Literature DB >> 17019432

Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial.

Jan Bogaerts1, Fatima Cardoso, Marc Buyse, Sofia Braga, Sherene Loi, Jillian A Harrison, Jacques Bines, Stella Mook, Nuria Decker, Peter Ravdin, Patrick Therasse, Emiel Rutgers, Laura J van 't Veer, Martine Piccart.   

Abstract

This Review describes the work conducted by the TRANSBIG consortium in the development of the MINDACT (Microarray In Node negative Disease may Avoid ChemoTherapy) trial. The goal of the trial is to provide definitive evidence regarding the clinical relevance of the 70-gene prognosis signature, and to assess the performance of this signature compared with that of traditional prognostic indicators for assigning adjuvant chemotherapy to patients with node-negative breast cancer. We outline the background work and the key questions in node-negative early-stage breast cancer, and then focus on the MINDACT trial design and statistical considerations. The challenges inherent in this trial in terms of logistics, implementation and interpretation of the results are also discussed. We hope that this article will trigger further discussion about the difficulties of setting up and analyzing trials aimed at establishing the worth of new methods for better selection of patients for cancer treatment.

Entities:  

Mesh:

Year:  2006        PMID: 17019432     DOI: 10.1038/ncponc0591

Source DB:  PubMed          Journal:  Nat Clin Pract Oncol        ISSN: 1743-4254


  63 in total

1.  Transforming the practice of medicine using genomics.

Authors:  Geoffrey S Ginsburg; Geoffrey S Ginsburg; Jeanette J McCarthy
Journal:  Clin Cases Miner Bone Metab       Date:  2009-01

Review 2.  Gene-expression-based prognostic assays for breast cancer.

Authors:  Chungyeul Kim; Soonmyung Paik
Journal:  Nat Rev Clin Oncol       Date:  2010-05-04       Impact factor: 66.675

Review 3.  Biomarkers and surrogate end points--the challenge of statistical validation.

Authors:  Marc Buyse; Daniel J Sargent; Axel Grothey; Alastair Matheson; Aimery de Gramont
Journal:  Nat Rev Clin Oncol       Date:  2010-04-06       Impact factor: 66.675

4.  Sample size requirements for training high-dimensional risk predictors.

Authors:  Kevin K Dobbin; Xiao Song
Journal:  Biostatistics       Date:  2013-07-19       Impact factor: 5.899

5.  Differentiating proteomic biomarkers in breast cancer by laser capture microdissection and MALDI MS.

Authors:  Melinda E Sanders; Eduardo C Dias; Baogang J Xu; James A Mobley; Dean Billheimer; Heinrich Roder; Julia Grigorieva; Mitchell Dowsett; Carlos L Arteaga; Richard M Caprioli
Journal:  J Proteome Res       Date:  2008-04-04       Impact factor: 4.466

6.  Use of archived specimens in evaluation of prognostic and predictive biomarkers.

Authors:  Richard M Simon; Soonmyung Paik; Daniel F Hayes
Journal:  J Natl Cancer Inst       Date:  2009-10-08       Impact factor: 13.506

7.  Is adjuvant chemotherapy of benefit for postmenopausal women who receive endocrine treatment for highly endocrine-responsive, node-positive breast cancer? International Breast Cancer Study Group Trials VII and 12-93.

Authors:  Olivia Pagani; Shari Gelber; Edda Simoncini; Monica Castiglione-Gertsch; Karen N Price; Richard D Gelber; Stig B Holmberg; Diana Crivellari; John Collins; Jurij Lindtner; Beat Thürlimann; Martin F Fey; Elizabeth Murray; John F Forbes; Alan S Coates; Aron Goldhirsch
Journal:  Breast Cancer Res Treat       Date:  2008-10-25       Impact factor: 4.872

8.  Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009.

Authors:  A Goldhirsch; J N Ingle; R D Gelber; A S Coates; B Thürlimann; H-J Senn
Journal:  Ann Oncol       Date:  2009-06-17       Impact factor: 32.976

9.  An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH.

Authors:  Joke Allemeersch; Steven Van Vooren; Femke Hannes; Bart De Moor; Joris Robert Vermeesch; Yves Moreau
Journal:  BMC Bioinformatics       Date:  2009-11-19       Impact factor: 3.169

10.  A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer.

Authors:  Fabien Reyal; Martin H van Vliet; Nicola J Armstrong; Hugo M Horlings; Karin E de Visser; Marlen Kok; Andrew E Teschendorff; Stella Mook; Laura van 't Veer; Carlos Caldas; Remy J Salmon; Marc J van de Vijver; Lodewyk F A Wessels
Journal:  Breast Cancer Res       Date:  2008-11-13       Impact factor: 6.466

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.