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EPMA Journal

, Volume 9, Issue 2, pp 175–186 | Cite as

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

  • Holger Fröhlich
  • Sabyasachi Patjoshi
  • Kristina Yeghiazaryan
  • Christina Kehrer
  • Walther Kuhn
  • Olga Golubnitschaja
Research

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 specific for the premenopausal BC that can be used for stratifying patients with benign breast alterations. Our predictive model is capable of discriminating individually between high and low BC-risk with high confidence (>90%) and considered of potential clinical utility. Novel risk assessment approaches and advanced screening programmes—as the long-term target of this project—are of particular importance for predictive, preventive and personalised medicine as the medicine of the future, due to the expected health benefits for young subpopulations and the healthcare system as a whole.

Keywords

Predictive preventive personalised medicine Breast cancer Menopause Patient stratification Bioinformatics Machine learning Multi-level diagnostics Biomarker panel Laboratory medicine 

Abbreviations

ACN

Acetonitrile

AUC

Area under ROC (receiver operating characteristic) curve

BC

Breast cancer

CA

Comet Assay

CA I, II, III

Comet classes I, II and III, respectively

Cat

Catalase

CHCA

α-cyano-4-hydroxycinnamic acid

GBM

Gradient Boosting Machine

Hcy

Homocysteine

H2O2

Hydrogen peroxide

MALDI-TOF

Matrix-assisted laser desorption/ionisation time-of-flight

TFA

Trifluoroacetic acid

NMF

Non-negative matrix factorisation

preBC

Premenopausal breast cancer

postBC

Postmenopausal breast cancer

ROS

Reactive oxygen species

SOD

Superoxide-dismutase

O

Superoxide radical

2D-PAGE

Two-dimensional poly-acrylamide gel electrophoresis

Notes

Acknowledgements

The authors thank Prof. Dr. H.J. Blom for measurements of homocysteine in blood plasma. The authors thank Dr. M. Fountoulakis and Dr. A. Papadopoulou for proteomic expertise strongly supported the project performance. Further, authors thank Ms. G. Windisch-Schuster for performing the Western blot analysis.

Authors’ contributions

OG created the concepts of the project and drafted the article. HF established the approach of machine learning applied to the project, and SP implemented the approach. KY carried out molecular biological investigations. HF and OG made the results interpretation. CK provided expertise in breast cancer. WK supervised the project with clinical expertise in breast cancer management.

Funding

The study funding has been performed by the Breast Cancer Research Centre, University of Bonn, Germany. KY has been awarded a fellowship by the European Association for Predictive, Preventive and Personalised Medicine (EPMA, Belgium).

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethical approval

All the patient investigations conformed to the principles outlined in the Declaration of Helsinki and have been performed with the permission (Nr. 148/05) released by the responsible Ethic’s Committee of the Medical Faculty, Rheinische Friedrich-Wilhelms-University of Bonn. Human rights have been obligatory protected during the entire duration of the project according to the European standards. All the patients were informed about the purposes of the study and have signed their “consent of the patient”. This article does not contain any studies with animals performed by any of the authors.

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Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2018

Authors and Affiliations

  • Holger Fröhlich
    • 1
  • Sabyasachi Patjoshi
    • 1
  • Kristina Yeghiazaryan
    • 2
    • 3
    • 4
  • Christina Kehrer
    • 3
    • 4
    • 5
  • Walther Kuhn
    • 3
    • 4
    • 5
  • Olga Golubnitschaja
    • 2
    • 3
    • 4
  1. 1.Bonn-Aachen International Centre for ITFriedrich-Wilhelms-Universität BonnBonnGermany
  2. 2.Radiological ClinicRheinische Friedrich-Wilhelms-Universität BonnBonnGermany
  3. 3.Breast Cancer Research CentreRheinische Friedrich-Wilhelms-Universität BonnBonnGermany
  4. 4.Centre for Integrated Oncology, Cologne-BonnRheinische Friedrich-Wilhelms-Universität BonnBonnGermany
  5. 5.Centre for Obstetrics and GynaecologyRheinische Friedrich-Wilhelms-Universität BonnBonnGermany

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