Abstract PO1-26-01: Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer

Abstract

Abstract Background: Patient-derived xenografts (PDXs) are pivotal in cancer research. Despite histopathological insights into factors driving PDX success, the role of artificial intelligence (AI) in predicting PDX engraftment remains unexplored. We aimed to bridge this gap by integrating clinicopathological data and AI-based morphometric analysis to predict PDX success in breast cancer. Methods: PDXs were generated from tumor tissues derived from breast cancer patients who had undergone surgical intervention. Clinicopathological information including subtypes, pathological diagnosis, modified Bloom-Richardson system histologic grades, treatment with neoadjuvant chemotherapy (NAC), Miller Payne grade, residual caner burden score, invasive tumor size, lymphovascular invasion status, AJCC 8th T and N stages and the percentage of tumor infiltrating lymphocytes were collected and analyzed. For the image analysis component, whole-slide images (WSIs) of hematoxylin and eosin–stained tissue samples from 64 surgically resected breast cancer patients were used as a training set for an AI model under the supervision of 2 pathologists to extract morphometric features. The model transformed image tiles into patches of morphologically similar patterns, and categorized them into adipose tissue, background, necrosis, invasive carcinoma, carcinoma in situ, stroma, and terminal ductal lobular unit. This trained model was subsequently applied to the WSIs employed in the establishment of PDXs. The classified patches and their relative ratios within the invasive tumor boundary were compiled. The consolidated data from clinicopathological and image analyses were subjected to logistic regression to discern correlates of successful PDX engraftment. Results: Out of the 311 patient tumor samples used for generating PDXs, (131 post-chemotherapy, 180 chemo-naïve), 47 PDXs were successfully established (15.1%). Logistic regression revealed several factors for successful engraftment including NAC treatment with an odds ratio of 6.71 (28.2% vs 5.6%, 95% CI: 2.53 - 17.80, p < 0.001), higher histologic grades (25.3% vs 2.2%, OR = 5.80, 95% CI: 1.49 - 22.63, p = 0.01), triple negative breast cancer compared to hormone receptor-positive cancers (32.0% vs 3.3%, OR = 10.99, 95% CI: 1.26 - 95.89, p = 0.03), and tumors of larger size (OR = 1.34, 95% CI: 1.02 - 1.76, p = 0.03). Interestingly, the percentage of specific tissue patch types within tumor did not significantly impact the likelihood of successful PDX engraftment. However, in our logistic regression analysis based solely on morphometric features, presence of necrosis within the tumor notably enhanced PDX establishment. Specifically, each percent increase in necrosis within tumor boosted the odds of successful PDX creation by 0.01% (OR = 1.0001, 95% CI: 1.00003 - 1.00024, p = 0.01). Conclusions: PDXs are often successfully established from clinically aggressive breast cancers, particularly those with NAC treatment, higher histologic grades, TNBC subtype, and larger tumor size. While morphometric features contribute to the prediction of PDX engraftment success, their importance is surpassed by these clinicopathological factors. However, presence of necrosis emerged as a key morphometric predictor of successful PDX engraftment. Keywords: breast cancer; patient-derived xenograft (PDX); Breast Cancer Morphometrics; Cancer Predictive Modeling. Citation Format: Jongwon Lee, GeonHee Lee, Gyungyub Gong, Hee Jin Lee. Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in 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 PO1-26-01.