Literature DB >> 23166150

Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement.

S Guiu1, S Michiels, F André, J Cortes, C Denkert, A Di Leo, B T Hennessy, T Sorlie, C Sotiriou, N Turner, M Van de Vijver, G Viale, S Loi, J S Reis-Filho.   

Abstract

The 2012 IMPAKT task force investigated the medical usefulness of current methods for the classification of breast cancer into the 'intrinsic' molecular subtypes (luminal A, luminal B, basal-like and HER2). A panel of breast cancer and/or gene expression profiling experts evaluated the analytical validity, clinical validity and clinical utility of two approaches for molecular subtyping of breast cancer: the prediction analysis of microarray (PAM)50 assay and an immuno-histochemical (IHC) surrogate panel including oestrogen receptor (ER), HER2 and Ki67. The panel found the currently available evidence on the analytical validity and clinical utility of Ki67 based on a 14% cut-off and PAM50 to be inadequate. The majority of the working group members found the available evidence on the analytical validity, clinical validity and clinical utility of ER/HER2 to be convincing. The panel concluded that breast cancer classification into molecular subtypes based on the IHC assessment of ER, HER2 and Ki67 with a 14% cut-off and on the PAM50 test does not provide sufficiently robust information to modify systemic treatment decisions, and recommended the use IHC for ER and HER2 for the identification of clinically relevant subtypes of breast cancers. Methods for breast cancer classification into molecular subtypes should, however, be incorporated into clinical trial design.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 23166150     DOI: 10.1093/annonc/mds586

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  99 in total

1.  MCM2: An alternative to Ki-67 for measuring breast cancer cell proliferation.

Authors:  Einas M Yousef; Daniela Furrer; David L Laperriere; Muhammad R Tahir; Sylvie Mader; Caroline Diorio; Louis A Gaboury
Journal:  Mod Pathol       Date:  2017-01-13       Impact factor: 7.842

2.  Cancer progression modeling using static sample data.

Authors:  Yijun Sun; Jin Yao; Norma J Nowak; Steve Goodison
Journal:  Genome Biol       Date:  2014-08-26       Impact factor: 13.583

Review 3.  [Gene expression analysis in breast cancer. A new diagnostic tool in pathology].

Authors:  C Denkert
Journal:  Pathologe       Date:  2013-09       Impact factor: 1.011

4.  Thermodynamically inspired classifier for molecular phenotypes of health and disease.

Authors:  Marc T Facciotti
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-07       Impact factor: 11.205

5.  Digital image analysis outperforms manual biomarker assessment in breast cancer.

Authors:  Gustav Stålhammar; Nelson Fuentes Martinez; Michael Lippert; Nicholas P Tobin; Ida Mølholm; Lorand Kis; Gustaf Rosin; Mattias Rantalainen; Lars Pedersen; Jonas Bergh; Michael Grunkin; Johan Hartman
Journal:  Mod Pathol       Date:  2016-02-26       Impact factor: 7.842

6.  Normal cell phenotypes of breast epithelial cells provide the foundation of a breast cancer taxonomy.

Authors:  Sandro Santagata; Tan A Ince
Journal:  Expert Rev Anticancer Ther       Date:  2014-09-27       Impact factor: 4.512

Review 7.  Current approaches to the treatment of metastatic brain tumours.

Authors:  Taofeek K Owonikoko; Jack Arbiser; Amelia Zelnak; Hui-Kuo G Shu; Hyunsuk Shim; Adam M Robin; Steven N Kalkanis; Timothy G Whitsett; Bodour Salhia; Nhan L Tran; Timothy Ryken; Michael K Moore; Kathleen M Egan; Jeffrey J Olson
Journal:  Nat Rev Clin Oncol       Date:  2014-02-25       Impact factor: 66.675

8.  Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results.

Authors:  Maria Adele Marino; Katja Pinker; Doris Leithner; Janice Sung; Daly Avendano; Elizabeth A Morris; Maxine Jochelson
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

9.  Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

Authors:  Richard Ha; Simukayi Mutasa; Jenika Karcich; Nishant Gupta; Eduardo Pascual Van Sant; John Nemer; Mary Sun; Peter Chang; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

10.  Taxonomy of breast cancer based on normal cell phenotype predicts outcome.

Authors:  Sandro Santagata; Ankita Thakkar; Ayse Ergonul; Bin Wang; Terri Woo; Rong Hu; J Chuck Harrell; George McNamara; Matthew Schwede; Aedin C Culhane; David Kindelberger; Scott Rodig; Andrea Richardson; Stuart J Schnitt; Rulla M Tamimi; Tan A Ince
Journal:  J Clin Invest       Date:  2014-01-27       Impact factor: 14.808

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.