Franchini, Melania (2024) Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in Triple Negative Breast Cancer. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in Triple Negative Breast Cancer |
| Autori: | Autore Email Franchini, Melania melania.franchini@unina.it |
| Data: | 4 Marzo 2024 |
| Numero di pagine: | 244 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Computational and quantitative biology |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Ceccarelli, Michele michele.ceccarelli@unina.it |
| Tutor: | nome email di Bernardo, Diego [non definito] Gambardella, Gennaro [non definito] |
| Data: | 4 Marzo 2024 |
| Numero di pagine: | 244 |
| Parole chiave: | lineage tracing, scRNA-seq, TNBC |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/34 - Bioingegneria industriale |
| Depositato il: | 19 Giu 2024 12:18 |
| Ultima modifica: | 12 Ago 2026 05:36 |
| URI: | https://www.fedoa.unina.it/id/eprint/15564 |
Abstract
Targeting the epidermal growth factor receptor (EGFR) in Triple Negative Breast Cancer (TNBC) resulted in varying and unpredictable clinical responses. To understand the reasons behind this variability, we utilized cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells when exposed to Afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Through integrated lineage tracing analysis, we identified a rare pre-existing subpopulation of cells exhibiting a distinctive biological signature, characterized by enhanced expression levels of IGFBP2 (Insulin-Like Growth Factor Binding Protein 2). Our findings demonstrate that overexpression of IGFBP2 is sufficient to confer tolerance to Afatinib treatment in TNBC cells by activating the compensatory IGF1-R signaling pathway. Lastly, we employed deep learning techniques to create an algorithm that predicts the sensitivity of TNBC cells to Afatinib based on the reconstructed mechanisms of resistance. In conclusion, our approach has proven successful in unraveling the intricate signaling network responsible for resistance to EGFR-targeted therapy, providing novel insights for tailoring individualized treatment strategies in TNBC.
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