Public Abstract

DE-SC0025403: Automated High Throughput Screening of the Effect of Low-Dose Radiation on Breast using Fibroblasts Incorporated Mammographic Density Models

Award Status: Active

Institution: Board of Regents of the UW System for UW-Milwaukee, Milwaukee, WI
UEI: JBQ9M3PLFDP5
DUNS: 627906399

Most Recent Award Date: 09/17/2024
Number of Support Periods: 1
PM: Kulkarni, Resham

Current Budget Period: 09/01/2024 - 08/31/2025
Current Project Period: 09/01/2024 - 08/31/2027
PI: Cheng, Qingsu

Supplement Budget Period: N/A

Public Abstract

In the US, one in eight women will develop breast cancer during their lifetime, with the risk increasing for those with high-density breasts. Radiation is a known additive risk factor for breast cancer, but the effects of low-dose and low-dose-rate radiation remain poorly understood. The US population is exposed to these types of radiation through natural sources, medical procedures, and occupational settings. It is crucial to link low-dose and low-dose-rate radiation exposure to adverse health effects, including cancer susceptibility, progression, and metastasis. However, cancer development is complex and involves interactions with stromal cells, such as fibroblasts, which can inhibit or promote microenvironment tumor growth. This complexity requires a thorough investigation of the phenotypic and molecular responses to low-dose and low-dose-rate radiation at both the cellular and organoid levels, particularly regarding the interplay between cancer cells and stromal cells. To address this need, we will utilize our combined expertise in radiation, breast cancer biology, and computational biology to elucidate the adverse health effects of low-dose and low-dose-rate radiation on breast tissue. The overarching goal of this initiative is to identify (i) phenotypic markers indicating the effects of low-dose and low-dose-rate radiation on breast tissue and (ii) a set of abnormal genetic and protein expression patterns associated with various aspects of breast cancer. We hypothesize that high mammographic density combined with exposure to low-dose and low-dose-rate radiation, within the range of occupational and medical imaging doses, increases the likelihood of developing benign conditions, which may subsequently lead to cancer. To test this hypothesis, we plan to develop a high-throughput 3D coculture model that mimics mammographic density using multi-head 3D printing. We will print 3D cultures of primary epithelial cells and coculture them with primary mammary gland fibroblasts. We will then examine the interaction between fibroblasts and epithelial cells under low-dose radiation, within the occupational exposure limit and medical imaging range, through various biological analyses of relevant biomarkers. These analyses will include immunofluorescent staining, qPCR, Western blotting, ELISA assays, and Multi-OMICs. Additionally, we will develop an unsupervised deep learning framework for high-throughput profiling of the phenotypic and molecular changes using a CustomNet. This CustomNet will synergistically combine customized convolutional blocks with advanced axial attention mechanisms to provide a robust and nuanced understanding of image features. To ensure the rigor of our research, we will authenticate all lab materials and collaborate closely with a biostatistician throughout the experimental design, implementing appropriate statistical methods. Our innovations include modulating stiffness within the range of mammographic density in 3D coculture assays for high-throughput imaging using primary cells, specifically in the context of low-dose and low-dose-rate radiation. We aim to deliver quantifiable metrics on colony organization and heterogeneity using deep learning algorithms to decipher fibroblast-epithelium interactions. This approach will pave the way for developing targeted interventions and personalized treatment strategies using generative AI. Furthermore, our efforts will lay the groundwork for creating digital twins of our experimental models, enabling the exploration of the nuanced relationship between low-dose and low-dose-rate radiation exposure and breast cancer, providing valuable insights that could inform public health policies and clinical practices, and facilitating the design and administration of reagents to reduce individual risks.

In the US, one in eight women will develop breast cancer during their lifetime, with the risk increasing for those with high-density breasts. Radiation is a known additive risk factor for breast cancer, but the effects of low-dose and low-dose-rate radiation remain poorly understood. The US population is exposed to these types of radiation through natural sources, medical procedures, and occupational settings. It is crucial to link low-dose and low-dose-rate radiation exposure to adverse health effects, including cancer susceptibility, progression, and metastasis. However, cancer development is complex and involves interactions with stromal cells, such as fibroblasts, which can inhibit or promote microenvironment tumor growth. This complexity requires a thorough investigation of the phenotypic and molecular responses to low-dose and low-dose-rate radiation at both the cellular and organoid levels, particularly regarding the interplay between cancer cells and stromal cells. To address this need, we will utilize our combined expertise in radiation, breast cancer biology, and computational biology to elucidate the adverse health effects of low-dose and low-dose-rate radiation on breast tissue.

The overarching goal of this initiative is to identify (i) phenotypic markers indicating the effects of low-dose and low-dose-rate radiation on breast tissue and (ii) a set of abnormal genetic and protein expression patterns associated with various aspects of breast cancer. We hypothesize that high mammographic density combined with exposure to low-dose and low-dose-rate radiation, within the range of occupational and medical imaging doses, increases the likelihood of developing benign conditions, which may subsequently lead to cancer.

To test this hypothesis, we plan to develop a high-throughput 3D coculture model that mimics mammographic density using multi-head 3D printing. We will print 3D cultures of primary epithelial cells and coculture them with primary mammary gland fibroblasts. We will then examine the interaction between fibroblasts and epithelial cells under low-dose radiation, within the occupational exposure limit and medical imaging range, through various biological analyses of relevant biomarkers. These analyses will include immunofluorescent staining, qPCR, Western blotting, ELISA assays, and Multi-OMICs. Additionally, we will develop an unsupervised deep learning framework for high-throughput profiling of the phenotypic and molecular changes using a CustomNet. This CustomNet will synergistically combine customized convolutional blocks with advanced axial attention mechanisms to provide a robust and nuanced understanding of image features. To ensure the rigor of our research, we will authenticate all lab materials and collaborate closely with a biostatistician throughout the experimental design, implementing appropriate statistical methods.

Our innovations include modulating stiffness within the range of mammographic density in 3D coculture assays for high-throughput imaging using primary cells, specifically in the context of low-dose and low-dose-rate radiation. We aim to deliver quantifiable metrics on colony organization and heterogeneity using deep learning algorithms to decipher fibroblast-epithelium interactions. This approach will pave the way for developing targeted interventions and personalized treatment strategies using generative AI. Furthermore, our efforts will lay the groundwork for creating digital twins of our experimental models, enabling the exploration of the nuanced relationship between low-dose and low-dose-rate radiation exposure and breast cancer, providing valuable insights that could inform public health policies and clinical practices, and facilitating the design and administration of reagents to reduce individual risks.