Abstract PD06-01: Automated computational Ki67 scoring in the GeparTrio breast cancer study cohort

Background: Scoring proliferation through Ki67-immunohistochemistry is an important component in predicting therapy response to chemotherapy in breast cancer patients. Therefore, an accurate and standardized Ki67-scoring is pivotal both in routine diagnostics and larger multi-center studies aiming at improving present or establishing new cut-off values for existing or novel therapy regimens. However, recent studies have cast some doubt on the reliability of “visual” Ki67 scoring by pathologists, especially within the lower - yet clinically important - proliferation range. Here, we present and apply a novel automated image analysis approach for Ki67-quantification in breast cancer tissue.

Methods: We perform automated Ki67-scoring in 1219 breast cancer patients from the GeparTrio study cohort using a novel image analysis approach that avoids detection biases due to morphological variability by using a generic minimum-model approach. The method is capable of tumor-stroma-separation and may be used to process large data sets fully unsupervised in batch mode while allowing for efficient visual checks of the results. We compare these results with a different in-house-developed subtiling-based automated quantification method and moreover, gauge our approach with manual scoring performed by pathologists.

Results: The results show deviations of 10% (automated method 1 vs. manual), 9% (automated method 2 vs. manual) and 3% (automated method 1 vs. automated method 2) on average. The Ki67 scores show Pearson correlations between automated and manual scoring of r>0.8 (p < 0.001) for both automated methods and r>0.95 (p < 0.001) between the two tested automated methods.

Conclusion: Because of the methodological differences of the presented techniques our results suggest a high robustness of the automated methods that at the same time show a good agreement with manual Ki67 scoring. Our approach therefore offers an automated and standardized means of Ki67 quantification applicable in routine diagnostics as well as larger clinical study settings, such as in the GeparTrio cohort shown here.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr PD06-01.

Abstract S4-5: Ki67 levels in pretherapeutic core biopsies as predictive and prognostic parameters in the neoadjuvant GeparTrio trial

Background: Ki67 has been suggested as a marker for definition of luminal A and luminal B tumors by the 2011 St. Gallen consensus panel. However, the cutoffs for Ki67 are still under debate. In particular, it is not clear if one single cutoff is useful for prognostic and predictive information in the different molecular subtypes. It is an advantage of the neoadjuvant approach that predictive and prognostic outcome measurements can be separated in the same cohort. In this study, we evaluated a large cohort of core biopsies from the neoadjuvant GeparTrio trial to investigate the impact of pretherapeutic Ki67 levels as a predictive marker for response to neoadjuvant chemotherapy as well as a prognostic marker for progression-free and overall survival. The analysis was stratified for hormone-receptor positive and negative tumors as well as HER2 status.

Methods: A total of 1166 pretherapeutic core biopsies from the neoadjuvant Gepartrio trial were evaluated for Ki67 by immunohistochemistry, a total of 200 cells were counted in each sample. Ki67 cutoffs were evaluated using web-based software Cutoff Finder (http://molpath.charite.de/cutoff/). The details of the GeparTrio study design have been described before (von Minckwitz, JNCI 2008). We compared pCR rate as well as the overall and disease free survival in the complete cohort as well as subgroups of patients based on hormone receptor and HER2 expression.

Results: Using Ki67 as a continuous parameter, a wide range of cutoffs between 10% and 80% for Ki67 were predictive for pCR. For DFS and OS, a wide range of cutoffs between 10% and 45% was significant. For further analysis, the three groups of Ki67 0–15% vs. Ki67 15.1%–35% vs. Ki67 >35 were defined and were compared for different outcome parameters. The pCR rates in these three groups of Ki67 expression were 4.2%, 12.9% and 29.0% (p < 0.0005). For DFS and OS, the groups were significantly linked to prognosis in univariate and multivariate analysis. A detailed subgroup analysis was performed showing that Ki67 was significantly predictive for pCR in all molecular subgroups. However, in subgroup survival analysis, Ki67 was prognostic in luminal, but not in triple-negative tumors.

Conclusion: Ki67 is a valid predictive and prognostic marker in breast cancer. This marker is significant over a wide range of different cutoffs, which explains the different results of Ki67 cutoffs in different previous studies. Therefore, the variability observed in different studies evaluating Ki67 might reflect A) the wide range of valid cutoffs B) the different clinical endpoints of the studies and C) the different contribution of the molecular subtypes in the study cohorts. Based on our results we suggest three subgroups for Ki67 (0–15% vs. 15.1–35 vs. >35%) as a reasonable approach for further standardization of this marker.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr S4-5.