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Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification

Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning... Background The breast cancer (BC) epidemic is a multifactorial disease attributed to the early twenty-first century: about two million of new cases and half a million deaths are registered annually worldwide. New trends are emerging now: on the one hand, with respect to the geographical BC prevalence and, on the other hand, with respect to the age distribution. Recent statistics demonstrate that young populations are getting more and more affected by BC in both Eastern and Western countries. Therefore, the old rule “the older the age, the higher the BC risk” is getting relativised now. Accumulated evidence shows that young premenopausal women deal with particularly unpredictable subtypes of BC such as triple-negative BC, have lower survival rates and respond less to conventional chemotherapy compared to the majority of postmenopausal BC. Working hypothesis Here we hypothesised that a multi-level diagnostic approach may lead to the identification of a molecular signature highly specific for the premenopausal BC. A multi-omic approach using machine learning was considered as a potent tool for stratifying patients with benign breast alterations into well-defined risk groups, namely individuals at high versus low risk for breast cancer development. Results and conclusions The study resulted in identifying multi-omic signature http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EPMA Journal Springer Journals

Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification

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Publisher
Springer Journals
Copyright
Copyright © 2018 by European Association for Predictive, Preventive and Personalised Medicine (EPMA)
Subject
Biomedicine; Biomedicine, general; Medicine/Public Health, general
ISSN
1878-5077
eISSN
1878-5085
DOI
10.1007/s13167-018-0131-0
Publisher site
See Article on Publisher Site

Abstract

Background The breast cancer (BC) epidemic is a multifactorial disease attributed to the early twenty-first century: about two million of new cases and half a million deaths are registered annually worldwide. New trends are emerging now: on the one hand, with respect to the geographical BC prevalence and, on the other hand, with respect to the age distribution. Recent statistics demonstrate that young populations are getting more and more affected by BC in both Eastern and Western countries. Therefore, the old rule “the older the age, the higher the BC risk” is getting relativised now. Accumulated evidence shows that young premenopausal women deal with particularly unpredictable subtypes of BC such as triple-negative BC, have lower survival rates and respond less to conventional chemotherapy compared to the majority of postmenopausal BC. Working hypothesis Here we hypothesised that a multi-level diagnostic approach may lead to the identification of a molecular signature highly specific for the premenopausal BC. A multi-omic approach using machine learning was considered as a potent tool for stratifying patients with benign breast alterations into well-defined risk groups, namely individuals at high versus low risk for breast cancer development. Results and conclusions The study resulted in identifying multi-omic signature

Journal

EPMA Journal – Springer Journals

Published: Apr 11, 2018

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