4.5 Article

Optimal Ki67 cut-off for luminal breast cancer prognostic evaluation: a large case series study with a long-term follow-up

Journal

BREAST CANCER RESEARCH AND TREATMENT
Volume 157, Issue 2, Pages 363-371

Publisher

SPRINGER
DOI: 10.1007/s10549-016-3817-9

Keywords

Ki67 cut-offs; Prognostic index; Luminal breast cancer; Relapse

Categories

Funding

  1. Rete Oncologica Piemonte e Valle d'Aosta
  2. LILT-Lega Italiana per Lotta contro i Tumori, local section of Turin (HealthResearch)

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Although Ki67 index suffers from poor reproducibility, it is one of the most important prognostic markers used by oncologists to select the treatment of estrogen receptor (ER) positive breast cancer patients. In this study, we aim to establish the optimal Ki67 cut-offs for stratifying patient prognosis and to create a comprehensive prognostic index for clinical applications. A mono-institutional cohort of 1.577 human epidermal growth factor receptor 2 negative/ER+ breast cancer patients having complete clinical, histological, and follow-up data was collected. The 14 and 20 % Ki67 cut-offs were correlated to disease-free interval (DFI) and disease-specific survival (DSS). To create a comprehensive prognostic index, we used independent variables selected by uni/multivariate analyses. In terms of DFI and DSS, patients bearing tumors with Ki67 < 14 % proliferation index did not differ from those with Ki67 values between 14 and 20 %. Patients with tumor with Ki67 > 20 % showed the poorest prognosis. Moreover, to tumor size, the number of metastatic lymph nodes and Ki67 > 20 % was given a score value, varying depending on definite cut-offs and used to create a prognostic index, which was applied to the population. Patients with a prognostic index a parts per thousand yen3 were characterized by significant risk of relapse [DFI: Hazard Ratio (HR) = 4.74, p < 0.001] and death (DSS: HR = 5.03, p < 0.001). We confirm that the 20 % Ki67 cut-off is the best to stratify high-risk patients in luminal breast cancers, and we suggest to integrate it with other prognostic factors, to better stratify patients at risk of adverse outcome.

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