Literature DB >> 18614375

Review of gene-expression profiling and its clinical use in breast cancer.

Zsofia K Stadler1, Steven E Come.   

Abstract

Despite advances in the treatment of early-stage breast cancer, physicians still lack the ability to accurately predict which individual patients will relapse and would benefit from adjuvant chemotherapy. Traditional clinicopathologic factors are important in helping to determine risk of relapse, but do not fully account for the biologic complexity of breast cancer. Gene-expression profiling has provided us with insight into the heterogeneity of breast cancer and led to the development of prognostic and predictive molecular gene signature models designed to aid in clinical decision-making. However, it remains to be determined how much refinement in prognosis genomic models provide over standard clinicopathologic features and whether these refinements translate into improvements in clinical practice. On-going large prospective multi-center clinical trials will provide us with information regarding the clinical utility of two of these assays, but for now, implementation of these models into widespread clinical practice remains limited.

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Year:  2008        PMID: 18614375     DOI: 10.1016/j.critrevonc.2008.05.004

Source DB:  PubMed          Journal:  Crit Rev Oncol Hematol        ISSN: 1040-8428            Impact factor:   6.312


  7 in total

Review 1.  Stress-induced EGF receptor signaling through STAT3 and tumor progression in triple-negative breast cancer.

Authors:  Nikolas Balanis; Cathleen R Carlin
Journal:  Mol Cell Endocrinol       Date:  2017-01-12       Impact factor: 4.102

Review 2.  Dissecting variability in responses to cancer chemotherapy through systems pharmacology.

Authors:  R Yang; M Niepel; T K Mitchison; P K Sorger
Journal:  Clin Pharmacol Ther       Date:  2010-06-02       Impact factor: 6.875

3.  Frequent genetic differences between matched primary and metastatic breast cancer provide an approach to identification of biomarkers for disease progression.

Authors:  Andrzej B Popławski; Michał Jankowski; Stephen W Erickson; Teresita Díaz de Ståhl; E Christopher Partridge; Chiquito Crasto; Jingyu Guo; John Gibson; Uwe Menzel; Carl Eg Bruder; Aneta Kaczmarczyk; Magdalena Benetkiewicz; Robin Andersson; Johanna Sandgren; Barbara Zegarska; Dariusz Bała; Ewa Srutek; David B Allison; Arkadiusz Piotrowski; Wojciech Zegarski; Jan P Dumanski
Journal:  Eur J Hum Genet       Date:  2010-01-06       Impact factor: 4.246

4.  Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue.

Authors:  Janine Antonov; Vlad Popovici; Mauro Delorenzi; Pratyaksha Wirapati; Anna Baltzer; Andrea Oberli; Beat Thürlimann; Anita Giobbie-Hurder; Giuseppe Viale; Hans Jörg Altermatt; Stefan Aebi; Rolf Jaggi
Journal:  BMC Cancer       Date:  2010-02-09       Impact factor: 4.430

5.  Effects of aging and anatomic location on gene expression in human retina.

Authors:  Hui Cai; Mark A Fields; Risa Hoshino; Lucian V Del Priore
Journal:  Front Aging Neurosci       Date:  2012-05-31       Impact factor: 5.750

6.  Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models.

Authors:  Siver A Moestue; Eldrid Borgan; Else M Huuse; Evita M Lindholm; Beathe Sitter; Anne-Lise Børresen-Dale; Olav Engebraaten; Gunhild M Maelandsmo; Ingrid S Gribbestad
Journal:  BMC Cancer       Date:  2010-08-17       Impact factor: 4.430

7.  Gene expression profiling of breast cancer in Lebanese women.

Authors:  Joelle Makoukji; Nadine J Makhoul; Maya Khalil; Sally El-Sitt; Ehab Saad Aldin; Mark Jabbour; Fouad Boulos; Emanuela Gadaleta; Ajanthah Sangaralingam; Claude Chelala; Rose-Mary Boustany; Arafat Tfayli
Journal:  Sci Rep       Date:  2016-11-18       Impact factor: 4.379

  7 in total

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