Breast Cancer Res Treat (2008) 107:249–257 The slides were scored manually by two independent pathologists, blinded to the clinical outcome, as previously described [11]. Total number of stained MCs in each core was recorded. ER status was assessed using the rabbit monoclonal antibody (SP1) antibody [18]. The fractions of ER positive tumor nuclei were scored as 0 (<1%), 1 (1–25%), 2 (25–75%), and 3 (>75%). Her2 SP3 rabbit monoclonal from LabVision (NeoMarkers) was used to stain the TMA slides and were scored using Hercept test (Dako Corporation, Carpinteria, CA) scoring system. Final Her2 score was derived using both IHC and Fluorescent in situ hybridization (FISH) assays. Cases with Her2 IHC Herceptest score = 3, were scored as positive. Those cases with Her2 IHC Herceptest score = 2 were re-evaluated using FISH assay, and only those cases with Her2 FISH ampliﬁcation ratio ‡2.0 were scored as Her2 positive. C-Kit and ER scores were binarized for statistical analysis as follows: CD-117: 0 = no MCs; 1 = any MCs; ER: 0 £ 1% nuclei stained; 1 ‡ 1% nuclei stained. We excluded cases for which it was not possible to assign a score to the immunostaining (insufﬁcient invasive tumor in the core, or missing core). Statistical analysis The raw scores were entered into an Excel database and each TMA core was assigned a unique core ID number. The spreadsheet was then processed utilizing TMA- Deconvoluter 1.06 software that had been adapted for TMA analysis [19]. A database was created by incorpora- tion of all deconvoluted marker data into the clinicopath- ologic patient database. We applied a split-sample validation technique for our statistical analysis. Our 4,444 patient cohort was strati- ﬁed into eight subgroups (Table 2) based on adjuvant treatment received and then randomized into equal sized training and validation sets. The two groups were bal- anced with respect to treatment received; but there are also no signiﬁcant differences in clinical or pathological variables, including age, tumor size and grade, nodal status, and ER status. For this study, and future studies using this TMA, the primary investigator is given access to all clinical, outcome, and TMA data from the training set only. The training set is used to generate and reﬁne hypotheses regarding the biomarker under study. Signif- icant ﬁndings are then formally presented at a bimonthly joint scientiﬁc group meeting of the Genetic Pathology Evaluation Centre (GPEC, a collaborative group of scientists and pathologists) and Breast Cancer Outcomes Unit (BCOU, a group of oncologists and epidemiologists based at the BC Cancer Agency). Those ﬁndings 251 considered to be of clinical and scientiﬁc interest are then re-tested on the validation set. A separate researcher who did not participate in the training set analysis performs the re-testing on the validation set. Our statis- tical approach is intended to minimize false positive results, particularly with subgroup analysis. Statistical analysis was performed using SPSS 14.0. In univariate analysis, breast-cancer speciﬁc survival (BCSS) was estimated using Kaplan–Meier (KM) curves, and sig- niﬁcant differences determined by Log rank tests. For BCSS, survival time was censored at the date of death if the cause was not breast cancer or at the date of last follow- up if the patient was still alive at the end of the study period. 6 patients with unknown cause of death were excluded from BCSS analysis. Cox proportional hazards models were used to calculate adjusted hazard ratios accounting for covariates. Kendall’s tau-b and the Mann-Whitney tests were used to measure the correlation of c-kit status to pathological variables. All statistical tests were two-sided. The Bonferroni correction for multiple comparisons was applied during validation set analysis, and the alpha value for each comparison is 0.05/n, where n is the total number of comparisons. X-tile analysis We also used X-tile software [20] to ﬁnd the optimal cut-off point for the total number of MCs that will predict prognosis in breast cancer patients. X-tile program split the cohort randomly into a matched training and validation set as a method for selecting optimal cut-points. It than calculated a P value for every possible division of the cohort expression data. A two-dimensional graph with its corresponding survival curves was plotted where each colored pixel was proportional to is v2 Value. The program automatically calculated the maximum v2 value which served as a cut-point to separate the number of MCs that predicted prognosis. Results Out of 4,620 cases on the TMAs, we selected 4,444 breast cancers that showed invasive tumor in the cores. The cli- nico-pathologic characteristics of patients included in the study are depicted in Table 2. The total number of stained MCs was recorded as a continuous variable with counts ranging from 0 to 24 MCs per core. They were seen as 4–20 lm round to oval mononuclear cells with granular cytoplasm and single oval nucleus. The cytoplasmic granules were ganglion-, net-, or crystal-shaped (Fig. 1). 123