Leveraging Digital Pathology to Characterize Intra-Tumor Heterogeneity in Prostate Cancer for Optimizing Radiation Therapy
This project aims to use digitized pathology slides to explore intra-tumor heterogeneity at the cellular and spatial levels in prostate cancer, identifying histopathological features that correlate with tumor radiosensitivity. The digitized pathology data will be analyzed using advanced machine learning algorithms for image segmentation, classification, and pattern recognition, mapping the distribution of different cell populations and tumor regions. The ultimate goal is to integrate this information into a digital twin platform that simulates tumor behavior and radiation therapy response, refining personalized treatment protocols.
By mapping tumor heterogeneity in high detail, this research will improve our understanding of the complex interactions within the tumor microenvironment that affect radiation response. The findings will inform the development of more accurate predictive models for radiation therapy, enabling the optimization of treatment strategies on a patient-by-patient basis. This approach aims to improve treatment outcomes, reduce side effects, and pave the way for personalized radiation therapy in prostate cancer patients.
Aim 1: Characterization of Tumor Heterogeneity Using Digitized Pathology Slides
This aim will focus on leveraging digitized whole-slide images (WSI) of prostate cancer tissues to characterize intra-tumor heterogeneity (ITH). We will identify distinct tumor cell populations, including tumor-stroma interactions and immune cell infiltration, using machine learning tools for image segmentation and classification. Additionally, we will map the spatial distribution of these populations within the tumor microenvironment, identifying areas of high and low heterogeneity. This will include the recognition of histologic features such as necrosis, fibrosis, and architectural changes that may influence radiosensitivity.
Aim 2: Correlation of Histopathological Features with Radiation Response
In this aim, we will correlate the histopathological features identified in Aim 1 with radiation response data, particularly the Radiosensitivity Index (RSI). Using available molecular data, we will assess how specific tumor regions with different histological patterns respond to radiation. This will help identify regions of the tumor that may require tailored radiation doses. We will also look for patterns that correlate with known molecular markers (e.g., hypoxia markers, p53) involved in radiation resistance or sensitivity.
Aim 3: Integration of Pathology Data with Radiotherapy Models
We will integrate the digitized pathology data into predictive models of tumor response to radiation therapy. By incorporating histopathological data alongside existing RSI and molecular data, we will develop machine learning models that simulate how different tumor regions will react to radiation therapy. This integrated data will be used to predict the optimal treatment protocols for individual patients, factoring in spatial heterogeneity and radiosensitivity.
Aim 4: Validation of Histopathology-Driven Models with External Cohorts
To ensure the robustness of the developed models, we will validate them with external prostate cancer cohorts. By comparing predictions from the integrated pathology-based models with clinical outcomes (e.g., tumor response, survival), we will assess the accuracy and generalizability of the models. This external validation will help refine the models for broader clinical application.
Aim 5: Integration into the Digital Twin Platform
In the final aim, we will incorporate the validated models of intra-tumor heterogeneity into a digital twin platform. This platform will simulate tumor evolution and radiation response, allowing for the optimization of radiation therapy protocols based on specific tumor characteristics. The digital twin will be tested using virtual radiation protocols to determine the most effective treatment strategies for individual patients.
Subu Gupta (AI/ML computer scientist) , Chief Technology Officer , Quantum Biosciences