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Biology subjects

Pellecchia, S.

Publications and source records attributed to Pellecchia, S..

2 recordsLinked to original sources

Predicting drug response from single-cell expression profiles of tumours

Drug response prediction at the single cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens and functional enrichment analysis to predict single cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. DREEP provides an in-silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerate drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.

cancer biology↗

Single cell lineage tracing reveals subclonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.

Epidermal growth factor receptor (EGFR)-targeted therapies have demonstrated variable and unpredictable clinical responses in triple negative breast cancer (TNBC). To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of IGFBP2 (Insulin-Like Growth Factor Binding Protein 2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory IGF1-R signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

cancer biology↗