title: Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors.
abstract: OBJECTIVE
  The purpose of this study was to determine whether comprehensive transcriptional profiles (TPs) can be obtained from single-passage fine-needle aspirations (FNAs) of breast cancer and to explore whether profiles capture routine clinicopathological parameters.
  
  
  METHODS
  Expression profiles were available on 38 patients with stage I-III breast cancer who underwent FNA at the time of diagnosis. [(33)P]dCTP-labeled cDNA probes were generated and hybridized to cDNA membrane microarrays that contained 30,000 human sequence clones, including 10,890 expressed sequence tags.
  
  
  RESULTS
  The median total RNA yield from the biopsies was 2 micro g (range, 1-25 micro g). The cellular composition of each biopsy was examined and, on average, 79% of the cells were cancer cells. TP was successfully performed on all 38 of the biopsies. Unsupervised complete-linkage hierarchical clustering with all of the biopsies revealed an association between estrogen receptor (ER) status and gene expression profiles. There was a strong correlation between ER status determined by TP and measured by routine immunohistochemistry (P = 0.001). A similar strong correlation was seen with HER-2 status determined by fluorescent in situ hybridization (P = 0.0002). Using the first 18 cases as the discovery set, we established a cutoff of 2.0 and 18.0 for ER and HER-2 mRNA levels, respectively, to distinguish clinically-negative from -positive cases. We also identified 105 genes (excluding the ER gene) the expression of which correlated highly with clinical ER status. Twenty tumors were used for prospective validation. HER-2 status was correctly identified in all 20 of the cases, based on HER-2 mRNA content detected by TP. ER status was correctly identified in 19 of 20 cases. When the marker set of 105 genes was used to prospectively predict ER status, TP-based classification correctly identified 9 of 10 of the ER-positive and 7 of 10 of the ER-negative tumors. We also explored supervised cluster analysis using various functional categories of genes, and we observed a clear separation between ER-negative and ER-positive tumors when genes involved in signal transduction were used for clustering.
  
  
  CONCLUSIONS
  These results demonstrate that comprehensive TP can be performed on FNA biopsies. TPs reliably measure conventional single-gene prognostic markers such as ER and HER-2. A complex pattern of genes (not including ER) can also be used to predict clinical ER status. These results demonstrate that needle biopsy-based diagnostic microarray tests may be developed that could capture conventional prognostic information but may also contain additional clinical information that cannot currently be measured with other methods.
authors.affiliation: Departments of Breast Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030-009, USA. lpusztai@mdanderson.org
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
authors.forename: Mark
authors.initials: M
authors.lastname: Ayers
authors.forename: James
authors.initials: J
authors.lastname: Stec
authors.forename: Edward
authors.initials: E
authors.lastname: Clark
authors.forename: Kenneth
authors.initials: K
authors.lastname: Hess
authors.forename: David
authors.initials: D
authors.lastname: Stivers
authors.forename: Andrew
authors.initials: A
authors.lastname: Damokosh
authors.forename: Nour
authors.initials: N
authors.lastname: Sneige
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Buchholz
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Esteva
authors.forename: Banu
authors.initials: B
authors.lastname: Arun
authors.forename: Massimo
authors.initials: M
authors.lastname: Cristofanilli
authors.forename: Daniel
authors.initials: D
authors.lastname: Booser
authors.forename: Marguerite
authors.initials: M
authors.lastname: Rosales
authors.forename: Vicente
authors.initials: V
authors.lastname: Valero
authors.forename: Constantine
authors.initials: C
authors.lastname: Adams
authors.forename: Gabriel N
authors.initials: GN
authors.lastname: Hortobagyi
authors.forename: W Fraser
authors.initials: WF
authors.lastname: Symmans
mesh_terms: D000328:Adult; D000368:Aged; D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000378:metabolism*; D016000:Cluster Analysis; D018076:DNA, Complementary / Q000378:metabolism; D004967:Estrogens / Q000378:metabolism*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007150:Immunohistochemistry; D017404:In Situ Hybridization, Fluorescence; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012313:RNA / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000096:biosynthesis; D011960:Receptors, Estrogen / Q000096:biosynthesis; D015398:Signal Transduction; D013997:Time Factors
chemical_list: D018076:DNA, Complementary; D004967:Estrogens; D012313:RNA; D012333:RNA, Messenger; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen