Network-guided prediction of aromatase inhibitor response in breast cancer
نویسندگان
چکیده
منابع مشابه
Aromatase inhibitor strategies in metastatic breast cancer
Despite ongoing therapeutic innovations, metastatic breast cancer (MBC) remains a treatable but incurable disease. In the developed world, a diagnosis of MBC without a preceding diagnosis of early stage disease is a rare event. However, approximately one-third of women with early stage breast cancer ultimately experience a distant recurrence. Because the majority of breast cancers express estro...
متن کاملMultiple forms of aromatase and response of breast cancer aromatase to antiplacental aromatase II antibodies.
Two distinct aromatase-active protein complexes are solubilized by use of deoxycholate and separated by diethylamino-ethyl-cellulose chromatography from lyophilized powder of 900 X g precipitate fraction of human term placenta. Aromatase activity to produce estriol, the major estrogen of human pregnancy, was designated to be aromatase I activity and measured by estriol formation from 16 alpha-h...
متن کاملBax/Bcl-2 expression ratio in prediction of response to breast cancer radiotherapy
Objective(s): Radiotherapy is one of the most effective modalities of cancer therapy, but clinical responses of individual patients varies considerably. To enhance treatment efficiency it is essential to implement an individual-based treatment. The aim of present study was to identify the mechanism of intrinsic apoptosis pathway on radiosensitivity and normal tissue complications caused by the ...
متن کاملCharacterising an aromatase inhibitor resistant breast cancer cell line
Introduction Aromatase inhibitors (AI) are a novel adjuvant endocrine treatment for estrogen receptor (ER)-positive, postmenopausal breast cancer. They function by inhibiting the aromatase enzyme that converts androgens into estrogens. AIs have demonstrated excellent efficacy in clinical trials and have shown supremacy over Tamoxifen. However, prolonged use of AIs can lead to acquired resistanc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2019
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1006730