PURPOSE: Our goal was to identify genes undergoing expressional changes shortly after the beginning of neoadjuvant chemotherapy for primary breast cancer. EXPERIMENTAL DESIGN: The biopsies were taken from patients with primary breast cancer prior to any treatment and 24 hours after the beginning of the neoadjuvant chemotherapy. Expression analyses from matched pair samples representing 25 patients were carried out with Clontech filter arrays. A subcohort of those 25 paired samples were additionally analyzed with the Affymetrix GeneChip platform. All of the transcripts from both platforms were queried for expressional changes. RESULTS: Performing hierarchical cluster analysis, we clustered pre- and posttreatment samples from individual patients more closely to each other than the samples taken from different patients. This reflects the rather low number of transcripts responding directly to the drugs used. Although transcriptional drug response occurring during therapy differed between individual patients, two genes (p21(WAF1/CIP1) and MIC-1) were up-regulated in posttreatment samples. This could be validated by semiquantitative and real-time reverse transcription-PCR. Partial least- discriminant analysis based on approximately 25 genes independently identified by either Clontech or Affymetrix platforms could clearly discriminate pre- and posttreatment samples. However, correlation of certain gene expression levels as well as of differential patterns and clusters as determined by a different platform was not always satisfying. CONCLUSIONS: This study has demonstrated the potential of monitoring posttreatment changes in gene expression as a measure of the pharmacodynamics of drugs. As a clinical laboratory model, it can be useful to identify patients with sensitive and reactive tumors and to help for optimized choice for sequential therapy and obviously improve relapse- free and overall survival.
PURPOSE: Our goal was to identify genes undergoing expressional changes shortly after the beginning of neoadjuvant chemotherapy for primary breast cancer. EXPERIMENTAL DESIGN: The biopsies were taken from patients with primary breast cancer prior to any treatment and 24 hours after the beginning of the neoadjuvant chemotherapy. Expression analyses from matched pair samples representing 25 patients were carried out with Clontech filter arrays. A subcohort of those 25 paired samples were additionally analyzed with the Affymetrix GeneChip platform. All of the transcripts from both platforms were queried for expressional changes. RESULTS: Performing hierarchical cluster analysis, we clustered pre- and posttreatment samples from individual patients more closely to each other than the samples taken from different patients. This reflects the rather low number of transcripts responding directly to the drugs used. Although transcriptional drug response occurring during therapy differed between individual patients, two genes (p21(WAF1/CIP1) and MIC-1) were up-regulated in posttreatment samples. This could be validated by semiquantitative and real-time reverse transcription-PCR. Partial least- discriminant analysis based on approximately 25 genes independently identified by either Clontech or Affymetrix platforms could clearly discriminate pre- and posttreatment samples. However, correlation of certain gene expression levels as well as of differential patterns and clusters as determined by a different platform was not always satisfying. CONCLUSIONS: This study has demonstrated the potential of monitoring posttreatment changes in gene expression as a measure of the pharmacodynamics of drugs. As a clinical laboratory model, it can be useful to identify patients with sensitive and reactive tumors and to help for optimized choice for sequential therapy and obviously improve relapse- free and overall survival.
Authors: Jens-Peter Volkmer; Debashis Sahoo; Robert K Chin; Philip Levy Ho; Chad Tang; Antonina V Kurtova; Stephen B Willingham; Senthil K Pazhanisamy; Humberto Contreras-Trujillo; Theresa A Storm; Yair Lotan; Andrew H Beck; Benjamin I Chung; Ash A Alizadeh; Guilherme Godoy; Seth P Lerner; Matt van de Rijn; Linda D Shortliffe; Irving L Weissman; Keith S Chan Journal: Proc Natl Acad Sci U S A Date: 2012-01-19 Impact factor: 11.205
Authors: Wolf C Prall; Akos Czibere; Franck Grall; Dimitrios Spentzos; Ulrich Steidl; Aristoteles Achilles Nikolaus Giagounidis; Andrea Kuendgen; Hasan Otu; Astrid Rong; Towia A Libermann; Ulrich Germing; Norbert Gattermann; Rainer Haas; Manuel Aivado Journal: Int J Hematol Date: 2009-01-20 Impact factor: 2.490
Authors: M Ihnen; R M Wirtz; K T Kalogeras; K Milde-Langosch; M Schmidt; I Witzel; A G Eleftheraki; C Papadimitriou; F Jänicke; E Briassoulis; D Pectasides; A Rody; G Fountzilas; V Müller Journal: Br J Cancer Date: 2010-08-24 Impact factor: 7.640
Authors: Cintia Milani; Maria Lucia Hirata Katayama; Eduardo Carneiro de Lyra; JoEllen Welsh; Laura Tojeiro Campos; M Mitzi Brentani; Maria do Socorro Maciel; Rosimeire Aparecida Roela; Paulo Roberto del Valle; João Carlos Guedes Sampaio Góes; Suely Nonogaki; Rodrigo Esaki Tamura; Maria Aparecida Azevedo Koike Folgueira Journal: BMC Cancer Date: 2013-03-15 Impact factor: 4.430