Biology subjects

Clappier, E.

Publications and source records attributed to Clappier, E..

3 recordsLinked to original sources

bioRxiv2026

In vivo isogenic modelling unveils a TP53-mediated relapse phenotype in T-cell acute lymphoblastic leukemia

Many patients with T-cell acute lymphoblastic leukemia (T-ALL) relapse into a treatment-resistant disease. The mechanisms driving relapse remain largely elusive, in part due to the lack of faithful experimental models. Here, we leveraged patient-derived xenograft (PDX) pairs generated from diagnosis and relapse T-ALLs to functionally address the cellular mechanisms driving TP53-altered relapse. Beyond inter-T-ALL variability, comparative analyses revealed a unique, cell-intrinsic relapse phenotype that includes greater leukemia-initiating capacity and that can be conferred to diagnosis cells by TP53 silencing. Transcriptomic profiling linked the relapse phenotype to deregulated OXPHOS metabolism and MYC signaling. Integration of single-cell profilings uncovered TP53-wildtype cell populations at diagnosis expressing a relapse profile, possibly reflecting a pre-existing modulation of TP53 signaling. These cells sequentially evolved towards biallelic TP53 inactivation at relapse. Collectively, our findings support a model in which T-ALL relapses emerge from a selected pre-existing transcriptional state characterized by deregulated metabolism that favors subsequent TP53 inactivation.

cancer biology↗

bioRxiv2025

Targeting glucocorticoid-induced CD20 activation in preclinical models of B-ALL

Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is effectively controlled with contemporary multi-agent chemotherapy, resulting to 5-year survival rates above 90%. However, relapse occurs in 15-20% of patients due to minimal residual disease (MRD), characterized by the presence of persisting and resistant leukemic cells, and associated with a poor clinical outcome. Despite its prognostic relevance, the molecular features driving MRD are poorly characterized. In this study, we developed patient-derived xenograft (PDX) models from matched diagnosis and relapse B-ALL samples combined to chemotherapy to mimic MRD in vivo. Drug-tolerant leukemic cells were profiled using single-cell RNA sequencing and we identified a transcriptionally distinct MRD-like population enriched for cell-quiescence, inflammatory stress, and B-cell receptor pathway signatures. Strikingly, the B-lymphocyte surface antigen CD20, encoding by MS4A1 gene, emerged as a consistent upregulated marker in MRD cells from PDXs and patients with diverse oncogenic subtypes. We further demonstrated that CD20 expression is induced by glucocorticoid exposure, creating a therapeutic opportunity where anti-CD20 monoclonal antibodies selectively eradicated MRD cells in vivo. Our data highlight CD20 not only as a biomarker but as an actionable vulnerability in B-ALL MRD, supporting clinical evaluation of anti-CD20 immunotherapy during induction treatment to kill drug-resistant cells and reduce relapse risk.

cancer biology↗

bioRxiv2019

A comprehensive dataset of TLX1 positive ALL-SIL lymphoblasts and primary T-cell acute lymphoblastic leukemias

Most currently available transcriptome data of T-cell acute lymphoblastic leukemia (T-ALL) are based on polyA[+] RNA sequencing methods thus lacking non-polyadenylated transcripts. Here, we present the data of polyA[+] and total RNA sequencing in the context of in vitro TLX1 knockdown in ALL-SIL cells and a primary T-ALL cohort. We extended this dataset with ATAC sequencing and H3K4me1 and H3K4me3 ChIP sequencing data to map putative gene regulatory regions. In this data descriptor, we present a detailed report of how the data were generated and which bioinformatics analyses were performed. Through several technical validations, we showed that our sequencing data are of high quality and that our in vitro TLX1 knockdown was successful. We also validated the quality of the ATAC and ChIP sequencing data and showed that ATAC and H3K4me3 ChIP peaks are enriched at transcription start sites. We believe that this comprehensive set of sequencing data can be reused by others to further unravel the complex biology of T-ALL in general and TLX1 in particular.

molecular biology↗