Ki-67 as a controversial predictive and prognostic marker in breast cancer patients treated with neoadjuvant chemotherapy

Ács, Balázs; Zámbó, Veronika; Vízkeleti, Laura; Szász, Attila Marcell; Madaras, Lilla; Szentmártoni, Gyöngyvér; Tőkés, Tímea; Molnár, Béla; Molnár, István; Vari-Kakas, Stefan; Kulka, Janina; Tőkés, Anna-Mária

doi:10.1186/s13000-017-0608-5

Cited by 69 publications

(50 citation statements)

References 28 publications

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“…In the ROC analysis, only the postsurgical Ki67 level and the decrease in the Ki67 level had significance for predicting RFS. Most of the Ki67 cutoff values in previous studies were determined through a dichotomy of direct calculations 20-22 , whereas the optimal cutoffs of the Ki67 level parameters in this study were acquired from an online biostatistical calculator, which is more appropriate and reliable from a methodological and statistical point of view 18 . …”

Section: Discussionmentioning

confidence: 99%

Decrease in the Ki67 index during neoadjuvant chemotherapy predicts favorable relapse-free survival in patients with locally advanced breast cancer

Chen

Zhang²,

Huang

et al. 2019

Cancer Biol Med

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Objective: The purpose of this study was to explore the optimal cutoffs of the three parameters of Ki67 during NAC for predicting patient prognosis and investigate whether the optimal cutoffs of the Ki67 values were associated with relapse-free survival (RFS) or breast cancer-specific survival (BCSS). Methods: A total of 92 patients with locally advanced breast cancer (LABC), who had residual disease after NAC were retrospectively investigated. The optimal cutoff values of the Ki67 parameters were assessed by the online algorithm Cutoff Finder. Kaplan-Meier analysis, the log-rank test and Cox regression analysis were carried out to analyze survival. Results: The optimal cutoff values for the postsurgical Ki67 level and the decrease in the Ki67 level during NAC were defined as 25% and 12.5%, respectively. According to the univariate survival analysis, a higher Ki67 level in residual disease was associated with poor RFS (P = 0.004) and BCSS (P = 0.014). In addition, a Ki67 expression decrease > 12.5% during NAC was related to favorable RFS ( P = 0.007), but was not related to BCSS (P = 0.452). Cox regression analysis showed that the Ki67 expression decrease (> 12.5% vs. = 12.5%) and histological grade (grade 3 vs. grade 1-2) were the independent factors associated with RFS (P = 0.020 and P = 0.023, respectively), with HR values of 0.353 (95% CI: 0.147-0.850) and 3.422 (95% CI: 1.188-9.858), respectively. Conclusions: The Ki67 decrease was one of the independent factors associated with RFS in LABC patients with residual disease after receiving NAC.

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Section: Discussionmentioning

confidence: 99%

Decrease in the Ki67 index during neoadjuvant chemotherapy predicts favorable relapse-free survival in patients with locally advanced breast cancer

Chen

Zhang²,

Huang

et al. 2019

Cancer Biol Med

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“…Acs B. et al suggested that both the Ki-67-labeling index and subtype showed a significant correlation with the pathological response [18]. Additionally, their data also suggested that if a tumor did not respond to neoadjuvant therapy, increased Ki-67 was a poor prognostic marker.…”

Section: Discussionmentioning

confidence: 98%

Favorable Reproducibility of Ki-67-Labeling Index between Core Needle Biopsy and Surgical Materials in Mammary Carcinoma: Reproducibility Influenced by Hot Spots, a High Ki-67 Labeling Index, and the Total Length of Biopsy Material

