Literature DB >> 18662965

Effect of molecular disease subsets on disease-free survival in randomized adjuvant chemotherapy trials for estrogen receptor-positive breast cancer.

Lajos Pusztai1, Kristine Broglio, Fabrice Andre, W Fraser Symmans, Kenneth R Hess, Gabriel N Hortobagyi.   

Abstract

PURPOSE: The majority of estrogen receptor (ER)-positive cancers are sensitive to endocrine therapy and may not derive much further benefit from chemotherapy, but a subset are potentially chemotherapy sensitive. Molecular diagnostic tests allow the identification of these various subsets with some accuracy. The goal of the current analysis was to examine how the proportion of cases in the various risk (recurrence score [RS]) categories of a commercially available multigene assay influences the power of randomized trials to show benefit from adjuvant chemotherapy.
METHODS: We modeled 10-year disease-free survival (DFS) for hypothetical, two-arm clinical trials that randomly assigned patients with ER-positive breast cancer to endocrine therapy alone or endocrine therapy plus chemotherapy. We varied the proportion of patients in low, intermediate, and high RS categories and used DFS estimates for each risk group based on results from the Southwest Oncology Group 8814 study.
RESULTS: The probability of observing significant improvement in DFS as a result of chemotherapy decreases as the proportion of patients in the low RS category increases. For example, if a trial is designed with 80% power and the actual proportion of low RS patients accrued to the study increases from 40% to 60%, the power drops to 63%.
CONCLUSION: Variable accrual of low RS patients into different randomized adjuvant chemotherapy trials may partly explain contradictory results in the literature. Studies can be underpowered to detect improvement with chemotherapy as a result of inclusion of too many patients with low RS. Future adjuvant studies for ER-positive breast cancer will need to consider stratifying patients by molecular subtype.

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Year:  2008        PMID: 18662965     DOI: 10.1200/JCO.2008.17.2544

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  12 in total

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Review 2.  Taxanes: optimizing adjuvant chemotherapy for early-stage breast cancer.

Authors:  Philippe L Bedard; Angelo Di Leo; Martine J Piccart-Gebhart
Journal:  Nat Rev Clin Oncol       Date:  2009-12-08       Impact factor: 66.675

3.  Estrogen receptor expression and docetaxel efficacy in patients with metastatic breast cancer: a pooled analysis of four randomized trials.

Authors:  Fabrice Andre; Kristine Broglio; Lajos Pusztai; Narjiss Berrada; John R Mackey; Jean Marc Nabholtz; Stephen Chan; Gabriel N Hortobagyi
Journal:  Oncologist       Date:  2010-04-26

Review 4.  Molecular Subtypes and Local-Regional Control of Breast Cancer.

Authors:  Simona Maria Fragomeni; Andrew Sciallis; Jacqueline S Jeruss
Journal:  Surg Oncol Clin N Am       Date:  2018-01       Impact factor: 3.495

Review 5.  Breast cancer intrinsic subtype classification, clinical use and future trends.

Authors:  Xiaofeng Dai; Ting Li; Zhonghu Bai; Yankun Yang; Xiuxia Liu; Jinling Zhan; Bozhi Shi
Journal:  Am J Cancer Res       Date:  2015-09-15       Impact factor: 6.166

6.  Ethics of mandatory research biopsy for correlative end points within clinical trials in oncology.

Authors:  Jeffrey Peppercorn; Iuliana Shapira; Deborah Collyar; Teresa Deshields; Nancy Lin; Ian Krop; Hans Grunwald; Paula Friedman; Ann H Partridge; Richard L Schilsky; Monica M Bertagnolli
Journal:  J Clin Oncol       Date:  2010-04-20       Impact factor: 44.544

7.  Testing violations of the exponential assumption in cancer clinical trials with survival endpoints.

Authors:  Gang Han; Michael J Schell; Heping Zhang; Daniel Zelterman; Lajos Pusztai; Kerin Adelson; Christos Hatzis
Journal:  Biometrics       Date:  2016-09-26       Impact factor: 2.571

8.  Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree.

Authors:  Yang Li; Xu-Qing Tang; Zhonghu Bai; Xiaofeng Dai
Journal:  Sci Rep       Date:  2016-10-27       Impact factor: 4.379

9.  Gene expression profiling of breast cancer.

Authors:  Lajos Pusztai
Journal:  Breast Cancer Res       Date:  2009-12-18       Impact factor: 6.466

10.  Comparison of Residual Risk-Based Eligibility vs Tumor Size and Nodal Status for Power Estimates in Adjuvant Trials of Breast Cancer Therapies.

Authors:  Wei Wei; Tomoko Kurita; Kenneth R Hess; Tara Sanft; Borbala Szekely; Christos Hatzis; Lajos Pusztai
Journal:  JAMA Oncol       Date:  2018-04-12       Impact factor: 31.777

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