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Predicting tumour resistance to paclitaxel and carboplatin utilising genome-wide screening in haploid human embryonic stem cells

Taxanes and platinum molecules, specifically paclitaxel and carboplatin, are widely used anticancer drugs that induce cell death and serve as first-line chemotherapy for various cancer types. Despite the efficient effect of both drugs on cancer cell proliferation, many tumours have innate resistance...

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Bibliographic Details
Published in:Cell proliferation 2024-11, p.e13771
Main Authors: Nissenbaum, Jonathan, Segal, Emanuel, Philip, Hagit, Cashman, Rivki, Golan-Lev, Tamar, Reubinoff, Benjamin E, Turjeman, Adi, Yanuka, Ofra, Lezmi, Elyad, Kopper, Oded, Benvenisty, Nissim
Format: Article
Language:English
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Summary:Taxanes and platinum molecules, specifically paclitaxel and carboplatin, are widely used anticancer drugs that induce cell death and serve as first-line chemotherapy for various cancer types. Despite the efficient effect of both drugs on cancer cell proliferation, many tumours have innate resistance against paclitaxel and carboplatin, which leads to inefficient treatment and poor survival rates. Haploid human embryonic stem cells (hESCs) are a novel and robust platform for genetic screening. To gain a comprehensive view of genes that affect or regulate paclitaxel and carboplatin resistance, genome-wide loss-of-function screens in haploid hESCs were performed. Both paclitaxel and carboplatin screens have yielded selected plausible gene lists and pathways relevant to resistance prediction. The effects of mutations in selected genes on the resistance to the drugs were demonstrated. Based on the results, an algorithm that can predict resistance to paclitaxel or carboplatin was developed. Applying the algorithm to the DNA mutation profile of patients' tumours enabled the separation of sensitive versus resistant patients, thus, providing a prediction tool. As the anticancer drugs arsenal can offer alternatives in case of resistance to either paclitaxel or carboplatin, an early prediction can provide a significant advantage and should improve treatment. The algorithm assists this unmet need and helps predict whether a patient will respond to the treatment and may have an immediate clinically actionable application.
ISSN:0960-7722
1365-2184
1365-2184
DOI:10.1111/cpr.13771