Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer

Correlation of FANCM expression with clinical factors in luminal B breast cancer

BACKGROUND

The genotype of Fanconi Anemia complementation group M (FANCM) was previously found to be associated with breast cancer risk in several populations. Here, we studied the expression of FANCM and its correlation with clinical characteristics in Chinese patients with breast cancer.

METHODS

We performed an immunohistochemical study of FANCM protein in clinical breast cancer tissues from 310 patients along with 44 adjacent tissues.

RESULTS

FANCM protein level is lower in triple-negative breast cancer tissues than in other subtypes (P = 0.008). In addition, high FANCM expression correlated with pathology type IDC (P = 0.040), estrogen receptor positive (P < 0.001), progesterone receptor positive (P = 0.001), and low Ki-67 status (P = 0.003). Multivariate analysis revealed that FANCM status was an independent prognostic factor for overall survival (P = 0.017) in luminal B breast cancer.

CONCLUSIONS

FANCM levels are significantly associated with different subtypes of human breast cancer. Specifically, FANCM could play a role in the progression of luminal B breast cancer.

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.

Ki-67 Expression by Immunohistochemistry and Quantitative Real-Time Polymerase Chain Reaction as Predictor of Clinical Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer

Background

Chemotherapy has become a standard of treatment in managing breast cancer. To achieve proper treatment for the right patients, the predictive marker is needed. Ki-67 is a biomarker of proliferation for solid tumor. Studies mentioned association of Ki-67 expression with chemotherapy response. The study aims are to evaluate whether Ki-67 expression detected by immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) may predict clinical response to neoadjuvant chemotherapy in breast cancer.

Methods

This study utilized a longitudinal study. IHC and qRT-PCR methods were used for detection of Ki-67 expression. Chemotherapy response was calculated using RECIST. Data were analyzed with Chi-square and Wilcoxon's test.

Results

There were 48 subjects in this study. Analysis of Ki-67 expression with chemotherapy response has a significant correlation with p = 0.025 (<0.05), OR: 1.69, confidence interval (95% CI) 1.022–2.810. Analysis of Ki-67 mRNA expression with chemotherapy response has a significant correlation p = 0.002 (<0.05), OR: 6.85, confidence interval (95% CI) 1.064–44.193. Detection of Ki-67 expression using IHC and qRT-PCR has similar results, p = 0.012 (<0.05).

Conclusion

These results suggest that Ki-67 expression detected by both IHC and qRT-PCR is considered to be a predictor of clinical response to neoadjuvant chemotherapy in locally advanced breast cancer.