A Drug Discovery Pipeline for Fighting Breast Cancer Metastasis
Abstract
Currently, there is no cure for metastasis. Treatments may temporarily control disease progression, but metastasis often leads to death in breast cancer patients as these treatments fail over time. While a large tumor size is sometimes indicative of advanced breast cancer, in reality, metastatic spread involves cellular processes that are fundamentally distinct from tumor growth. For example, cancer cells that are more invasive or survive longer when traveling in the bloodstream are often more metastatic. Thus, limiting the growth of the tumor may postpone but will not abrogate metastatic relapse. This distinction is important and should be considered when searching for effective drugs for reducing mortality among the breast cancer patient population. However, candidate drugs are often evaluated based on their capacity to reduce tumor size and not their impact on metastatic progression. Our proposal aims to develop a new strategy for identifying drug candidates and validating their efficacy to meet the goal of eliminating the mortality associated with metastasis. Current approaches in drug discovery involve high-throughput testing of large compound libraries for their ability to specifically kill cancer cells with limited toxicity for non-cancerous cells. After further optimization, the candidate compounds are tested in mouse models using established cancer cell lines, or, more recently, patient-derived xenograft models, to quantify their impact on tumor growth. The drugs that pass these extensive preclinical testing enter clinical trials. In this commonly used pipeline, compounds are specifically selected for their ability to reduce tumor size, often without consideration for metastatic progression. A major challenge in developing a pipeline for anti-metastasis drugs is the absence of reliable cell culture models. While high-throughput microscopy, using fluorescence to study protein function, can be used to measure changes in specific attributes, such as cancer cell invasiveness, in response to treatments, metastasis is simply too complex to be modeled in culture with our current technologies. In vivo models of breast cancer metastasis using established human lines in immunocompromised mice have proven to be effective research tools in the lab and their clinical relevance has been long established. However, these mouse models are not suitable for high-throughput screening of large libraries of compounds, as every mouse can be treated with only a single compound. As such, only a handful of drugs can be tested for their anti-metastasis effects through treatment of tumor-bearing mice. To overcome this obstacle, we have developed a computational approach that prioritizes candidate drugs based on their ability to counteract the gene regulatory programs that are hallmarks of breast cancer progression. Since cancer cells hijack regulatory programs to modify the relevant cellular processes that enhance their metastatic capacity, reverting these programs will diminish breast cancer progression. In order to test the most promising compounds, we will pre-treat, in culture, breast cancer cells that carry unique DNA barcodes in their genome so that the barcodes can be used to map the cells back to the treatment they received. All cells will then be pooled and injected in mice to form metastases. The frequency of each barcode in metastatic tumors reflects the efficacy of its associated treatment. In other words, if a drug treatment is effective, the cells receiving it will not form metastases and their barcode will be absent in the resulting tumors. Given that hundreds of barcodes can be combined and tested in a single mouse, this strategy enables us to deploy large-scale compound screening with a limited number of mice. The most effective compounds in this assay will then be tested individually by treating tumor-bearing mice. In a pilot study, we have shown that this integrated computational and experimenta
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010541
Entities
People
- Hani Goodarzi
Organizations
- United States Army
- University of California, San Francisco