Ductal carcinoma in situ (DCIS) is a non-invasive proliferation of neoplastic cells within the duct of the mammary gland that represents around 20% of newly diagnosed breast cancers. It is a potential precursor of invasive breast cancer. DCIS patients are treated with surgery and radiation treatment. Radiation treatment enables significantly better local control. However, most of patients treated with radiation will experience acute or late adverse events that importantly decrease patient’s quality of life. There is great interindividual variability in occurrence of adverse events and the key challenge is therefore how predict and to limit the toxicity without compromising the efficacy of the treatment. At the moment, there is no management strategy that would allow treatment personalisation regarding the risk of adverse events in clinical practice. Reliable and easy to use molecular predictors of radiation treatment response that would enable patient stratification and treatment selection in DCIS, limiting adverse events and improving treatment outcome are therefore needed.
Some studies have already shown that genomic and plasma (extracellular vesicles and miRNAs) biomarkers can be associated with radiation treatment response and occurrence of adverse events. Most previous genomic studies focused on single nucleotide polymorphisms, however telomere length dynamics could also help predict response to radiation treatment. In recent years, extracellular vesicles and miRNAs from plasma emerged as new additional potential noninvasive predictors in breast cancer. Comprehensive pathway and bioinformatic analysis of published data obtained with different omics approaches would enable identification of novel potential biomarkers to be evaluated on clinical samples. However, there are currently no molecular predictors or models of radiation treatment response in DCIS that could be used in the clinical setting. If a validated molecular signature combining clinical data, genetic factors, miRNAs and characteristics of extracellular vesicles could predict acute or late toxicity, such a finding could be very important for the development of personalised radiation treatment.
The aim of our study is therefore to find novel molecular predictors of radiation treatment response in DCIS patients by integrating bioinformatics data, genetic and plasma biomarkers. We will first identify novel potential biomarkers of radiation treatment response using pathway based and bioinformatic approaches. We will then investigate these potential genomic and plasma biomarkers in a clinically well-defined cohort of DCIS patients receiving adjuvant radiation treatment. We will also prepare multivariable predictive models of radiation treatment response integrating the clinical, radiation therapy parameters and biomarker data that will enable translation of our results in the clinical practice for personalization of radiation treatment.