Biology subjects

Pelicci, P. G.

Publications and source records attributed to Pelicci, P. G..

17 recordsLinked to original sources

bioRxiv2026

Cell cycle control of chromatin creates a therapeutic window for epigenetic therapy in tumors

The relationship between cell cycle length and differentiation competence is well established in developmental biology, particularly in embryonic stem cells, where a short G1 phase maintains pluripotency and G1 lengthening permits lineage commitment. Whether this principle operates in cancer cells, where the cell cycle is deregulated and G1 is frequently shortened, and whether it can be pharmacologically exploited for therapy, has not been tested. Here we show that the duration of G1 is a causal determinant of chromatin state in cancer cells and that extending G1 creates a therapeutic window for epigenetic drugs. Using acute myeloid leukemia (AML) as a model, we demonstrate that low-dose palbociclib, at concentrations well below those required for cytostatic arrest, extends G1 without halting proliferation. This modest prolongation reshapes the histone modification landscape: repressive marks (H3K9me2/3, H4K20me2/3) increase while acetylation decreases, and chromatin accessibility rises broadly in the euchromatic compartment. Naturally slow-cycling AML lines share this epigenetic signature regardless of their oncogenic driver mutations, and pharmacologically extending G1 in fast-cycling cells recapitulates it, establishing G1 length as a causal regulator of the cancer epigenome rather than a passive correlate. To identify epigenetic vulnerabilities created by G1 extension, we performed complementary drug and CRISPR-Cas9 screens in G1-extended AML cells. Both approaches converged on LSD1 (KDM1A): slow-cycling AML cells are intrinsically sensitive to LSD1 inhibition, while fast-cycling cells become sensitive when G1 is prolonged. The combination of low-dose palbociclib and LSD1 inhibition triggers differentiation and significantly prolongs survival in AML xenograft models. p21 (CDKN1A) emerges as the central molecular determinant of this response. In slow-cycling AML cells, p21 is highly expressed and its knockdown abolishes LSD1 inhibitor sensitivity. Structure-function analysis using p21 mutants separates the two known activities of p21: the CDK-inhibitory function (which extends G1) is required for sensitization, whereas the PCNA-binding function is dispensable. Three pharmacological routes converge on the same endpoint, CDK inhibition, G1 extension, and a differentiation-competent chromatin state: direct CDK4/6 inhibition by palbociclib, p21 overexpression, and p21 induction through HDAC or EZH1/2 inhibitors. Palbociclib bypasses the requirement for p21 entirely, confirming that G1 length itself, not p21 as a protein, is the critical variable. Mechanistically, the combination of G1 extension and LSD1 inhibition produces a qualitatively distinct chromatin state rather than an additive one. ATAC-seq reveals thousands of combination-exclusive accessible regions, enriched for footprints of myeloid differentiation transcription factors including SPI1/PU.1, IRF1, and STAT1/2. A double-lock principle governs this remodeling: palbociclib drives the removal of repressive marks (H3K9me3 and H3K27me3), while LSD1 inhibition installs active marks at the newly accessible regions. The ncBAF chromatin remodeling complex, identified in our CRISPR screen and validated by knockout of its essential subunits BRD9 and SMARCD1, is specifically required for this response. Loss of ncBAF abolishes the combination-induced chromatin remodeling and differentiation program but does not affect the initial G1 extension or retinoic acid-induced differentiation, indicating that ncBAF specifically couples cell-cycle modulation to chromatin remodeling rather than acting as a general differentiation factor. The principle generalizes beyond AML. In melanoma, breast cancer, and small-cell lung cancer (SCLC), sensitivity to LSD1 inhibition tracks with p21 expression and cycling speed. Primary melanoma samples stratified by p21 recapitulate the same pattern: p21-high, slow-cycling cells are sensitive; p21-low, fast-cycling cells are resistant but can be sensitized by palbociclib cotreatment. Cisplatin-induced drug-tolerant persister (DTP) cells, which emerge as a slow-cycling, chemo-resistant population and upregulate both p21 and LSD1, become vulnerable to LSD1 inhibition and are eradicated by the combination. In melanoma patient-derived xenograft (PDX) models, p21-high tumors respond to LSD1 inhibitor monotherapy, while p21-low tumors are sensitized by palbociclib cotreatment, with p21 knockdown abolishing the response. Together, these findings establish cell-cycle duration as a tunable regulator of the cancer epigenome and demonstrate that pharmacological G1 extension converts cytostatic CDK4/6 inhibition into an epigenetic sensitization strategy. Both fast-proliferating and slow-cycling tumor compartments (including drug-resistant persisters) can be targeted by matching the epigenomic state to the appropriate combination of cell-cycle modulators and epigenetic drugs. p21 emerges as a candidate biomarker for patient stratification. More broadly, our work repositions the cell cycle from a passive conduit for proliferation signals to an active, druggable regulator of chromatin fate, with implications that extend from cancer therapy to stem cell biology and regenerative medicine.

