Improving Prognosis of Surrogate Assay for Breast Cancer Patients by Absolute Quantitation of Ki67 Protein Levels Using Quantitative Dot Blot (QDB) Method.

Abstract

BACKGROUND:

Immunohistochemistry (IHC)-based surrogate assay is the prevailing method in daily clinical practice to determine the necessity of chemotherapy for Luminal-like breast cancer patients worldwide. It relies on Ki67 scores to separate Luminal A-like from Luminal B-like breast cancer subtypes. Yet, IHC-based Ki67 assessment is known to be plagued with subjectivity and inconsistency to undermine the performance of the surrogate assay. A novel method needs to be explored to improve the clinical utility of Ki67 in daily clinical practice.

MATERIALS AND METHODS:

The Ki67 protein levels in a cohort of 253 specimens were assessed with IHC and quantitative dot blot (QDB) methods, respectively, and used to assign these specimens into Luminal A-like and Luminal B-like subtypes accordingly. Their performances were compared with the Kaplan-Meier, univariate, and multivariate survival analyses of the overall survival (OS) of Luminal-like patients.

RESULTS:

The surrogate assay based on absolutely quantitated Ki67 levels (cutoff at 2.31 nmol/g) subtyped the Luminal-like patients more effectively than that based on Ki67 scores (cutoff at 14%) (Log rank test, p = 0.00052 vs. p = 0.031). It is also correlated better with OS in multivariate survival analysis [hazard ratio (HR) at 6.89 (95% CI: 2.66-17.84, p = 0.0001) vs. 2.14 (95% CI: 0.89-5.11, p = 0.087)].

CONCLUSIONS:

Our study showed that the performance of the surrogate assay may be improved significantly by measuring Ki67 levels absolutely, quantitatively, and objectively using the QDB method.

• Authors: Junmei Hao, Yan Lyu, Jiarui Zou, Yunyun Zhang, Shuishan Xie, Lili Jing, Fangrong Tang, Jiahong Lyu, Wenfeng Zhang, Jianbo Zhang, Xunting Wang, Kuisheng Chen, Jiandi Zhang
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 737781 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 34604077 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Hao, Lyu, Zou, Zhang, Xie, Jing, Tang, Lyu, Zhang, Zhang, Wang, Chen and Zhang.