Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset

BioData Mining
Heloisa Helena MilioliPablo Moscato

Abstract

Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset. In this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients' overall survival than those originally provided by the PAM50 method. The assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing...Continue Reading

References

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Citations

Sep 12, 2018·Molecular Genetics and Genomics : MGG·Xiaoyong PanYu-Dong Cai
Dec 30, 2016·Future Science OA·Heloisa Helena Milioli
Sep 22, 2020·Computational and Structural Biotechnology Journal·Md Mohaiminul IslamPingzhao Hu

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Software Mentioned

Weka
METABRIC
R
survival

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