Approximately 25% of breast cancer patients are now diagnosed with ductal carcinoma in situ (DCIS). Currently, predicting local recurrence in patients with DCIS is imprecise, leading to substantial variability in treatment. We aim to identify markers that predict DCIS patients who are most at risk of relapse, and those at low risk that could be potentially spared aggressive interventions.
This study utilises a large cohort (1126 DCIS cases), including patients from the Parkville Medical Precinct, Victoria, diagnosed during 1994-2018 with a median age of diagnosis of 58 years and a median follow-up period of 8 years. Importantly for this study, the majority had wide excision (WE) surgery (n= 1055, 94%) and were not treated with radiotherapy (n=726, 64%). Multivariate analysis of the standard pathological variables/markers revealed that patients with high-grade DCIS were more likely to have invasive ipsilateral breast cancer events compared to those with low-grade when adjusted for age, radiation, estrogen (ER) and progesterone (PR) status (hazard ratio: 2.72, p-value: 0.04). Patients with a PR positive status were 41% less likely to develop any ipsilateral event (hazard ratio: 0.59, p-value: 0.02) compared to a PR negative status.
We are currently focused on the addition of novel myoepithelial and immune biomarkers as prognostic biomarkers in DCIS. A subset of the DCIS cohort (n=99) has been assessed for expression of novel immune and myoepithelial markers that have been identified using multiplex Opal IHC and a high-content siRNA 3D co-culture screen to identify myoepithelial cell-derived breast cancer suppressor proteins. We are now validating the function of these proteins in suppressing early cancer invasion. To date, this work has revealed that higher proportions of a ‘memory’ immune marker predicted those most at risk of relapse in DCIS and myoepithelial protein expression was lost in high-grade DCIS, the grade most likely to develop invasive relapse.
Given clinicopathological factors are failing to identify a low-risk group in DCIS, our work offers a new approach to patient stratification to ensure treatment is personalised and overtreatment is reduced.