cancer Research Foundation (BCRF).