Abstract P4-08-17: Expression score (Escore) for the prediction of likelihood of recurrence of DCIS
Authors: Badve SS , Cho S , Gokmen-Polar Y , Zavodszky M , Sui Y , Chadwick C , Tan PH , Gerdes M , Harris AL , Ginty F 
Published: May 2019

Background: Ductal carcinoma in situ (DCIS) accounts for at least 20% of breast cancers. Factors associated with recurrence of DCIS or progression to invasive carcinoma are not well delineated. The goals of the current study were to profile the epithelial cells using the GE Cell DIVE™ immuno-fluorescent based analyses. This was coupled with semi-automated algorithms to characterize the inter-relationships between cell populations and likelihood of recurrence.

Patients and Methods: A TMA-based (total 8 TMAs) cohort of cases of DCIS with and without recurrence was obtained from Oxford University. Recurrence in this cohort was defined as ipsilateral DCIS, ipsilateral invasive, contralateral invasive and metastatic. Analysis for 31 epithelial markers (HER4, CK56, ABCG2, PTEN, S6, CKAE1, PR, ER, NaKATPase, CK19, ALDH1, CK PCK26, cMET, CD44v6, HER2, CDCP1, p53, CK15, COX2, VEGFR2, ABCb1, HTF9C, CD10, MRP4, CEACAM5, EGFR, p21, MRP5, SLC7A5, Ki67, DAPI) was performed on a single FFPE TMA section containing cases of DCIS. Briefly, FFPE sections from TMAs containing DCIS were sequentially (cyclically) stained for the markers. Each cycle entailed staining with 2-3 markers followed by imaging, dye inactivation, and re-staining. DAPI was used for nuclear demarcation and for registration of the images, while S6, pan-cadherin, Na+K+ATPase and pan-cytokeratin were used for epithelial segmentation. K-means clustering followed regression analysis was performed to identify inter-relationships between markers and association with likelihood of recurrence. Log-rank analysis was performed and the relapse-free survival data depicted using Kaplan Meier plots. Escore was developed by logistic regression model, classification model on recurrence

Results: Filtering of the expression analysis by the quality, specificity, compartment localization and fields entirely composed of DCIS, in addition to availability of clinical data resulted final analysis of 31 markers in 67 cases. Correlation analyses were performed on each of the markers to identify markers that were significantly correlated in univariate analysis. K-means cluster analysis was performed using a set of 4 markers (ER, HER2, SLC7A5 and cMET) to identify 6 clusters. High cMET (cluster 1; low HER2 and SLC7A5) and High ER (low cMET, HER2, SLC7A5; Cluster 5) were associated with low risk of recurrence (p values 0.014 and &lt;0.0001). In contrast, Cluster 2 (High HER2, high SLC7A5, low ER) and Cluster 3 (High HER2, low ER, SLC7A5and cMET) were associated with increased risk of recurrence (P values 0.038 and 0.076). A regression analysis based algorithm was developed using these markers to calculate a numerical score which could predict likelihood of recurrence. As depicted in the KM plots, the HR for recurrence increases significantly (P-value 2.4E-05; p=0.02 with LOOCV) with increase in expression score (Escore).

Conclusions: We describe the development of an Escore using expression 4 markers to predict likelihood of recurrence. Additional ongoing studies will seek to validate the utility of the Escore in predicting likelihood of recurrence of DCIS and development of invasive carcinomas and comparison with other scoring systems.

Citation Format: Badve SS, Cho S, Gokmen-Polar Y, Zavodszky M, Sui Y, Chadwick C, Tan PH, Gerdes M, Harris AL, Ginty F. Expression score (Escore) for the prediction of likelihood of recurrence of DCIS [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-17.