Augmentation of Apoptosis in High-Grade Serous Epithelial Ovarian Cancer (HGSC) by Inhibition of Antiapoptosis Proteins and DNA Repair
Abstract
High-grade serous ovarian cancer (HGSC) is a devastating disease and the most common form of ovarian cancer. While most patients respond to platinum-based chemotherapy, the response is rarely durable and recurrence almost inevitable. A characteristic of HGSC is defective DNA repair. A class of drugs called PARP inhibitors (PARPi) exploit this vulnerability and has proven useful in delaying recurrence. However, eventually tumors respond by making one or more proteins that prevent cancer cells from dying in response to PARPi and other drugs. In models of HGSC, a protein that frequently confers resistance is called Bcl-xL, a member of the Bcl-2 family of proteins that prevent programmed cell death. This result suggests that, if patients were treated with PARPi to make the cells dependent on Bcl-xL and then an inhibitor of Bcl-xL was added to their treatment, the cancer cells would die. Therefore, we have initiated a clinical trial in which patients that have had a recurrence after receiving platinum-based therapy will first be treated with the PARPi Veliparib, and then an inhibitor of BclxL, Navitoclax, will be added to their course of treatment. The idea is that for some women this will provide the one-two-punch needed to eliminate the cancer or at least dramatically prolong response. The problem is that Bcl-xL is only one of the five known inhibitors of cell death and, at present, we do not have a way of identifying which women will most benefit from adding Navitoclax to their treatment. To improve this one-two-punch therapeutic approach, we need a biomarker that can be used to determine which women will benefit from the Veliparib/Navitoclax combination. An ideal biomarker would also let us determine for other women what combination of PARPi and inhibitor of Bcl-2 proteins would be best for them. As a pragmatic way to identify an appropriate biomarker, we will use a needle biopsy to obtain a small sample of the HGSC tumor from each patient in the trial. We will then use a new technique called conditional reprogramming to enable the cancer cells to grow as tiny tumors in the laboratory so that we can examine them with an automated microscope. By growing thousands of these microtumors called organoids, we can then test hundreds of drug combinations for each patient sample. We will treat the organoids with the different PARPi that are used in the clinic and then add the inhibitors of Bcl-2 family proteins to some of the organoids. As controls some of the organoids will left untreated and allowed to grow. After several days, we will stain the organoids with dyes that we have made that report on how the cells in the tumors respond to the drugs. To understand the response fully for each patient, we acquire thousands of pictures of the cells in the organoids using our automated microscope. We will repeat the process with other drugs that are approved for use in patients (FDA-approved), as there are published data suggesting some of them might also kill the cancer cells when combined with the inhibitors of Bcl-2 family proteins. Using this strategy, we will develop a new method to determine what drug combinations are likely to work best for individual patients. This drug testing process generates hundreds of thousands of micrographs/pictures of cells that we need to analyze. To do this, we use artificial intelligence (AI) and machine-learning programs that we have written to analyze the micrographs to determine which pair of drugs is likely to be the best for any individual patient. Because our automated microscope has high magnification and high resolution and because we have written specialized machine learning software for interpreting images of single cells, we obtain a readout that is more specific than for the whole tumor. Instead, we get information on how each of the different cell types in the tumor samples responds to the different drug combinations. This enables us to deal with the hetero
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110403
Entities
People
- David William Andrews
Organizations
- Sunnybrook Research Institute
- United States Army