Abstract PS03-07: Morphometric signature identifies ductal carcinoma in situ of the breast with low risk of progression to invasive breast cancer

Abstract Background. Ductal carcinoma in situ (DCIS) is a frequently found precursor of invasive breast cancer (IBC). However, the majority of DCIS will never progress to IBC. As we cannot distinguish yet which DCIS will remain indolent (‘harmless’) from those that might progress in the future to IBC, almost all women with DCIS are intensively treated by surgery, often followed by radiotherapy. This brings an urgent clinical need to learn distinguishing harmless from potentially progressive DCIS to save many women with indolent DCIS the burden of unnecessary overtreatment. Aim. We aimed to investigate if the geometry and spatial configuration of DCIS ducts in Hematoxylin-Eosin (H&E) stained tissue sections are related to the risk of progression of DCIS to ipsilateral IBC (iIBC). Methods. We obtained data from a population-based cohort of women diagnosed with primary DCIS between 1989 and 2004 in the Netherlands, treated with breast conserving surgery (BCS) only and a median follow-up time of 12 years. A nested case-control study (n=689) was designed in which patients diagnosed with iIBC recurrences during follow-up were considered as “cases” (n= 226) and those with no subsequent iIBC as “controls” (n=463). The DCIS and stroma regions were digitally annotated on H&E-stained whole slide images (WSIs) by a pathologist as ground truth for the deep learning neural network of HALO AI module (IndicaLabs). We developed a computational pipeline to automatically detect and measure stroma areas, DCIS ducts, and the nucleus of their cells. We validated the accuracy of DCIS detection in H&Es WSIs from an external study, in which DCIS regions were digitally annotated by an independent pathologist (Translational Breast Cancer Research Consortium, TBCRC). We classified cases and controls according to morphological measurements using logistic ridge regression with double-loop cross-validation, followed by hierarchical clustering. The risk of subsequent iIBC after primary DCIS diagnosis was evaluated by multivariate Cox proportional hazards models. Results. The accuracy of DCIS detection performed by the computational pipeline was compared with pathologist annotations in 20 slides from the TBCRC study. Results showed a satisfactory DCIS overlap area agreement of 0.76 (0.68 – 0.83). We applied the DCIS computational pipeline on the case-control series. We obtained 15 morphological measurements for each DCIS duct, such as duct area, cell density, distance between ducts, average nucleus area, etc. We calculated 8 distribution parameters from each measurement in each WSI, including median and range. After leaving out redundant variables, 55 unique morphometric variables were obtained, representing the heterogeneity of DCIS ducts per WSI. The c lassifier revealed a median area-under the curve (AUC) of 0.66 (0.55-0.77) to predict 5-years free of iIBC, 0.59 (0.50-0.67) to predict 10-years and 0.60 (0.52-0.68) to predict 15-years. The 30 variables with the highest association with outcome were used to build four morphometric signatures. Signature number 1, which is characterized by lesions with small-sized ducts, a lower number of cells and a lower DCIS/stroma area ratio, showed a significant lower risk of developing iIBC compared to the other three signatures in a multivariate Cox regression model including grade, ER, COX-2 and HER2 expression: HR = 0.56 (0.28-0.78 95%CI). Conclusion. We developed a computational pipeline able to detect and measure DCIS ducts in H&E WSIs with high accuracy and reproducibility. DCIS lesions presenting the morphometric signature of small-sized DCIS ducts have a very low chance to progress to invasive breast cancer. After successful validation, our morphometric method will serve as a robust and easy to implement biomarker for de-escalation strategies in DCIS, and as such, could limit unnecessary overtreatment in the near future. Citation Format: Marcelo Sobral-Leite, Simon Castillo, Shiva Vonk, Xenia Melillo, Noomie Lam, Brandi de Bruijn, Yeman Hagos, Joyce Sanders, Mathilde Almekinders, Lindy Visser, Emilie Groen, Carolien Van der Borden, Petra Kristel, Ercan Caner, Leyla Azarang, Yinyin Yuan, Renee Menezes, Esther Lips, Jelle Wesseling, Grand Challenge PRECISION Consortium. Morphometric signature identifies ductal carcinoma in situ of the breast with low risk of progression to invasive breast cancer [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 PS03-07.