Literature DB >> 27558625

St Gallen 2015 subtyping of luminal breast cancers: impact of different Ki67-based proliferation assessment methods.

Cornelia M Focke1,2, Paul J van Diest3, Thomas Decker4.   

Abstract

Ki67 has been proposed as prognostic proliferation marker in luminal breast cancer (BC), but little is known on the influence of Ki67 assessment methods on subtyping into luminal A- and B-like tumors. Our aim was to study the influence of different Ki67-labeling index (Ki67-LI) assessment methods on the proportion of BCs classified as luminal A-like. 280 early BCs were subtyped according to the St Gallen 2015 definitions into 71 % luminal (HER2 negative), 6 % luminal B-like (HER2 positive), 13 % triple negative, 1 % HER2 positive (nonluminal), and 9 % special type. Digitized whole slides were counted manually on the screen. We used nine defined counting methods to assess the Ki67-LI (including the International Ki67 in Breast Cancer Working Group recommendations), and compared the resulting medians and the proportions of cancers classified as luminal A-like according to the formerly used cut-off <20 %. Methods assessing hot spots and tumor periphery resulted in significantly higher Ki67-LI medians than those measuring an average proliferation (27.45 % vs 16.96 %, p < 0.0001). Substantially lower median Ki67-LI were found when assessing 1020 compared to counting 100, 200, 300 cells (17.65 vs 33 %, vs 28 %, vs 24.33 %, respectively; p < 0.0001), or 510 cells (20.59 %, p = 0.019). Applying a standard Ki67-LI cut-off <20 % to define low proliferation for all methods, the proportion of luminal A-like cancers varied between 13 and 44 %. The proportion of BCs classified as luminal A-like is highly influenced by the Ki67-LI assessment method. As a consequence, the selection of a specific Ki67-LI assessment method may have a direct effect on the proportion of patients considered having low-risk disease and thus influence therapeutic decision making. This calls for a standardized assessment method.

Entities:  

Keywords:  Breast cancer; Intrinsic subtyping; Ki67; Methodology; Proliferation; St Gallen consensus

Mesh:

Substances:

Year:  2016        PMID: 27558625     DOI: 10.1007/s10549-016-3950-5

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  19 in total

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2.  Ki67 Immunohistochemistry Quantification in Breast Carcinoma: A Comparison of Visual Estimation, Counting, and ImmunoRatio.

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Journal:  Appl Immunohistochem Mol Morphol       Date:  2021-02-01

3.  MiR-328 May be Considered as an Oncogene in Human Invasive Breast Carcinoma.

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Journal:  Iran Red Crescent Med J       Date:  2016-11-07       Impact factor: 0.611

4.  Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling.

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Journal:  BMC Cancer       Date:  2017-11-29       Impact factor: 4.430

5.  Sequential immunohistochemistry and virtual image reconstruction using a single slide for quantitative KI67 measurement in breast cancer.

Authors:  Garazi Serna; Sara Simonetti; Roberta Fasani; Francesca Pagliuca; Xavier Guardia; Paqui Gallego; Jose Jimenez; Vicente Peg; Cristina Saura; Serenella Eppenberger-Castori; Santiago Ramon Y Cajal; Luigi Terracciano; Paolo Nuciforo
Journal:  Breast       Date:  2020-07-13       Impact factor: 4.380

6.  The prognostic value of quantitative analysis of CCL5 and collagen IV in luminal B (HER2-) subtype breast cancer by quantum-dot-based molecular imaging.

Authors:  Yong-Yun Zhu; Chuang Chen; Juan-Juan Li; Sheng-Rong Sun
Journal:  Int J Nanomedicine       Date:  2018-06-28

7.  Ki-67 assessment in early breast cancer: SAKK28/12 validation study on the IBCSG VIII and IBCSG IX cohort.

Authors:  Zsuzsanna Varga; Qiyu Li; Wolfram Jochum; Ulrike Perriard; Tilman Rau; Jean-Christoph Tille; Hanne Hawle; Dirk Klingbiel; Beat Thuerlimann; Thomas Ruhstaller
Journal:  Sci Rep       Date:  2019-09-19       Impact factor: 4.379

8.  Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.

Authors:  Min Feng; Yang Deng; Libo Yang; Qiuyang Jing; Zhang Zhang; Lian Xu; Xiaoxia Wei; Yanyan Zhou; Diwei Wu; Fei Xiang; Yizhe Wang; Ji Bao; Hong Bu
Journal:  Diagn Pathol       Date:  2020-05-29       Impact factor: 2.644

9.  Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer.

Authors:  Timothy Kwang Yong Tay; Aye Aye Thike; Nirmala Pathmanathan; Ana Richelia Jara-Lazaro; Jabed Iqbal; Adeline Shi Hui Sng; Heng Seow Ye; Jeffrey Chun Tatt Lim; Valerie Cui Yun Koh; Jane Sie Yong Tan; Joe Poh Sheng Yeong; Zi Long Chow; Hui Hua Li; Chee Leong Cheng; Puay Hoon Tan
Journal:  Oncotarget       Date:  2018-02-05

10.  Conventional Pathology Versus Gene Signatures for Assessing Luminal A and B Type Breast Cancers: Results of a Prospective Cohort Study.

Authors:  Julia E C van Steenhoven; Anne Kuijer; Paul J van Diest; Joost M van Gorp; Marieke Straver; Sjoerd G Elias; Jelle Wesseling; Emiel Rutgers; Johanna N H Timmer-Bonte; Peter Nieboer; Tineke J Smilde; Alex Imholz; Charlotte F J M Blanken; Sabine Siesling; Thijs van Dalen
Journal:  Genes (Basel)       Date:  2018-05-17       Impact factor: 4.096

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