P1-07-16: Number Needed To Count: A Novel Model for Ki67 Assessment in Breast Cancer.
Authors: Bendahl P-O , Romero Q , Grabau D , Borgquist S 
Published: June 2012

Abstract
Background: Tumor proliferation assessed by Ki67 is frequently used as a prognostic and above all treatment predictive marker i breast cancer where a decrease in Ki67 expression, following neo-adjuvant therapy, may be interpreted as a reliable treatment effect. Previous studies have indicated that the optimal number of tumor cells needed to count may vary from case to case. The standard error of the estimated proportion will decrease with increasing number of tumor cells counted (n), but a dilution bias will affect the estimate in cases with few tumor cells within a hotspot area. Hence, a large n is appropriate in Ki67-homogeneous hotspot areas and inappropriate, at least for small hotspots, in case of heterogeneity. Further, a chosen cut-off value has implications for the choice of n. A low n may be sufficient for extreme proportions, but the closer the true unknown proportion is to the cutoff, the larger n will be required. The lack of consensus concerning Ki67 assessment may jeopardize the comparison of research results. Hence, a standardized counting model is warranted.
Material and Methods: Exact two-sided confidence intervals for proportions based on the binomial distribution were used to derive rejection regions for sequential testing of the null hypothesis that the fraction of Ki67-positive cells is equal to the chosen cut-off (20% in this study). A lower limit of 50 counted tumor cells to get a reasonably stable estimate and an upper limit of 400 tumor cells to prevent extreme dilution bias for small hotspots, were applied. Briefly, the counting strategy can be explained as follows: Locate a hotspot and count 50 tumor cells. If the Ki67 estimate belongs to the upper or lower rejection region, stop counting. If not, count another 10 tumor cells and perform a new hypothesis test. Proceed until either the null hypothesis has been rejected or the upper limit of 400 has been reached. Simulation was used to determine that a nominal significance level of α=0.01 for each test will keep the overall probability of falsely rejecting the null hypothesis fixed at 0.05. The novel counting strategy was compared to static counting of 200 tumor cells using 100 Ki67-stained breast cancer samples.
Results: The median number of tumor cells needed to count to determine Ki67-status was 100 and the average 175. The rejection region was reached immediately after 50 tumor cells counted for 32 samples with Ki67-levels far from the cut-off, whereas counting 400 tumor cells was insufficient for classification in another 18 samples. In samples classified as highly proliferative (&gt;20%), the mean Ki67-estimate was 49% using the counting model compared to 42% using a fixed denominator of 200 tumor cells. The largest absolute difference between the two estimates for these 32 samples was 23% — from 42% (model) to 19% (static), thus more than a factor two and a dilution effect leading to changed Ki67-status.
Conclusions: Estimation of the fraction of Ki67-positive tumor cells using a fixed denominator may be inadequate — especially for small hotspots. We hereby propose a strategy for tumor cell count optimization that hopefully will contribute to standardization of the counting practice for tumor proliferation assessed by Ki67.
Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-16.