Literature DB >> 22318550

Gene expression and pathologic response to neoadjuvant chemotherapy in breast cancer.

Agnieszka Kolacinska1, Wojciech Fendler, Janusz Szemraj, Bozena Szymanska, Ewa Borowska-Garganisz, Magdalena Nowik, Justyna Chalubinska, Robert Kubiak, Zofia Pawlowska, Maria Blasinska-Morawiec, Piotr Potemski, Arkadiusz Jeziorski, Zbigniew Morawiec.   

Abstract

Pathologic complete response after neoadjuvant systemic treatment appears to be a valid surrogate for better overall survival in breast cancer patients. Currently, together with standard clinicopathologic assessment, novel molecular biomarkers are being exhaustively tested in order to look into the heterogeneity of breast cancer. The aim of our study was to examine an association between 23-gene real-time-PCR expression assay including ABCB1, ABCC1, BAX, BBC3, BCL2, CASP3, CYP2D6, ERCC1, FOXC1, GAPDH, IGF1R, IRF1, MAP2, MAPK 8, MAPK9, MKI67, MMP9, NCOA3, PARP1, PIK3CA, TGFB3, TOP2A, and YWHAZ receptor status of breast cancer core biopsies sampled before neoadjuvant chemotherapy (anthracycline and taxanes) and pathologic response. Core-needle biopsies were collected from 42 female patients with inoperable locally advanced breast cancer or resectable tumors suitable for downstaging, before any treatment. Expressions of 23 genes were determined by means of TagMan low density arrays. Analysis of variance was used to select genes with discriminatory potential between receptor subtypes. We introduced a correction for false discovery rates (presented as q values) due to multiple hypothesis testing. Statistical analysis showed that seven genes out of a 23-gene real-time-PCR expression assay differed significantly in relation to pathologic response regardless of breast cancer subtypes. Among these genes, we identified: BAX (p = 0.0146), CYP2D6 (p = 0.0063), ERCC1 (p = 0.0231), FOXC1 (p = 0.0048), IRF1 (p = 0.0022), MAP2 (p = 0.0011), and MKI67 (p = 0.0332). The assessment of core biopsy gene profiles and receptor-based subtypes, before neoadjuvant therapy seems to predict response or resistance and to define new signaling pathways to provide more powerful classifiers in breast cancer, hence the need for further research.

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Year:  2012        PMID: 22318550     DOI: 10.1007/s11033-012-1576-1

Source DB:  PubMed          Journal:  Mol Biol Rep        ISSN: 0301-4851            Impact factor:   2.316


  23 in total

Review 1.  Lessons from the neoadjuvant setting on how best to choose adjuvant therapies.

Authors:  Gunter von Minckwitz; Sibylle Loibl; Andrea Maisch; Michael Untch
Journal:  Breast       Date:  2011-10       Impact factor: 4.380

2.  Measuring β-tubulin III, Bcl-2, and ERCC1 improves pathological complete remission predictive accuracy in breast cancer.

Authors:  Xiaosong Chen; Jiayi Wu; Hongfen Lu; Ou Huang; Kunwei Shen
Journal:  Cancer Sci       Date:  2011-11-29       Impact factor: 6.716

3.  The pathologic complete response open question in primary therapy.

Authors:  Caterina Marchiò; Anna Sapino
Journal:  J Natl Cancer Inst Monogr       Date:  2011

4.  Phase III randomized trial of dose intensive neoadjuvant chemotherapy with or without G-CSF in locally advanced breast cancer: long-term results.

Authors:  Banu K Arun; Kapil Dhinghra; Vicente Valero; Shu-Wan Kau; Kristine Broglio; Daniel Booser; Laura Guerra; Guosheng Yin; Ronald Walters; Aysegul Sahin; Nuhad Ibrahim; Aman U Buzdar; Debbie Frye; Nour Sneige; Eric Strom; Merrick Ross; Richard L Theriault; Saroj Vadhan-Raj; Gabriel N Hortobagyi
Journal:  Oncologist       Date:  2011-10-31

5.  Frequent loss of heterozygosity at the interferon regulatory factor-1 gene locus in breast cancer.

Authors:  Luciane R Cavalli; Rebecca B Riggins; Antai Wang; Robert Clarke; Bassem R Haddad
Journal:  Breast Cancer Res Treat       Date:  2009-08-21       Impact factor: 4.872

6.  Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules.

Authors:  Andrew E Teschendorff; Sergio Gomez; Alex Arenas; Dorraya El-Ashry; Marcus Schmidt; Mathias Gehrmann; Carlos Caldas
Journal:  BMC Cancer       Date:  2010-11-04       Impact factor: 4.430

7.  Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?

