Biology subjects

Bruna, A.

Publications and source records attributed to Bruna, A..

7 recordsLinked to original sources

bioRxiv2025

KDM5-driven transcriptional noise fuels plasticity-led awakening and relapse in paediatric cancer

How drug-tolerant persister (DTP) cells escape quiescence to drive tumour relapse is a central unresolved question in cancer evolution. Here, we identify transcriptional noise (TN), defined as the stochastic variability in gene expression, as a latent property of paediatric cancer cells that becomes a driver of adaptive regrowth after treatment withdrawal. Using functional assays, lineage tracing, single-cell transcriptomics, and multiscale landscape modelling, we show that therapy enriches mesenchymal-like tolerant states in neuroblastoma without clonal selection, while post-treatment awakening is a stochastic process fuelled by noise-enabled plasticity in cell-identity programmes. The histone demethylase KDM5A relocates to noisy cell-state genes during awakening, promoting H3K4me3 removal and chromatin remodelling at these loci. KDM5 inhibition abrogates this process, and suppresses transcriptional noise, halts DTP exit, and prevents tumour recovery in both neuroblastoma and hepatoblastoma models. These results establish DTP as an exploitable evolutionary bottleneck, positioning KDM5-mediated transcriptional noise as an actionable therapeutic target to limit cancer adaptation and relapse GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/682004v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@edba70org.highwire.dtl.DTLVardef@16b1bd9org.highwire.dtl.DTLVardef@f153acorg.highwire.dtl.DTLVardef@1ac5a4e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

The Role of Phenotypic Plasticity in Adaptation to Treatment and Prospective Plasticity Drivers in Hepatoblastoma

Hepatoblastoma (HB) is a paediatric liver cancer, associated with one of the lowest mutational burdens compared to other cancers. Despite this, HBs exhibit diverse phenotypes. It has been proposed that hijacking of early developmental plasticity is especially relevant to chemotherapy response in childhood cancers, however the underlying mechanisms of plasticity in HB remain poorly understood. Plasticity in HB requires investigation after treatment at a more granular temporal scale to further elucidate its role in treatment adaptation. In this work, we aim to study the role of plasticity in treatment adaptation and its underlying mechanisms by integrating heritable and expressed static DNA barcode technology with single-cell sequencing. The synergy between these approaches allows for simultaneous lineage tracing of single clones and phenotyping of single cells. We identify the phenotypic states that exist in HB preclinical models and explore the phenotypic transitions that occur following treatment, which could be important for cells to survive, persist and eventually recover. Expression and accessibility landscapes reveal prospective targets that could be markers or drivers of the plasticity which enables treatment adaptation in HB. By homing in on the clonal, transcriptomic and epigenetic dynamics post treatment, these results examine in greater detail the role of plasticity in treatment adaptation in HB.

cancer biology↗

bioRxiv2024

Chemotherapy-driven de novo Wnt pathway activation dictates a dynamic shift to a drug-tolerant state in breast cancer cells

The efficacy of chemotherapy is often hindered by the enrichment of a population of cancer cells that enter a drug-tolerant persister (DTP) state, mimicking embryonic diapause, yet the underlying mechanisms of this transition remain poorly understood. This study demonstrates that both parental and chemotherapy-induced Wnt-active (WntHigh) cells in Triple-negative breast cancer exhibit transcriptional and functional properties characteristic of DTP cells, including a diapause transcriptional signature, reduced MYC expression, reversible restricted proliferation, and pronounced chemoresistance. Our findings reveal that the de novo activation of the Wnt signaling pathway, triggered by the transcriptional upregulation of components essential for canonical Wnt ligand-secretion and -activation, is critical for enriching the diapause-DTP (DTPDiap) population across various chemotherapy regimens. The diapause-DTP/WntHigh population can be selectively ablated by concomitant, rather than sequential, pharmacological inhibition of Wnt ligand-secretion alongside chemotherapy, highlighting new vulnerabilities in DTPDiap cell-emergence and potentially yielding a therapeutic opportunity against DTPs. This study shows that activation of Wnt signaling pathway is sufficient and necessary for the induction of a DTPDiap state and enhances our understanding of the introductory mechanisms driving DTP cell-enrichment upon chemotherapy.