Ogura¹,

Matsumoto²,

Sakaguchi³

et al. 2018

OJOG

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Aims: The reproducibility of Ki-67 between core-needle biopsies and surgical materials has not been well documented in the literature, although the concordance affects the utility of the Ki-67 labeling index based on the core-needle biopsy materials, which indicates the need for preoperative chemotherapy. The aim of this study was to reveal the reproducibility of Ki-67 between both materials and the cause of discrepancies. Methods and Results: We analyzed 137 cases of invasive carcinoma of the breast and the compared Ki-67-labeling index between core-needle biopsy and surgical materials. The Ki-67-labeling index of biopsy and surgical specimens ranged from 1% to 85% (median: 13%) and 1% to 80% (median: 12%), respectively. The discrepancy of Ki-67-labeling ranged from 0% to 55% (median: 4%) and could be calculated by the tumor size, hot spots of surgical materials, a high Ki-67-labeling index based on the core-needle biopsy materials, and the total length of core needles, respectively. Conclusions: The concordance rate of the Ki-67-labeling index between core-needle biopsies and surgical materials was favorable, so we can use each Ki-67-labeling index of core-needle biopsies as a marker for preoperative chemotherapy. Factors affecting the index discrepancy were hot spots, a high Ki-67-labeling index, and the total length of biopsy material. Judgements on the subtypes and clinical procedures of invasive breast carciHow to cite this paper: Ogura, K., Matsumoto, T., Sakaguchi, A., Onagi, H., Ura, A., Kosaka, T., Kitabatake, T., Kojima, K. and Morizane, T. (2018)

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“…We did not include Ki67 as it was not available for the vast majority of patients in NCRAS, and has already been shown to have poor predictive power. 35,36 The extracted NCRAS dataset contained complete information on which patients where treated with received trastuzumab and 3% received bisphosphonates.…”

Section: Data Sources and Patient Inclusion Criteriamentioning

confidence: 99%

Machine Learning to Guide the use of Adjuvant Therapies for Breast Cancer

Alaa

Gurdasani

Harris

et al. 2020

Preprint

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Accurate prediction of the individualized survival benefit of adjuvant therapy is key to making informed therapeutic decisions for patients with early invasive breast cancer. Here, we use a state-of the-art automated and interpretable machine learning algorithm to develop a breast cancer prognostication and treatment benefit prediction model — Adjutorium — using data from large-scale cohorts of nearly 1 million women captured in the national cancer registries of the United Kingdom and the United States. We trained and internally validated the Adjutorium model on 395,862 patients from the UK National Cancer Registration and Analysis Service (NCRAS); we then externally validated the model among 571,635 patients from the US Surveillance, Epidemiology, and End Results (SEER) Program. Adjutorium exhibited significantly improved accuracy compared to the major prognostic tool in current clinical use (PREDICT v2.1) in both internal and external validation (AUC-ROC for 5-year survival prediction in NCRAS was 0.835, 95% CI: 0.833–0.837 and 0.755, 95% CI: 0.753–0.757 for Adjutorium and PREDICT v2.1. In SEER, the AUC-ROC performance was 0.815, 95% CI: 0.813–0.817 and 0.775, 95% CI: 0.772–0.778 for Adjutorium and PREDICT v2.1, respectively). Importantly, our model substantially improved accuracy in specific subgroups known to be under-served by existing models. Adjutorium is currently implemented as a web-based decision support tool (vanderschaar-lab.com/adjutorium/) to aid decisions on adjuvant therapy in women with early breast cancer, and can be publicly accessed by patients and clinicians worldwide.

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Ki-67 as a controversial predictive and prognostic marker in breast cancer patients treated with neoadjuvant chemotherapy

Cited by 69 publications

References 28 publications

Decrease in the Ki67 index during neoadjuvant chemotherapy predicts favorable relapse-free survival in patients with locally advanced breast cancer

Decrease in the Ki67 index during neoadjuvant chemotherapy predicts favorable relapse-free survival in patients with locally advanced breast cancer

Favorable Reproducibility of Ki-67-Labeling Index between Core Needle Biopsy and Surgical Materials in Mammary Carcinoma: Reproducibility Influenced by Hot Spots, a High Ki-67 Labeling Index, and the Total Length of Biopsy Material

Machine Learning to Guide the use of Adjuvant Therapies for Breast Cancer

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