Villemin, J.-P. | bioRxiv Science

bioRxiv2022

Farnesyltransferase inhibition overcomes the adaptive resistance to osimertinib in EGFR-mutant NSCLC

Drug-tolerant "dormant" cells (DTC) have emerged as one of the major non-genetic mechanisms driving resistance to targeted therapy in lung cancer, although the sequence of events leading to entry and exit from dormancy remain poorly described. Here, we performed real-time monitoring of the cell cycle dynamics during the adaptive response to Epidermal Growth Factor Receptor tyrosine kinase inhibitors (EGFR-TKi) in a panel of EGFR-mutated lung cancer cell lines. We identified a rare population of S/G2 cycling cells (referred to as early escapers) that emerged in the first hours of treatment amongst stably arrested and progressively dying G1 cells. We determined that early escapers evolved from a non-proliferative differentiated alveolar type 1 (AT1) phenotype which was invariably associated with cytoskeletal remodeling through Rho/ROCK pathway activation. Using a panel of Rho-pathway inhibitors, we found that the farnesyltransferase inhibitor tipifarnib induced a complete clearance of EGFR-TKi-induced DTC thus fully preventing relapse in vitro. Using a xenograft model and a PDX model of EGFRL858R/T790M lung cancer, co-treatment with tipifarnib prevented relapse to osimertinib for up to 6 months with no evidence of toxicity. Among the farnesylated proteins regulated during osimertinib treatment, concomitant inhibition of RHOE and LaminB1 was sufficient to recapitulated FTIs effect. Osimertinib and tipifarnib co-treatment completely suppressed the emergence of AT1 phenotype, prevented mitosis of S/G2-treated cells and increased the apoptotic response through activation of ATF4-CHOP-dependent Integrated Stress Response (ISR) pathway. Our data strongly support the use of tipifarnib in combination with osimertinib in patients to effectively and durably prevent relapse.

cancer biology↗

bioRxiv2020

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

BackgroundBreast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. ResultsTranscriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. ConclusionsUsing a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

cancer biology↗