Traditionally the stratification of many cancers involves combining tumour and clinicopathological features (e.g., patient age; tumour size, grade, receptor status and location) to inform treatment options and predict recurrence risk and survival. However, current biomarkers often require invasive excision of the tumour for profiling, do not allow monitoring of the response to treatment and stratify patients into broad heterogeneous groups leading to inconsistent treatment responses. Here we explore and describe the benefits of using circulating biomarkers (nucleosomes and/or modifications to nucleosomes) as a non-invasive method for detecting cancer and monitoring response to treatment. Nucleosomes (DNA wound around eight core histone proteins) are responsible for compacting our genome and their composition and post-translational modifications are responsible for regulating gene expression. Here, we focus on breast and colorectal cancer as examples where utilizing circulating nucleosomes as biomarkers hold real potential as liquid biopsies. Utilizing circulating nucleosomes as biomarkers is an exciting new area of research that promises to allow both the early detection of cancer and monitoring of treatment response. Nucleosome-based biomarkers combine with current biomarkers, increasing both specificity and sensitivity of current tests and have the potential to provide individualised precision-medicine based treatments for patients.

Breast cancer is the leading cause of cancer related death in women, with metastasis the principle cause of mortality. New non-invasive prognostic markers are needed for the early detection of metastasis, facilitating treatment decision optimisation. MicroRNA (miRNA) are small, non-coding RNAs regulating gene expression and involved in many cellular processes, including metastasis. As biomarkers, circulating miRNAs (in blood) hold great promise for informing diagnosis or monitoring treatment responses. Plasma extracted RNA from age matched local Luminal A (n = 4) or metastatic disease (n = 4) were profiled using Next Generation Sequencing. Selected differentially expressed miRNA were validated on a whole blood extracted miRNA cohort [distant metastatic disease (n = 22), local disease (n = 31), healthy controls (n = 21)]. Area Under the Curve (AUC) in Receiver Operating Characteristic (ROC) analyses was performed. Of 4 miRNA targets tested (miR-181a, miR-329, miR-331, miR-195), mir-331 was significantly over-expressed in patients with metastatic disease, compared to patients with local disease (p < 0.001) or healthy controls (p < 0.001). miR-195 was significantly under-expressed in patients with metastatic disease, compared to patients with local disease (p < 0.001) or healthy controls (p = 0.043). In combination, miR-331 and miR-195 produced an AUC of 0.902, distinguishing metastatic from local breast cancer. We identified and validated two circulating miRNAs differentiating local Luminal A breast cancers from metastatic breast cancers. Further investigation will reveal the molecular role of these miRNAs in metastasis, and determine if they are subtype specific. This work demonstrates the ability of circulating miRNA to identify metastatic disease, and potentially inform diagnosis or treatment effectiveness.

Recent studies have shown that breast cancer subtype can change from the primary tumour to the recurrence. Discordance between primary and recurrent breast cancer has implications for further treatment and ultimately prognosis. The aim of the study was to determine the rate of change between primary and recurrence of breast cancer and to assess the impact of these changes on survival and potential treatment options. Patient demographics were collected on those who underwent surgery for breast cancer between 2001 and 2014 and had a recurrence with biopsy results and pathology scoring of both the primary and recurrence. One hundred thirty two consecutive patients were included. There were 31 (23.5%) changes in subtype. Discordance occurred most frequently in luminal A breast cancer (n = 20), followed by triple negative (n = 4), luminal B (n = 3) and HER2 (n = 3). Patients who changed from luminal A to triple negative (n = 18) had a significantly worse post-recurrence survival (p < 0.05) with overall survival approaching significance (p = 0.064) compared to concordant luminal A cases (n = 46). Overall receptor discordance rates were: estrogen receptor 20.4% (n = 27), progesterone receptor 37.7% (n = 50) and HER2 3% (n = 4). Loss of estrogen receptor and progesterone receptor was more common than gain (21 vs. 6 (p = 0.04) and 44 vs. 6 (p = 0.01) respectively). Nine patients (6.8%) gained receptor status potentially impacting treatment options. Discordance in subtype and receptor status occurs between primary and recurrent breast cancer, ultimately affecting survival and potentially impacting treatment options.

Objective: The Wavelia Microwave Breast Imaging (MBI) system, based on non-ionising imaging technology, has demonstrated exciting potential in the detection and localisation of breast pathology in symptomatic patients. In this study, the ability of the system to accurately estimate the size and likelihood of malignancy of breast lesions is detailed, and its clinical usefulness determined. Methods: Institutional review board and Health Products Regulatory Authority (HPRA) approval were obtained. Patients were recruited from the symptomatic unit to three groups; breast cancer (Group-1), unaspirated cysts (Group-2) and biopsied benign lesions (Group-3). MBI, radiological and histopathological findings were reviewed. MBI size estimations were compared with the sizes determined by conventional imaging and histopathology. A Quadratic Discriminant Analysis (QDA) classifier was trained in a 3D feature space to discriminate malignant from benign lesions. An independent review was performed by two independent breast radiologists. Results: 24 patients (11 Group-1, 8 Group-2 and 5 Group-3) underwent MBI. The Wavelia system was more accurate than conventional imaging in size estimation of breast cancers. The QDA accurately separated benign from malignant breast lesions in 88.5% of cases. The addition of MBI and the Wavelia malignancy risk calculation was deemed useful by the two radiologists in 70.6% of cases. Conclusion: The results from this MBI investigation demonstrate the potential of this novel system in estimating size and malignancy risk of breast lesions. This system holds significant promise as a potential non-invasive, comfortable, and harmless adjunct for breast cancer diagnosis. Further larger studies are under preparation to validate the findings of this study. Advances in knowledge: This study details the potential of the Wavelia MBI system in delineating size and malignancy risk of benign and malignant breast lesions in a symptomatic cohort. The usefulness of the Wavelia system is assessed in the clinical setting.

Background: Medical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.Methods: Data on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller–Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.Results: 74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.Conclusion: This study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.