cancer biology↗

bioRxiv2023

Dynamic Plasticity Systems Direct Early Adaptation to Treatment in Neuroblastoma

Neuroblastoma, like many aggressive cancers, exhibits phenotypic heterogeneity, contributing to therapy resistance and disease progression. However, direct evidence of how phenotypic plasticity influences tumour evolution remains limited. Using a multi-resolution quantitative approach, we define the principles, types, and dynamics of plasticity in neuroblastoma at unprecedented resolution. We demonstrate that intrinsic plasticity is a model-dependent process that enables the coexistence of drug-sensitive and drug-resistant states in environmentally stable conditions, positioning plasticity as a bet-hedging strategy that allows tumours to anticipate environmental changes. Additionally, we show plasticity varies across lineages and single-cell-derived clones, establishing that it is not merely an induced response but a heritable and selectable trait. By simultaneously mapping plasticity at and clonal dynamics in evolving neuroblastoma populations, we show plasticity is shaped by selective pressures, reinforcing its role as a fundamental driver of neuroblastoma evolution in a treatment- and genetic background-dependent manner. We define three distinct modes of plasticityled adaptation. In the selection of the plasticity model, strong selective pressure temporarily constrains phenotypic transitions, but they reemerge with greater dynamics once the stressor is removed, favouring the selection of highly plastic clones. In the adaptive plasticity model, phenotypic transitions actively reshape tumour heterogeneity, allowing for rapid adaptation to treatment while minimising the impact of clonal selection. Finally, in the plasticity equilibrium model, phenotypic transitions persist at baseline, maintaining a state of phenotypic fluidity, with clonal selection ultimately dictating tumour evolution. These findings highlight the diverse, context-dependent strategies that neuroblastoma populations employ to navigate selective pressures and therapy resistance, emphasizing the need for plasticity-targeting therapeutic approaches to disrupt tumour adaptation and improve treatment outcomes. TeaserNatural selection and phenotypic transitions shape adaptive evolution, guiding neuroblastoma survival under treatment pressure.

cancer biology↗

bioRxiv2023

Modelling drug responses and evolutionary dynamics using triple negative breast cancer patient-derived xenografts

Triple negative breast cancers (TNBC) exhibit inter- and intra-tumour heterogeneity, which is reflected in diverse drug responses and interplays with tumour evolution. Here, we use TNBC patient-derived tumour xenografts (PDTX) as a platform for co-clinical trials to test their predictive value and explore the molecular features of drug response and resistance. Patients and their matched PDTX exhibited mirrored drug responses to neoadjuvant therapy in a clinical trial. In parallel, additional clinically-relevant treatments were tested in PDTXs in vivo to identify alternative effective therapies for each PDTX model. This framework establishes the foundation for anticipatory personalised therapies for those patients with resistant or relapsed tumours. The PDTXs were further explored to model PDTX- and treatment-specific behaviours. The dynamics of drug response were characterised at single-cell resolution revealing a novel mechanism of response to olaparib. Upon olaparib treatment PDTXs showed phenotypic plasticity, including transient activation of the immediate-early response and irreversible sequential phenotypic switches: from epithelial to epithelial-mesenchymal-hybrid states, and then to mesenchymal states. This molecular mechanism was exploited ex vivo by combining olaparib and salinomycin (an inhibitor of mesenchymal-transduced cells) to reveal synergistic effects. In summary, TNBC PDTXs have the potential to help design individualised treatment strategies derived from model-specific evolutionary insights.

cancer biology↗

bioRxiv2021

RANK is an independent biomarker of poor prognosis in estrogen receptor-negative breast cancer and a therapeutic target in patient-derived xenografts

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor denosumab in BC patients, beyond its bone-related effects, is unclear. Here, we investigated the prognostic value of RANK expression and its functionality in human BC. We analyzed RANK and RANKL expression in more than 1500 BC cases (777 being estrogen receptor-negative (ER-)) from four independent cohorts. We confirmed that RANK is more frequently expressed in ER- tumors, but it is also found in a subset of ER+ tumors. In ER- BC, RANK expression was independently associated with poor outcome, especially in postmenopausal patients and those who received adjuvant chemotherapy. Gene expression analyses unraveled distinct biology associated with RANK in relation to ER expression and menopause, and evidenced enhanced RANK activation in ER- postmenopausal tumors, together with regulation of metabolic pathways. Functional studies and transcriptomic analyses in ER- RANK+ patients-derived orthoxenografts demonstrated that activation of RANK signaling pathway promotes tumor cell proliferation and stemness, and regulates multiple biological processes including tumor immune surveillance and metabolism. Our results demonstrate that RANK expression is an independent poor prognosis biomarker in postmenopausal ER- BC patients and support the rational of using RANK pathway inhibitors in combination with chemotherapy in ER- BC.

cancer biology↗

bioRxiv2018

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

Inter-tumour heterogeneity is one of cancers most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

bioinformatics↗