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A comparison of Ki-67 counting methods in luminal Breast Cancer: The Average Method vs. the Hot Spot Method.

ABSTRACT: In spite of the usefulness of the Ki-67 labeling index (LI) as a prognostic and predictive marker in breast cancer, its clinical application remains limited due to variability in its measurement and the absence of a standard method of interpretation. This study was designed to compare the two methods of assessing Ki-67 LI: the average method vs. the hot spot method and thus to determine which method is more appropriate in predicting prognosis of luminal/HER2-negative breast cancers. Ki-67 LIs were calculated by direct counting of three representative areas of 493 luminal/HER2-negative breast cancers using the two methods. We calculated the differences in the Ki-67 LIs (?Ki-67) between the two methods and the ratio of the Ki-67 LIs (H/A ratio) of the two methods. In addition, we compared the performance of the Ki-67 LIs obtained by the two methods as prognostic markers. ?Ki-67 ranged from 0.01% to 33.3% and the H/A ratio ranged from 1.0 to 2.6. Based on the receiver operating characteristic curve method, the predictive powers of the KI-67 LI measured by the two methods were similar (Area under curve: hot spot method, 0.711; average method, 0.700). In multivariate analysis, high Ki-67 LI based on either method was an independent poor prognostic factor, along with high T stage and node metastasis. However, in repeated counts, the hot spot method did not consistently classify tumors into high vs. low Ki-67 LI groups. In conclusion, both the average and hot spot method of evaluating Ki-67 LI have good predictive performances for tumor recurrence in luminal/HER2-negative breast cancers. However, we recommend using the average method for the present because of its greater reproducibility.

SUBMITTER: Jang MH

PROVIDER: S-EPMC5302792 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionKi-67 labeling index assessed by immunohistochemical assays has been shown useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers (BC) and distinguishing Luminal A-like from Luminal B-like tumors. We aimed to assess the performance of the Ventana CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody.MethodsWe constructed a case-cohort design based on a random sample (n?=?679) of all patients operated on for a first primary, non-metastatic, ER-positive, HER2-negative BC at the European Institute of Oncology (IEO) Milan, Italy during 1998-2002 and all additional patients (n?=?303) operated during the same period, who developed an event (metastasis in distant organs or death due to BC as primary event) and were not included in the previous subset. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of event according to Ki-67 (30-9) and derived intrinsic molecular subtype, using previously defined cutoff values, i.e., respectively 14% and 20%.ResultsKi-67 was?<?14% in 318 patients (32.4%), comprised between 14 and 19% in 245 patients (24.9%) and???20 in 419 patients (42.7%). At multivariable analysis, the risk of developing distant disease was 1.88 (95% CI 1.20-2.93; P?=?0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93-4.84; P?<?0.0001) for those with Ki-67???20% compared to those with Ki-67?<?14%. Patients with Luminal B-like BC had an approximate twofold risk of developing distant disease (HR?=?1.91; 95% CI 1.35-2.71; P?=?0.0003) than patients with Luminal A-like BC defined using Ki-67 (30-9).ConclusionsKi-67 evaluation using the 30-9 rabbit monoclonal primary antibody was able to stratify patients with ER-positive HER2-negative BC into prognostically distinct groups. Ki-67 assessment, with strict adherence to the international recommendations, should be included among the clinically useful biological parameters for the best treatment of patients with BC.

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A comparison of Ki-67 counting methods in luminal Breast Cancer: The Average Method vs. the Hot Spot Method.

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