Introduction
Chemotherapy given as adjuvant therapy after surgery to patients with primary operable breast cancer reduces the subsequent risk of relapse and death [
1]. Although the absolute reduction in mortality is only about 10–12%, most patients now receive adjuvant chemotherapy because it is not possible to identify, at the start of treatment, which patients might gain benefit. Our approach to this problem has been to give the chemotherapy before surgery (neoadjuvant therapy) to use the response in the primary tumor as a surrogate marker of subsequent survival benefit [
2]. Predictions of response have been attempted on the basis of tumor expression of proliferation and apoptosis markers [
3], endocrine and growth factors, and oncogenes [
4]. However, no single tumor marker has been shown to possess a sufficient predictive value to render it clinically useful. To achieve a greater predictive value, multiple markers need to be examined and correlated with the response of tumor cells to chemotherapy. The development of cDNA microarray technology has provided such an opportunity. With this technology it has been possible to identify new classes in breast cancer according to their gene expression patterns and to correlate them with distinct clinical outcomes [
5,
6]. We have demonstrated previously that RNA amplification is suitable and reliable for the determination of gene expression profiles by using small amounts of RNA from core biopsies [
7].
Our goal in this project was to determine the feasibility of obtaining representative cDNA expression array profiles from fine needle aspirations (FNAs) performed on breast carcinomas, which are known to have substantial heterogeneity after RNA amplification. We also evaluated the correlation between such profiles and subsequent clinical responses after neoadjuvant chemotherapy to determine whether this might provide a useful approach to response prediction for testing in future prospective studies.
Patients and methods
Patients
All patients provided informed consent before any procedures. To evaluate the sensitivity and reproducibility of the FNA technique, repeat presurgical FNA samples were taken from six patients. For two patients in this group, corresponding surgical specimens were available. Additionally, a group of 10 patients with breast cancer, who were treated with neoadjuvant chemotherapy, were evaluated by using three FNAs, two performed one week apart and before chemotherapy (adriamycin 60 mg/m2 and cyclophosphamide 600 mg/m2), and the third on day 21 after the first cycle of chemotherapy. A 23-gauge needle (Oncotech Inc, Irvine, California, USA) was used for FNA in all cases.
RNA sample and cDNA preparation
From each aspirate a single-cell suspension was made in 2.5 ml of normal saline. Each sample was snap-frozen in liquid nitrogen and stored at -80°C. All FNA samples contained more than 50% tumor cells as assessed by a cytological examination of cytospin. The phenol/chloroform procedure (Trizol
®; Gibco, Grand Island, New York, USA) was used to extract total RNA from each FNA sample. Total RNA isolated from the MCF10A human mammary epithelial immortalized cell line served as a common reference. Two rounds of Eberwine's RNA amplification procedure were performed, with minor modifications, with total RNA from tumor FNA specimens and the MCF10A human mammary epithelial immortalized cell line as described previously [
8]. Labeled cDNAs were prepared from 3 g of amplified RNA, hybridized onto a 7600-feature glass cDNA microarray (NCI) and scanned with a 10 m resolution GenePix 4000 scanner (Axon Instruments, Inc, Foster City, California, USA) as described elsewhere [
9].
Data analysis
Expression profiles were analyzed with BRB Array tools, version 1.03 (Molecular Statistics and Bioinformatics Section, National Cancer Institute, Bethesda, Maryland, USA). Only spot-filtered data were considered for the analysis as described in the Supplementary Material. To compare gene expression profiles generated from each FNA-FNA and FNA-surgical specimen pair, we performed an unsupervised hierarchical cluster analysis [
10]. A compound covariate predictor algorithm was applied to find a set of predictors for classifying each patient into the two predetermined classes (good responders versus bad responders) [
11]. A cross-validated approach was performed to validate the class prediction: one patient was removed; the classifier was trained on the remaining patients and then tested for its ability to classify the withheld patient. To estimate the probability of obtaining the predicting membership purely by chance we performed random groupings of patients and we calculated the number of misclassifications (see Supplementary Material).
Real-time quantitative RT-PCR
Gene expression was measured with the GeneAmp 5700 Sequence Detector (Applied Biosystems, Foster City, California, USA). Primers and probes (BioServe Biotechnologies, Laurel, Maryland, USA) were designed with Primer Express software (see Supplementary Material) (Applied Biosystems). The following mRNAs were evaluated: CD44, HMG1, COX17, and ACTB as a normalizing control. Gene expression in each patient sample was then compared with expression in the reference cell line, MCF10A.
Discussion
This study is the first to test the feasibility of obtaining representative array profiles from FNAs of breast carcinomas in a clinical setting. Amplification of tumor RNA from FNAs was used to achieve adequate signal. Our results demonstrate that when amplified RNA is used as a template, array profiles derived from an FNA of a given tumor are highly reproducible and representative. In addition, the molecular profiles generated from FNAs of different breast tumors can be distinguished from one another. Although the profiles derived from the surgical specimen and the corresponding FNAs showed discernible differences, these discrepancies are in the same range as the histologic differences between FNAs and surgical specimens [
2]. Specifically, FNAs are enriched for the less adherent carcinoma cells and reduced in the stromal components in comparison with the intact tumor. Therefore, the profile of FNA samples might be more representative of the biology of the pure cancer cells because the stromal components have been removed. Most importantly, however, the composite expression profiles remain intact with repeat FNAs, such that comparisons between FNAs can be used to distinguish between different forms of breast tumor.
