Overview
- Editors:
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Alexey Larionov
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Academic Laboratory of Medical Genetics, School of Clinical Medicine, Cancer Research UK Cambridge Institute, Statistics and Computational Biology Laboratory & University of Cambridge, Cambridge, United Kingdom
- Brings together the current knowledge of molecular and translational aspects of AIs resistance in breast cancer
- Provides mechanisms of physiological resistance and potential biomarkers that recognize or predict mechanisms of resistance
- Explores acquired, adjuvant, pre-operative, and metastatic resistance
- Includes supplementary material: sn.pub/extras
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Table of contents (12 chapters)
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- Debashis Ghosh, Jessica Lo, Chinaza Egbuta
Pages 33-61
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- Gauri Sabnis, Angela Brodie
Pages 63-86
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- Elizabeth E. Sweeney, V. Craig Jordan
Pages 101-114
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- Jean McBryan, Leonie S. Young
Pages 115-144
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- Raffaella Maria Gadaleta, Luca Magnani
Pages 145-168
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- Abdul Aziz Bin Aiderus, Anita K. Dunbier
Pages 169-190
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- Alexey A. Larionov, William R. Miller
Pages 191-228
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- Hazel Lote, Stephen Johnston
Pages 229-259
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Back Matter
Pages 287-288
About this book
Aromatase Inhibitors (AIs) treat postmenopausal estrogen receptor positive tumours, which constitute the majority of breast cancer patients. This comprehensive volume brings together the current knowledge from different relevant areas, including molecular mechanisms and translational aspects of drug resistance in AIs. Topics covered include research, experimental , and clinical data specifically focused on AI resistance in breast cancer. The volume will include three sections. The first section covers general knowledge about aromatase inhibitors, including regulation of aromatase genes, and structure and function of aromatase protein. The second section provides the detailed mechanisms of resistance to AIs, while the third section explores prediction of resistance and potential strategies to overcome resistance. Breast cancer is the most common female cancer and AIs significantly improve treatments outcomes compatibly to previously used endocrine treatments. However 10-15% of post-operative patients develop a relapse during adjuvant treatment with AIs; about 25-50% of the patients do not respond to AIs in neo-adjuvant or metastatic setting, and the majority of metastatic patients who initially respond develop resistance within 3 years. There is an important need to understand these mechanisms of resistance in order to develop methods of preventing or overcoming the resistance to AIs, which will ensure a more successful outcome in treating breast cancer.
Editors and Affiliations
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Academic Laboratory of Medical Genetics, School of Clinical Medicine, Cancer Research UK Cambridge Institute, Statistics and Computational Biology Laboratory & University of Cambridge, Cambridge, United Kingdom
Alexey Larionov
About the editor
Alexey Larionov, Ph.D. is a researcher at the Academic Laboratory of Medical Genetics and Statistics and Computational Biology Lab at the University of Cambridge. Dr Larionov is an expert in endocrine resistance in breast cancer, as well as whole exome sequencing data analysis. Dr. Larionov has received over 1019 citations.
