Polyploid Giant Cancer Cells Are Frequently Found in the Urine of Prostate Cancer Patients

Castillo, Luciana Andrea; Anract, J.; Delongchamps, Nicolas Barry; Huillard, Olivier; BenMohamed, Fatima; Decina, Alessandra; Lebrét, Thierry; Dachez, Roger; Paterlini‐Bréchot, Patrizia

doi:10.3390/cancers15133366

Cited by 5 publications

References 45 publications

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High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

Ma,

Shih,

Cheng

et al. 2024

Preprint

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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.

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High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

Ma,

Shih,

Cheng

et al. 2024

Preprint

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GeTMoR: Simultaneous genomic, transcriptomic, and morphological profiling of rare single cells

Prabakar,

Schmidt,

Kuhn

et al. 2024

Preprint

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Circulating tumor cells (CTCs), and circulating tumor related cells, are extremely rare cells that intravasate from the tumor into the circulatory system and can be captured via a liquid biopsy. Although CTCs contribute to the metastatic cascade, and diverse phenotypes of CTCs have been observed - including the cytokeratin expressing CTCs, CTC clusters, large polyploid CTCs, and CTCs undergoing epithelial to mesenchymal transition - little is known about their functionality. By virtue of CTCs being rare, a detection method that maximizes the information obtained per cell would be ideal to understand their biology and for use in diagnostic approaches. The challenge is that rare cell detection necessitates extensive processing steps, during which molecular content, such as RNA and DNA, needs to be preserved for downstream single cell analysis. We developed GEnomic, Transcriptomic, and MOrphological profiling of Rare cells (GeTMoR), a method that extends the High Definition Single Cell Assay for detecting rare cancer related cells to simultaneously image and profile the genome and transcriptome from single rare cells. We validated GeTMoR by spiking in cancer cell lines into whole blood to evaluate the quality of recovered gene expression and copy number profile from the same cell. The GeTMoR approach provides the ability to link the phenotype of rare cells, including CTCs, to their genome and transcriptome, thereby enabling insight into rare cell biology.

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Polyploid Giant Cancer Cells Generated from Human Cytomegalovirus-Infected Prostate Epithelial Cells

Bouezzedine,

El Baba,

Haidar Ahmad

et al. 2023

Cancers

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Background: Prostate cancer is the most commonly diagnosed malignancy and the sixth leading cause of cancer death in men worldwide. Chromosomal instability (CIN) and polyploid giant cancer cells (PGCCs) have been considered predominant hallmarks of cancer. Recent clinical studies have proven the association of CIN, aneuploidy, and PGCCs with poor prognosis of prostate cancer (PCa). Evidence of HCMV transforming potential might indicate that HCMV may be involved in PCa. Methods: Herein, we underline the role of the high-risk HCMV-DB and -BL clinical strains in transforming prostate epithelial cells and assess the molecular and cellular oncogenic processes associated with PCa. Results: Oncogenesis parallels a sustained growth of “CMV-Transformed Prostate epithelial cells” or CTP cells that highly express Myc and EZH2, forming soft agar colonies and displaying stemness as well as mesenchymal features, hence promoting EMT as well as PGCCs and a spheroid appearance. Conclusions: HCMV-induced Myc and EZH2 upregulation coupled with stemness and EMT traits in IE1-expressing CTP might highlight the potential role of HCMV in PCa development and encourage the use of anti-EZH2 and anti-HCMV in PCa treatment.

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Polyploid Giant Cancer Cells Are Frequently Found in the Urine of Prostate Cancer Patients

Cited by 5 publications

References 45 publications

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

GeTMoR: Simultaneous genomic, transcriptomic, and morphological profiling of rare single cells

Polyploid Giant Cancer Cells Generated from Human Cytomegalovirus-Infected Prostate Epithelial Cells

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