Our results also suggest that gene expression profiles obtained before and changes after one cycle of chemotherapy correlate with response to treatment. In the analysis of the pretreatment expression profiles, we identified 37 genes differentially expressed in pretreatment samples that together could segregate excellent responders from the poorer responders. Although the 'leave one out' cross-validation approach that we performed with the profiles from the pretreatment samples suggested that these 37 genes could correctly classify all patients, our numbers are small and the results should therefore be considered exploratory. Nevertheless, the comprehensive nature of array analysis permits the framing of hypothetical gene sets even with small sample sizes that can be validated with an independent and larger set of breast cancer patients in a prospective manner.
The change in gene expression as measured by the number of outliers when comparing the pretreatment and post-treatment samples seems to be more substantial in the responder than the non-responder group. This might be expected because changes in expression should be associated with the more effective cellular intervention. Thus, responders and non-responders could be differentiated both by specific gene changes and by the quantity of the change in expression.
An additional benefit of studying gene expression profiles is the identification of genes and/or gene pathways, which might associate with the response or intrinsic resistance to chemotherapy. Examination of the subset of genes identified before any treatment (Fig.
2a) revealed that these 'discriminators' are involved in a variety of cellular functions. This list included transcription factors (
ERG and
NFYB), oncogene (
DLC1), growth factor receptors (
KIT), genes involved in the ubiquitin-proteasome pathway (
UBPH) and DNA repair and cell death regulators (
HMG1,
COX 17,
PAPPA,
BCL2-like2,
GADD34,
RPL27 and
CD44).
One of these genes,
HMG1 (for high-motility group protein 1), was more highly expressed in responders. This gene encodes a structure-specific HMG-domain protein that is evolutionarily conserved. HMG1 binds preferentially to cruciform DNA, cisplatin-modified DNA, and other distorted structures. Overexpression of HMG1 has been shown to sensitize cells to cisplatin and carboplatin by shielding its major DNA adducts from nucleotide excision repair [
12]. Depleting HMG1 and HMG2 from cell extracts by immunoprecipitation enhances the excision repair of cisplatin-modified DNA. Furthermore, introducing HMG2 by transfection enhances the cisplatin sensitivity of a lung adenocarcinoma cell line [
13]. Thus, overexpression of HMG1 in the group of good responders might confer an increased sensitivity to the cytotoxic effect of chemotherapy.
Another interesting finding was the upregulated expression of
COX17 (for cytochrome
c oxidase assembly protein) in the good responders group. This gene encodes a copper-binding chaperone for cytochrome
c oxidase (COX), the terminal enzyme of the mitochondrial respiratory chain. Dysfunction of the mitochondrial respiratory chain has been associated with apoptosis induction [
14]. Interestingly, one of the cytotoxic mechanisms of doxorubicin is to inhibit COX activity [
15]. Additionally, decreased COX expression was observed in doxorubicin-resistant leukemia K562 cells [
16]. Thus, the overexpression of this cytochrome oxidase would be consistent with the association with better responders.
Finally, CD44, a main cell-surface receptor for hyaluronic acid, was downregulated in the group of good responders. CD44 is involved in matrix adhesion, lymphocyte activation, and the regulation of tumor proliferation. Several reports have shown an association between decreased CD44 expression and facilitation of apoptosis [
17]. One of the mechanisms of this activity is thought to be the CD95/FAS-triggered induced shedding of CD44. Clinically, a decrease in CD44v6 expression has also been linked to a better response of neoadjuvant anthracycline-based treatment in cervical cancer [
18].
Similar observations could also be made about genes whose expression was altered after one cycle of chemotherapy. For example,
Cdk9, which encodes a catalytic subunit of TAK (cyclin T1/P-TEFb) and has been shown to have an antiapoptotic function during monocyte differentiation [
19], was induced in the group of poor responders after the first treatment. TIMP-1, a collagenase inhibitor that was downregulated in the good responders, has been reported to control the cell growth phenotype during breast cancer development in an autocrine and paracrine manner [
20]. Finally,
DCTD, a gene that transcribes dCMP deaminase and is associated with increased resistance to cytarabine [
21], was also induced in the poor responders. Taken together, these results indicate that the induction of genes associated with cellular resistance to chemotherapy seems to be associated with a poor clinical response, whereas a marker of cell growth seems to be downregulated in the good responders after one cycle of chemotherapy.
Our results indicate the complexity of genetic alterations involving various molecular pathways that might be associated with intrinsic and/or acquired resistance to chemotherapy in breast cancer. However, these expression profiles could represent a distinct signature for drug resistance in this particular small group of breast cancer patients. Interestingly, we have found that the dynamic changes after a single cycle of chemotherapy might further distinguish good responders from poor responders. The small numbers in our study preclude the definitive assignment of association genes with response, but these results have identified a significant list of plausible candidate markers that should be validated in larger clinical data sets.
In summary, our study demonstrates that expression profiles can be obtained in a reproducible manner from the small amount of RNA obtained from breast tumor FNAs. Moreover, we provide a proof of principle that profiles derived before treatment and changes in profiles shortly after starting treatment might have the potential to predict clinical outcomes from anthracycline-based chemotherapy in individual patients. Furthermore, using this approach we might be able to identify candidate genes and pathways, which could be involved in intrinsic chemoresistance and could prove to be useful in identifying targets for the treatment of drug-resistant carcinomas.