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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, PA 15232, USA.
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 2,726 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, empirically testing 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 two compounds as potent PGCC inhibitors. 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 a novel screening method to identify drugs targeting therapy-resistant polyploid giant cancer cells (PGCCs) in breast cancer. Machine learning further predicted and validated new PGCC inhibitors, offering hope for improved cancer treatments.
Area of Science:
- Cancer Biology
- Drug Discovery
- Computational Biology
Background:
- Therapy resistance in breast cancer is linked to polyploid giant cancer cells (PGCCs), which survive standard treatments and cause relapse.
- Limited high-throughput methods exist to assess PGCC viability, hindering the development of targeted therapies.
- PGCCs are characterized by whole-genome doubling, enlarged nuclei, and dormant states under therapeutic stress.
Purpose of the Study:
- To develop and implement a high-throughput single-cell morphological analysis workflow to identify compounds targeting PGCCs.
- To screen FDA-approved drugs and discover novel anti-PGCC agents.
- To build and validate a machine learning model for *in silico* prediction of anti-PGCC efficacy.
Main Methods:
- Developed a high-throughput single-cell morphological analysis workflow to differentiate compound effects on PGCCs vs. non-PGCCs.
- Screened 2,726 FDA Phase 1-approved drugs, followed by RNA-Seq analysis of promising candidates.
- Trained a machine learning model using chemical fingerprints and compound descriptions to predict anti-PGCC efficacy.
Main Results:
- Identified proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones as promising anti-PGCC candidates.
- Pyronaridine (a macrocyclic lactone) showed potential via AXL inhibition.
- Machine learning model accurately predicted efficacy, leading to experimental validation of two novel PGCC inhibitors.
Conclusions:
- A synergistic approach combining high-throughput empirical screening and machine learning accelerates the discovery of therapies targeting PGCCs.
- Novel PGCC inhibitors were identified and validated, offering potential new treatments for therapy-resistant breast cancer.
- This strategy can be applied to discover drugs targeting other resistant cancer cell populations.
