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Tumour evolution and novel biomarkers in breast cancer

Doctoral thesis
Authors Jana Biermann
Date of public defense 2019-05-24
ISBN 978-91-7833-440-7 (PRINT); 978-91-7833-441-4 (PDF)
Publication year 2019
Published at Institute of Clinical Sciences, Department of Oncology
Sahlgrenska Cancer Center
Language en
Links hdl.handle.net/2077/59802
Keywords breast cancer, gene signature, molecular biomarkers, tumour clonality, genomic instability, Swedish haemangioma cohort
Subject categories Bioinformatics and Systems Biology, Genetics, Molecular biology, Biostatistics, Bioinformatics (Computational Biology), Cancer and Oncology

Abstract

Several gene signatures have been proposed in the past two decades to improve outcome prediction for breast cancer patients and to guide treatment decisions. Current treatment guidelines, however, primarily focus on established clinicopathological features. In Paper I, we identified a novel 18-marker gene expression signature predicting breast cancer-specific survival. The 18-marker signature was validated in three independent cohorts and showed increased predictive power over the clinically validated Oncotype Dx signature. Despite increasing survival rates, about 6-23% of patients suffer from recurrences within five years of initial diagnosis indicating treatment failure. It is highly important to differentiate between clonally related recurrences and independent primary tumours due to potentially differing prognoses and treatment regimes. Currently, there is no consensus on how to define clonal relatedness between multiple tumours in the same patient. In Paper II, we identified the Similarity Index (SI) as the most reliable tool to classify tumour clonality. The mammary gland is known to be highly sensitive to radiation, especially at a young age. In the years from 1920-1965, a total of 17,200 female Swedish infants were treated with ionizing radiation for skin haemangioma, resulting in an increased risk of developing breast cancer. In Paper III, we analysed breast tumours for genomic instability, which can be induced by ionizing radiation. Patients with higher absorbed doses to the breast exhibited increased genomic instability compared to patients exposed to lower absorbed doses. These results strongly suggest radiation-induced genomic instability as a biological link between ionizing radiation exposure at a young age and the increased breast cancer risk in subsequent decades. In conclusion, this work highlights the importance of complementing established clinicopathological features with molecular biology and statistical models to improve breast cancer risk assessment and personalize treatment strategies.

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