Literature DB >> 14623525

Neoadjuvant comparisons of aromatase inhibitors and tamoxifen: pretreatment determinants of response and on-treatment effect.

Matthew J Ellis1, Eric Rosen, Holly Dressman, Jeffery Marks.   

Abstract

Adjuvant endocrine therapy reduces the risk of relapse and death from early stage hormone receptor positive breast cancer. However, tamoxifen is only partially effective because of the development of tumor resistance. Aromatase inhibitors (letrozole, anastrozole and exemestane) are also prone to the development of resistance but the pharmacologic action (estrogen deprivation) is distinct and so different mechanisms may be responsible. The problem of endocrine resistance can be directly studied in patients by examining the relationship between predictive tumor biomarkers and clinical outcome. In an example of a prospectively planned biomarker study, tumor samples were examined from a randomized trial of neoadjuvant endocrine treatment in which letrozole proved more effective than tamoxifen in terms of the rate of breast conservation and tumor regression. Interestingly letrozole was more effective at all levels of ER expression and was particularly more efficacious than tamoxifen for tumors that expressed HER1 and/or HER2 (with ER). This suggests that HER1/2 predicts primary tamoxifen resistance and relative sensitivity to potent estrogen deprivation, perhaps because HER1/2 signaling promotes the partial agonist effects of tamoxifen. A Phase 2 study of neoadjuvant letrozole is now underway to focus on gene expression profiling as a mechanism to further investigate the transcriptional programs that underlie resistance and sensitivity to estrogen deprivation. Expression profiles taken at baseline and after 1 month of therapy reveal dramatic reductions in the expression from genes responsible for DNA replication and synthesis, cell cycle progression, suppression of apoptosis and tissue invasion. When enough profiles have been generated it should be possible to detect complex interaction patterns that correctly reclassify ER+ disease into treatment responsive and resistant categories with high probability.

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Year:  2003        PMID: 14623525     DOI: 10.1016/s0960-0760(03)00371-6

Source DB:  PubMed          Journal:  J Steroid Biochem Mol Biol        ISSN: 0960-0760            Impact factor:   4.292


  7 in total

1.  Diagnosis and medical treatment of breast cancer. Cordoba Consensus of 2007.

Authors:  Juan de la Haba-Rodríguez; Emilio Alba; Agustí Barnadas; Eloisa Bayo; Antonio Llombart; Ana Lluch; Miguel Martín; José Andrés Moreno-Nogueira; Gumersindo Pérez Manga; Alvaro Rodríguez-Lescure; Enrique Aranda
Journal:  Clin Transl Oncol       Date:  2008-09       Impact factor: 3.405

Review 2.  [Treatment of breast cancer: from hormones to antibodies].

Authors:  J Eucker; A Emde; K Possinger
Journal:  Internist (Berl)       Date:  2006-12       Impact factor: 0.743

3.  Role of neo-adjuvant hormonal therapy in the treatment of breast cancer: a review of clinical trials.

Authors:  Catherine Abrial; Xavier Durando; Marie-Ange Mouret-Reynier; Emilie Thivat; Mathilde Bayet-Robert; Béatrice Nayl; Pascale Dubray; Christophe Pomel; Philippe Chollet; F Penault-Llorca
Journal:  Int J Gen Med       Date:  2009-07-30

4.  Imaging early changes in proliferation at 1 week post chemotherapy: a pilot study in breast cancer patients with 3'-deoxy-3'-[18F]fluorothymidine positron emission tomography.

Authors:  Laura Kenny; R Charles Coombes; David M Vigushin; Adil Al-Nahhas; Sami Shousha; Eric O Aboagye
Journal:  Eur J Nucl Med Mol Imaging       Date:  2007-03-02       Impact factor: 9.236

5.  Letrozole in the neoadjuvant setting: the P024 trial.

Authors:  Matthew J Ellis; Cynthia Ma
Journal:  Breast Cancer Res Treat       Date:  2007-10-03       Impact factor: 4.872

Review 6.  Live or let die: oestrogen regulation of survival signalling in endocrine response.

Authors:  Alison J Butt; Robert L Sutherland; Elizabeth A Musgrove
Journal:  Breast Cancer Res       Date:  2007       Impact factor: 6.466

7.  Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

Authors:  Nadia M Penrod; Casey S Greene; Jason H Moore
Journal:  Genome Med       Date:  2014-04-30       Impact factor: 11.117

  7 in total

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