Literature DB >> 17255270

Protein expression profiling in high-risk breast cancer patients treated with high-dose or conventional dose-dense chemotherapy.

Raihanatou Diallo-Danebrock1, Evelyn Ting, Oleg Gluz, Alexander Herr, Svjetlana Mohrmann, Helene Geddert, Achim Rody, Karl-Ludwig Schaefer, Stephan E Baldus, Arndt Hartmann, Peter J Wild, Michael Burson, Helmut E Gabbert, Ulrike Nitz, Christopher Poremba.   

Abstract

PURPOSE: To characterize the prognostic and predictive impact of protein expression profiles in high-risk breast cancer patients who had previously been shown to benefit from high-dose chemotherapy (HDCT) in comparison to dose-dense chemotherapy (DDCT). EXPERIMENTAL
DESIGN: The expression of 34 protein markers was evaluated using tissue microarrays containing paraffin-embedded breast cancer samples from 236 patients who were randomized to the West German Study Group AM01 trial.
RESULTS: (a) 24 protein markers of the initial panel of 34 markers were sufficient to identify five profile clusters (subtypes) by K-means clustering: luminal-A (27%), luminal-B (12%), HER-2 (21%), basal-like (13%) cluster, and a so-called "multiple marker negative" (MMN) cluster (27%) characterized by the absence of specifying markers. (b) After DDCT, HER-2 and basal-like groups had significantly worse event-free survival [EFS; hazard ratio (HR), 3.6 [95% confidence interval (95% CI), 1.65-8.18; P = 0.001] and HR, 3.7 (95% CI, 1.68-8.48; P < 0.0001), respectively] when compared with both luminal groups. (c) After HDCT, the HR was 1.5 (95% CI, 0.76-3.05) for EFS in the HER-2 subgroup and 1.1 (95% CI, 0.37-3.32) in the basal-like subgroup, which indicates a better outcome for patients in the HER-2 and basal-like subgroups who received HDCT. The MMN cluster showed a trend to a better EFS after HDCT compared with DDCT.
CONCLUSIONS: Protein expression profiling in high-risk breast cancers identified five subtypes, which differed with respect to survival and response to chemotherapy: In contrast to luminal-A and luminal-B subtypes, HER-2 and basal-like subgroups had a significant predictive benefit, and the MMN cluster had a trend to a predictive benefit, both from HDCT when compared with DDCT.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17255270     DOI: 10.1158/1078-0432.CCR-06-1842

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  17 in total

Review 1.  Molecular therapy of breast cancer: progress and future directions.

Authors:  Sheng-Xiang Lin; Jiong Chen; Mausumi Mazumdar; Donald Poirier; Cheng Wang; Arezki Azzi; Ming Zhou
Journal:  Nat Rev Endocrinol       Date:  2010-07-20       Impact factor: 43.330

Review 2.  Triple-negative breast cancer.

Authors:  Rupert Bartsch; Reinhard Ziebermayr; Christoph C Zielinski; Guenther G Steger
Journal:  Wien Med Wochenschr       Date:  2010-04

3.  Prognostic Evaluation of Epidermal Growth Factor Receptor (EGFR) Genotype and Phenotype Parameters in Triple-negative Breast Cancers.

Authors:  Sofia Levva; Vassiliki Kotoula; Ioannis Kostopoulos; Kyriaki Manousou; Christos Papadimitriou; Kyriaki Papadopoulou; Sotiris Lakis; Kyriakos Koukoulias; Vasilios Karavasilis; George Pentheroudakis; Eufemia Balassi; Flora Zagouri; Ioannis G Kaklamanos; Dimitrios Pectasides; Evangelia Razis; Gerasimos Aravantinos; Pavlos Papakostas; Dimitrios Bafaloukos; Grigorios Rallis; Helen Gogas; George Fountzilas
Journal:  Cancer Genomics Proteomics       Date:  2017 May-Jun       Impact factor: 4.069

4.  Targeting RLIP with CRISPR/Cas9 controls tumor growth.

Authors:  Jyotsana Singhal; Shireen Chikara; David Horne; Sanjay Awasthi; Ravi Salgia; Sharad S Singhal
Journal:  Carcinogenesis       Date:  2021-02-11       Impact factor: 4.944

5.  Prognostic value of periostin in early-stage breast cancer treated with conserving surgery and radiotherapy.

Authors:  Changyou Li; Jing Xu; Qi Wang; Shaoqing Geng; Zheng Yan; Jin You; Zhenfeng Li; Xiao Zou
Journal:  Oncol Lett       Date:  2018-03-21       Impact factor: 2.967

Review 6.  Demystifying basal-like breast carcinomas.

Authors:  L Da Silva; C Clarke; S R Lakhani
Journal:  J Clin Pathol       Date:  2007-05-11       Impact factor: 3.411

7.  Overexpression of KPNA2 correlates with poor prognosis in patients with gastric adenocarcinoma.

Authors:  Chen Li; Lv Ji; Zhong-Yang Ding; Qian-De Zhang; Guo-Rong Huang
Journal:  Tumour Biol       Date:  2013-01-03

8.  Evaluation of the Insulin-like Growth Factor Receptor Pathway in Patients with Advanced Breast Cancer Treated with Trastuzumab.

Authors:  Christos Christodoulou; Georgios Oikonomopoulos; Georgia Angeliki Koliou; Ioannis Kostopoulos; Vassiliki Kotoula; Mattheos Bobos; George Pentheroudakis; George Lazaridis; Maria Skondra; Sofia Chrisafi; Angelos Koutras; Dimitrios Bafaloukos; Evangelia Razis; Kyriaki Papadopoulou; Pavlos Papakostas; Haralambos P Kalofonos; Dimitrios Pectasides; Pantelis Skarlos; Konstantine T Kalogeras; George Fountzilas
Journal:  Cancer Genomics Proteomics       Date:  2018 Nov-Dec       Impact factor: 4.069

9.  Molecular profiling including epidermal growth factor receptor and p21 expression in high-risk breast cancer patients as indicators of outcome.

Authors:  G Somlo; P Chu; P Frankel; W Ye; S Groshen; J H Doroshow; K Danenberg; P Danenberg
Journal:  Ann Oncol       Date:  2008-07-17       Impact factor: 32.976

10.  Identification of biology-based breast cancer types with distinct predictive and prognostic features: role of steroid hormone and HER2 receptor expression in patients treated with neoadjuvant anthracycline/taxane-based chemotherapy.

Authors:  Silvia Darb-Esfahani; Sibylle Loibl; Berit M Müller; Marc Roller; Carsten Denkert; Martina Komor; Karsten Schlüns; Jens Uwe Blohmer; Jan Budczies; Bernd Gerber; Aurelia Noske; Andreas du Bois; Wilko Weichert; Christian Jackisch; Manfred Dietel; Klaus Richter; Manfred Kaufmann; Gunter von Minckwitz
Journal:  Breast Cancer Res       Date:  2009       Impact factor: 6.466

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.