Abstract PO4-13-02: Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS
Authors: Hahn Ezra , Sutradhar Rinku , Paszat Lawrence , Nguyen Lena , Rodin Danielle , Nofech-Mozes Sharon , Trebinjac Sabina , Fong Cindy , Rakovitch Eileen 
Published: May 2024

Introduction

Ductal carcinoma in situ (DCIS) is routinely treated with adjuvant radiotherapy (RT) after breast conserving surgery (BCS) in order to reduce the risk of local recurrence (LR) and invasive LR. Nomograms based on clinicopathological features (CPF) and molecular expression assays have been developed in an effort to provide individualized risk estimates and personalize decision-making. However, molecular assays are costly and it remains unclear if they provide more accurate recurrence risk estimates compared to algorithms based on CPF alone. We examined the impact of the 12-Gene DCIS Score (DS) and the 21-Gene Recurrence Score (RS) molecular expression assays, in addition to CPF, on the accuracy of predicting 10-year LR and invasive LR risk compared to predicted estimates based on CPF alone. In addition, we examined if a model including the 21-Gene RS improves the 10-year predicted risks of invasive LR after BCS for DCIS compared to estimates based on the DS+CPF or CPF alone.

Methods

We used a population-based cohort diagnosed with pure DCIS treated with BCS +/- RT from 1994-2003. All cases had expert pathology review providing contemporary assessment of diagnosis, margin status, margin width, multifocality, presence and extent of comedo necrosis, subtype, nuclear grade, and tumor size. For each case, a representative tissue block or unstained slide was sent to measure the 12-Gene DS and 21-Gene RS. Predictive models were developed using multivariable Cox regression analyses with backward selection and included all CPF, treatment with RT, and interactions. The performance of each model was evaluated based on c-statistic, -2log likelihood estimate (-2LLE), and Akaike information criterion (AIC). Calibration was performed using bootstrap resamples, with replacement. We compared the performance of the best model derived from CPF alone, the 12-Gene DS with CPF, and the 21-Gene RS with CPF on their ability to predict the 10 year risks of LR and invasive LR measured against outcomes observed in the cohort.

Results

The population-based cohort includes 1226 women, 514 were treated with BCS alone and 712 were treated with BCS + RT. Median age was 56 years. Median follow-up was 10 years. Fifty-two percent of tumors were between 1 and 2.5 cm, 35% were ≤1cm, and 13% were &gt;2.5 cm. Comedo necrosis was present in 68%, and nuclear grade was low, moderate, and high in 7%, 54%, and 39%, respectively. Margins were negative in 90.5% of cases (N=1109). The 12-Gene DS was low, intermediate, and high in 53.5%, 20.9%, and 25.6% and the 21-Gene RS was &gt;25 in 30% of patients. 194 women (15.8%) experienced ipsilateral LR as a first event; 112 were invasive LR.

Models including either the DS or RS expression assays performed better in predicting the 10-year risk of LR after BCS compared to the model based on CPFs alone, demonstrating higher c-statistics (0.705, 0.699, and 0.662, respectively), lower AIC and lower -2LLE. The two molecular-based predictive models also performed better in predicting the risk of invasive LR compared to CPF model, although with smaller differences in c-statistics (0.684, 0.683, and 0.667, respectively), AIC or -2LLE. The predictive model based on the 21-Gene RS with CPF did not perform better in the prediction of the 10 year risk of invasive LR compared the 12-Gene DS + CPF model. All models were well calibrated.

Conclusion

The predictive model based on the 12-Gene DS with CPF more accurately predicted the 10-year risk of LR and invasive LR after BCS compared to model based on CPF alone. Inclusion of the 21-Gene RS with CPF did not improve the prediction of the 10-year risk of LR or invasive LR. This suggests that nomograms that include the 12-Gene assay with CPF provide more accurate individualized estimates of recurrence risk after BCS and can help improve personalized decision-making in the management of DCIS.

Citation Format: Ezra Hahn, Rinku Sutradhar, Lawrence Paszat, Lena Nguyen, Danielle Rodin, Sharon Nofech-Mozes, Sabina Trebinjac, Cindy Fong, Eileen Rakovitch. Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-02.