ArticleFrontiers in genetics2019
Gene Expression-Based Predictive Markers for Paclitaxel Treatment in ER+ and ER- Breast Cancer.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Article
- FZR1 as a novel biomarker for breast cancer neoadjuvant chemotherapy prediction.Cell death & disease · 2020Article
- Article
- In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.Journal of cellular and molecular medicine · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
One of the objectives of precision oncology is to identify patient's responsiveness to a given treatment and prevent potential overtreatments through molecular profiling. Predictive gene expression biomarkers are a promising and practical means to this purpose. The overall response rate of paclitaxel drugs in breast cancer has been reported to be in the range of 20-60% and is in the even lower range for ER-positive patients. Predicting responsiveness of breast cancer patients, either ER-positive or ER-negative, to paclitaxel treatment could prevent individuals with poor response to the therapy from undergoing excess exposure to the agent. In this study, we identified six sets of gene signatures whose gene expression profiles could robustly predict nonresponding patients with precisions more than 94% and recalls more than 93% on various discovery datasets (
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