Authors:  Takayuki Iwamoto; Lajos Pusztai
Journal:  Genome Med       Date:  2010-11-12       Impact factor: 11.117

8.  Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer.

Authors:  Ana M Gonzalez-Angulo; Bryan T Hennessy; Funda Meric-Bernstam; Aysegul Sahin; Wenbin Liu; Zhenlin Ju; Mark S Carey; Simen Myhre; Corey Speers; Lei Deng; Russell Broaddus; Ana Lluch; Sam Aparicio; Powel Brown; Lajos Pusztai; W Fraser Symmans; Jan Alsner; Jens Overgaard; Anne-Lise Borresen-Dale; Gabriel N Hortobagyi; Kevin R Coombes; Gordon B Mills
Journal:  Clin Proteomics       Date:  2011-07-08       Impact factor: 3.988

9.  Network based consensus gene signatures for biomarker discovery in breast cancer.

Authors:  Holger Fröhlich
Journal:  PLoS One       Date:  2011-10-25       Impact factor: 3.240

10.  Evaluation of biological pathways involved in chemotherapy response in breast cancer.

Authors:  Attila Tordai; Jing Wang; Fabrice Andre; Cornelia Liedtke; Kai Yan; Christos Sotiriou; Gabriel N Hortobagyi; W Fraser Symmans; Lajos Pusztai
Journal:  Breast Cancer Res       Date:  2008-04-29       Impact factor: 6.466

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  6 in total

1.  Interferon regulatory factor-1 signaling regulates the switch between autophagy and apoptosis to determine breast cancer cell fate.

Authors:  Jessica L Schwartz-Roberts; Katherine L Cook; Chun Chen; Ayesha N Shajahan-Haq; Margaret Axelrod; Anni Wärri; Rebecca B Riggins; Lu Jin; Bassem R Haddad; Bhaskar V Kallakury; William T Baumann; Robert Clarke
Journal:  Cancer Res       Date:  2015-01-09       Impact factor: 12.701

2.  Serial expression analysis of breast tumors during neoadjuvant chemotherapy reveals changes in cell cycle and immune pathways associated with recurrence and response.

Authors:  Mark Jesus M Magbanua; Denise M Wolf; Christina Yau; Sarah E Davis; Julia Crothers; Alfred Au; Christopher M Haqq; Chad Livasy; Hope S Rugo; Laura Esserman; John W Park; Laura J van 't Veer
Journal:  Breast Cancer Res       Date:  2015-05-29       Impact factor: 6.466

3.  Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

Authors:  Shumei Zhang; Yihan Wang; Yue Gu; Jiang Zhu; Ce Ci; Zhongfu Guo; Chuangeng Chen; Yanjun Wei; Wenhua Lv; Hongbo Liu; Dongwei Zhang; Yan Zhang
Journal:  Mol Oncol       Date:  2018-05-21       Impact factor: 6.603

4.  Proteomic analysis of gemcitabine-resistant pancreatic cancer cells reveals that microtubule-associated protein 2 upregulation associates with taxane treatment.

Authors:  Tessa Ya Sung Le Large; Btissame El Hassouni; Niccola Funel; Bart Kok; Sander R Piersma; Thang V Pham; Kenneth P Olive; Geert Kazemier; Hanneke W M van Laarhoven; Connie R Jimenez; Maarten F Bijlsma; Elisa Giovannetti
Journal:  Ther Adv Med Oncol       Date:  2019-05-10       Impact factor: 8.168

Review 5.  Predictive Value of Ercc1 and Xpd Polymorphisms for Clinical Outcomes of Patients Receiving Neoadjuvant Therapy: A Prisma-Compliant Meta-Analysis.

Authors:  Mao Qixing; Dong Gaochao; Xia Wenjie; Yin Rong; Jiang Feng; Xu Lin; Qiu Mantang; Chen Qiang
Journal:  Medicine (Baltimore)       Date:  2015-09       Impact factor: 1.817

6.  Identification of stromal ColXα1 and tumor-infiltrating lymphocytes as putative predictive markers of neoadjuvant therapy in estrogen receptor-positive/HER2-positive breast cancer.

Authors:  Alexander S Brodsky; Jinjun Xiong; Dongfang Yang; Christoph Schorl; Mary Anne Fenton; Theresa A Graves; William M Sikov; Murray B Resnick; Yihong Wang
Journal:  BMC Cancer       Date:  2016-04-18       Impact factor: 4.430

  6 in total

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