A Biological Signature for Breast Ductal Carcinoma In Situ to Predict Radiotherapy Benefit and Assess Recurrence Risk
Troy Bremer1, Pat W Whitworth2, Rakesh Patel3
1PreludeDx, Laguna Hills, California. tbremer@preludedx.com Fredrik.Warnberg@surgsci.uu.se.
Purpose:
Ductal carcinoma in situ (DCIS) patients and their physicians currently face challenging treatment decisions with limited information about the individual's subsequent breast cancer risk or treatment benefit. The DCISionRT biological signature developed in this study provides recurrence risk and predicts radiotherapy (RT) benefit for DCIS patients following breast-conserving surgery (BCS).
Experimental Design:
A biological signature that calculates an individualized Decision Score (DS) was developed and cross-validated in 526 DCIS patients treated with BCS ± RT. The relationship was assessed between DS and 10-year risk of invasive breast cancer (IBC) or any ipsilateral breast event (IBE), including IBC or DCIS. RT benefit was evaluated by risk group and as a function of DS.
Results:
The DS was significantly associated with IBC and IBE risk, HR (per 5 units) of 4.2 and 3.1, respectively. For patients treated without RT, DS identified a Low Group with 10-year IBC risk of 4% (7% IBE) and an Elevated Risk Group with IBC risk of 15% (23% IBE). In analysis of DS and RT by group, the Elevated Risk Group received significant RT benefit, HR of 0.3 for IBC and IBE. In a clinicopathologically low-risk subset, DS reclassified 42% of patients into the Elevated Risk Group. In an interaction analysis of DS and RT, patients with elevated DS had significant RT benefit over baseline.
Conclusions:
The DS was prognostic for risk and predicted RT benefit for DCIS patients. DS identified a clinically meaningful low-risk group and a group with elevated 10-year risks that received substantial RT benefit over baseline.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.


