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Published on: December 26, 2016
High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in
Yushu Ma1,2, Chien-Hung Shih1, Jinxiong Cheng1,3
1UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, Pennsylvania 15232, United States.
Abstract:
Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirical testing of all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico, integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated five compounds as potent PGCC inhibitors using cell lines and patient-derived models. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.
Insights
Researchers developed new methods to find drugs that kill therapy-resistant polyploid giant cancer cells (PGCCs) in breast cancer. Combining high-throughput screening with machine learning identified promising compounds to overcome treatment resistance.
Area of Science:
- Oncology
- Cancer Biology
- Drug Discovery
Background:
- Polyploid giant cancer cells (PGCCs) contribute to breast cancer therapy resistance through whole genome doubling and dormancy.
- Limited high-throughput methods exist to assess PGCC viability and identify targeted therapies.
- Developing strategies to eliminate PGCCs is crucial for overcoming treatment relapse.
Purpose of the Study:
- To develop and apply novel high-throughput methods for identifying compounds that selectively target PGCCs.
- To screen FDA-approved drugs and utilize machine learning for predicting anti-PGCC efficacy.
- To discover and validate new therapeutic strategies against therapy-resistant breast cancer.
Main Methods:
- Developed a high-throughput single-cell morphological analysis workflow to screen compounds.
- Screened a library of 2726 FDA Phase 1-approved drugs for anti-PGCC activity.
- Trained a machine learning model using chemical fingerprints to predict anti-PGCC efficacy.
- Validated top predicted compounds in cell lines and patient-derived models.
Main Results:
- Identified proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones as promising anti-PGCC candidates.
- Pyronaridine treatment revealed AXL inhibition as a potential PGCC targeting strategy.
- Machine learning model accurately predicted anti-PGCC efficacy, identifying novel compounds.
- Experimentally validated five novel compounds as potent PGCC inhibitors.
Conclusions:
- High-throughput screening and machine learning integration accelerate the discovery of therapies targeting PGCCs.
- Novel compounds identified show potential for overcoming breast cancer therapy resistance.
- Targeting PGCCs represents a promising strategy to prevent disease relapse in breast cancer.
