Literature DB >> 17140367

A genetic signature can predict prognosis and response to therapy in breast cancer: Oncotype DX.

Virginia Kaklamani1.   

Abstract

We now recognize that not all breast cancers are the same. Different characteristics in gene expression profiles result in differential clinical behavior. With the use of gene microarrays, different subtypes of breast cancer have been characterized. These subtypes include the basal, the ERBB2+, and the luminal A, B and C subtypes. The importance of these different subtypes lies in the fact that they differ in clinical outcome, with the basal and ERBB2+ subtypes having the worst prognosis and the luminal A group having the best prognosis. However, identification of these subtypes is still not clinically used. Other strategies for evaluating tumors in a clinical setting have been developed using smaller sets of genes. One such strategy is the 21-gene assay (Oncotype DX), which is currently in commercial use in the USA. One advantage of this test is the use of paraffin-embedded blocks instead of previous methods, which required fresh frozen tissue. Oncotype DX has been shown to predict 10-year distant recurrence in patients with estrogen receptor-positive, axillary lymph node-negative breast cancer. This genomic assay has also been shown to predict chemotherapy and endocrine therapy response. Large, prospective, randomized clinical trials are currently underway using this genomic test. Other similar tests are also finding their way in clinical practice. A 70-gene assay, which has been developed by a group in The Netherlands, is currently being used as a tool to assign treatment in women with early stage breast cancer. In the near future, clinical decisions will most likely be dictated by the genetic characteristics of the tumor, with the clinical characteristics becoming less important. Tailoring our treatment based on individual tumor characteristics will help us develop better therapeutic strategies and save many of our patients from receiving unnecessary toxic therapy.

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Year:  2006        PMID: 17140367     DOI: 10.1586/14737159.6.6.803

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  16 in total

Review 1.  Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine.

Authors:  Ashley G Rivenbark; Siobhan M O'Connor; William B Coleman
Journal:  Am J Pathol       Date:  2013-08-27       Impact factor: 4.307

2.  Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?

Authors:  S Bahri; J-H Chen; H J Yu; A Kuzucan; O Nalcioglu; M-Y Su
Journal:  Ann Oncol       Date:  2008-03-05       Impact factor: 32.976

3.  Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.

Authors:  Adi L Tarca; Mario Lauria; Michael Unger; Erhan Bilal; Stephanie Boue; Kushal Kumar Dey; Julia Hoeng; Heinz Koeppl; Florian Martin; Pablo Meyer; Preetam Nandy; Raquel Norel; Manuel Peitsch; Jeremy J Rice; Roberto Romero; Gustavo Stolovitzky; Marja Talikka; Yang Xiang; Christoph Zechner
Journal:  Bioinformatics       Date:  2013-08-20       Impact factor: 6.937

Review 4.  Involvement of microRNAs in HER2 signaling and trastuzumab treatment.

Authors:  Ling Mao; Ai-Jun Sun; Jian-Zhong Wu; Jin-Hai Tang
Journal:  Tumour Biol       Date:  2016-10-12

Review 5.  Insights into the Genetic Basis of the Renal Cell Carcinomas from The Cancer Genome Atlas.

Authors:  Scott M Haake; Jamie D Weyandt; W Kimryn Rathmell
Journal:  Mol Cancer Res       Date:  2016-06-21       Impact factor: 5.852

6.  Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

Authors:  Shweta S Chavan; Michael A Bauer; Erich A Peterson; Christoph J Heuck; Donald J Johann
Journal:  BMC Bioinformatics       Date:  2013-10-09       Impact factor: 3.169

Review 7.  Tissue-based research in kidney cancer: current challenges and future directions.

Authors:  Sabina Signoretti; Gennady Bratslavsky; Frederick M Waldman; Victor E Reuter; John Haaga; Maria Merino; George V Thomas; Michael R Pins; Towia Libermann; John Gillespie; Joseph E Tomaszewski; Carolyn C Compton; Andrew Hruszkewycz; W Marston Linehan; Michael B Atkins
Journal:  Clin Cancer Res       Date:  2008-06-15       Impact factor: 12.531

Review 8.  Evidence for field cancerization of the prostate.

Authors:  Larisa Nonn; Vijayalakshmi Ananthanarayanan; Peter H Gann
Journal:  Prostate       Date:  2009-09-15       Impact factor: 4.104

9.  Classifying patients for breast cancer by detection of autoantibodies against a panel of conformation-carrying antigens.

Authors:  Rick L Evans; James V Pottala; Kristi A Egland
Journal:  Cancer Prev Res (Phila)       Date:  2014-03-18

Review 10.  Gene expression profiling in human neurodegenerative disease.

Authors:  Johnathan Cooper-Knock; Janine Kirby; Laura Ferraiuolo; Paul R Heath; Magnus Rattray; Pamela J Shaw
Journal:  Nat Rev Neurol       Date:  2012-08-14       Impact factor: 42.937

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