BMC Cancer 2009, 9:182 http://www.biomedcentral.com/1471-2407/9/182 upon-Tyne, UK), Ki-67 (Clone SP6, Labvision NeoMark- ers, Fremont, CA), and HER2/neu 1 (rabbit polyclonal, DAKO Corporation, Carpinteria, CA). The basic staining procedure used an avidin-biotin-alkaline phosphatase method, modified for antigen retrieval from paraffin- embedded tissue using the procedure of Shi et al [36]. Fol- lowing overnight incubation with the primary antibodies at 4°C, tissue sections were sequentially incubated with a biotinylated secondary antibody and a streptavidin-alka- line phosphatase conjugate at 37°C for 20 minutes, respectively, (Biogenex, San Ramon, CA, USA) and then visualized using the chromogen/substrate Vector Red (Vector Laboratories, Burlingame, CA, USA). Sections were counterstained with Mayer's hematoxylin, dehy- drated, cleared through p-Xylene, and coverslipped. Appropriate positive and negative controls were included for each antibody. During staining, about 7–9% of the samples fell off the slides. The number was similar across epithelial markers, but twice as many benign as malignant samples were missing. When a trained pathologist (JMC) evaluated all stained specimens to confirm their malignancy status and the presence of epithelial tissue, 118 breast tissue samples were re-categorized as benign or malignant, i.e., the core had been taken from a malignant part of the block although the intent had been to get a benign sample or vice versa. Another 409–433 (depending on the marker) core sections with equivocal features, e.g., connective tis- sue or fat tissue only or bad dye quality, were excluded. As a result, at least one benign tissue sample was available for 159 women (mean = 1.7 specimens per woman). Quanti- fication of staining was done on individual TMA core sec- tions at a magnification of 20×, using a Nikon Labophot 2 microscope, a 3 megapixel digital camera (Infinity 2–3, Lumenera Inc., Ottawa, Ontario), and color imaging soft- ware (Image Pro Plus, Media Cybernetics, Bethesda, MD). The area of all nuclei in the core section was measured by a color selection corresponding to hematoxylin (A). The area of positively immunostained tissue was measured by a color selection corresponding to the Vector Red chro- mogen (B). For the nuclear stains used in this study, the percentage of staining was expressed as B/A × 100; results were averaged for subjects with several cores. Mammographic density assessment Breast density readings used for the present study had all been obtained previously as part of the NCC study [33]. All mammographic films were scanned with a Kodak LS85 Film Digitizer (absorbance range, 0.001–4.1; East- man Kodak Company, Rochester, New York) at a resolu- tion of 98 pixels per inch. One of the authors (GM) performed computer-assisted density assessment with the Cumulus package [37]. After the reader determined a threshold for the edge of the breast and the dense tissue, the computer computed the number of pixels that consti- tuted the total and the dense area and the ratio between the two, i.e., percent breast density. To convert the pixels of the area into cm2, a factor of 0.000676 was used. The intraclass correlation coefficients (ICC) to assess reliabil- ity were 0.96 (95% confidence interval (CI): 0.95, 0.97) and 0.996 (95% CI: 0.995, 0.997) for the size of the dense and the total breast area, respectively. This resulted in an ICC of 0.974 for percent density (95% CI: 0.968, 0.978). For the present study, the cranial caudal view closest to, but before, breast cancer diagnosis was selected; the mean time between the two dates was 10.0 ± 14.8 months. Statistical analysis SAS statistical software package version 9.1 was used for all analyses (SAS Institute Inc., Cary, NC). The dense breast area was square root transformed to normalize the distribution. For ease of interpretation, back transformed mean values are presented. For all histology markers, the mean percentage of stained cells of all available cores per sample was calculated. To assess marker agreement by subject, ICCs were computed. For all six markers, the dis- tributions of samples were skewed with strong left tails. The interquartile ranges were 0.0–8.1%, 0.0–13.8%, 0.0– 1.5%, 0.0–14.5%, 0.0–0.6%, 0.8–16.2% for ERα, ERβ, PR, HER2, Ki-67, and PCNA, respectively. Therefore, sam- ples were divided into two categories; negative staining (<10% of cells stained) and positive staining (≥ 10% of cells). Because the number of women with positive stain- ing for PR and Ki-67 was very low (4 and 13 respectively), we dichotomized the results into no vs. any epithelial staining. Linear regression models were used to analyze the association between histological markers as categori- cal variables and mammographic density (absolute den- sity and percent density) as continuous variables. Associations were adjusted for variables previously shown to be associated with mammographic density including age at mammogram, body mass index (BMI), ethnicity, age at menarche, parity, age at first live birth, HT use at mammogram, menopausal status, and family history of breast cancer. Separate analyses stratified by ethnicity (Caucasian and Japanese) and by total size of the breast with the median as cutpoint were also performed. Results Women in the TMA study were slightly younger, were more likely premenopausal, and had a lower BMI than in the original NCC study, but were otherwise similar (Table 1). The study population included 49 Caucasians, 70 Jap- anese, 21 Native Hawaiians, and 19 women of other eth- nicities. Mean total breast area as measured on the mammograms was largest for Caucasian women and smallest for Japanese women (135 and 91 cm2, respec- tively). BMI and total breast area were strongly correlated 0.52 (p < 0.0001). Absolute mammographic density was Page 3 of 8 (page number not for citation purposes)