cancer biology↗

bioRxiv2025

MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics

Mitochondrial single-cell lineage tracing (MT-scLT) has recently emerged as a scalable and non-invasive tool to trace somatic cell lineages. However, the reliability and resolution of MT-scLT remains highly debated. Here, we present MiTo, the first end-to-end framework for robust MT-scLT data analysis. Thanks to highly-optimized algorithms and user-friendly interfaces, this modular toolkit offers unprecedented control across the entire MT-scLT workflow. Benchmarked against novel real-world datasets (375-2,757 cells; 8-216 lentiviral clones), MiTo outperformed state-of-the-art methods and baselines in MT-scLT data pre-processing and clonal inference. Applied to a time-resolved dataset of breast cancer evolution (>2,500 cells), MiTo accurately inferred ground-truth cell lineages (ARI=0.94) and cell state transitions, detected clonal fitness markers, and quantified heritability of gene regulatory networks. Comparing alternative lineage markers, MiTo quantified the resolution limit of existing MT-scLT systems, which currently enable reliable inference of coarse-grained cellular ancestries, but not high-resolution phylogenetic inference. In conclusion, this work provides robust tools and practical guidelines to dissect somatic evolution with single-cell multi-omics.

bioinformatics↗

bioRxiv2024

PoreMeth2: decoding the evolution of methylome alterations with Nanopore sequencing

In epigenetic analysis, identifying differentially methylated regions (DMRs) typically involves detecting groups of consecutive CpGs that show significant changes in their average methylation levels. However, the methylation state of a genomic region can also be characterized by a mixture of patterns (epialleles) with variable frequencies, and the relative proportions of such patterns can provide insights into its mechanisms of formation. Traditional methods based on bisulfite conversion and NGS, due to the read size (150 bp), allow epiallele frequency analysis only in high-CpG-density regions, limiting differential methylation studies to just 50% of the human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high- and low-CpG-density regions. We introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from Nanopore data to identify DMRs, assess their formation mechanisms, and annotate them to genic and regulatory elements. We applied PoreMeth2 to cancer and glial cell datasets, demonstrating its ability to distinguish epigenomic changes with a strong effect on gene expression from those with a weaker impact on transcriptional activity. PoreMeth2 is publicly available at https://github.com/Lab-CoMBINE/PoreMeth2.

bioinformatics↗

bioRxiv2023

A novel, RAS-independent role for NF1 in microtubular dynamics and damage repair dictates sensitivity to T-DM1 in HER2-positive breast cancer

Antibody-Drug Conjugates (ADC) have revolutionized the treatment of several tumors, and extensive research is being devoted to the identification of predictive biomarkers. These are particularly sought after in fields, like breast cancer, in which multiple ADCs with identical target but different payloads have been approved. NF1 is a tumor suppressor widely mutated across several cancers, best characterized as an inhibitor of RAS signaling. Additional functions have been proposed but not deeply investigated, due to its large size and complex domain structure. Whether somatic NF1 mutations can be used to guide clinical decisions is not known. Here, combining patient data, in vitro/in vivo models and protein biochemistry, we show that NF1 loss sensitizes cancer cells to T-DM1, the first approved ADC in breast cancer, through a novel, RAS-independent function on microtubular dynamics and repair. NF1 exhibits all biochemical properties of a bona fide Microtubule-Associated Protein (MAP) and specifically enhances intratubular repair, a recently discovered phenomenon whose regulation remains poorly characterized. NF1 loss results in mitotic defects and low-grade aneuploidy in cell lines and patients. Increased sensitivity to T-DM1 upon NF1 loss is confirmed in breast cancer patients analysed across institutions in Europe and USA. Our results define NF1 as a key regulatory factor for microtubular repair and the first ADC payload-associated predictive biomarker identified to date.

cancer biology↗

bioRxiv2023

IN VIVO ISOLATION OF A QUIESCENT MELANOMA POPULATION WITH INVASIVE PROPERTIES UNVEILS A TRANSCRIPTIONAL REPROGRAMMING DRIVEN BY THE TUMOR NICHE

Melanoma is a heterogeneous tumor composed of many interacting cellular populations and highly plastic melanoma cells that pass through distinct cell states to adapt to the surrounding microenvironment. Slow cycling is a transient state that defines a minor population of cells with cancer-initiating features. These cells are enriched upon drug therapy and can trigger cancer relapse and metastasis dissemination when they acquire proliferative potential. This population is still not entirely characterized. Here we provide evidence of the existence of a slow cycling melanoma population isolated in vivo from melanoma PDXs using the H2B-GFP system. These cells display a highly invasive phenotype and are able to dynamically respond to cancer microenvironmental stimuli. Single cell transcriptomic analysis unveils a significant transcriptional heterogeneity of GFP-retaining slow cycling cells, defining a quiescent subpopulation of cells. These cells show a different phenotype in primary tumors and matched metastases, suggesting that tumor niche pressure drives a transcriptional reprogramming of quiescent cells during melanoma progression.

cancer biology↗

bioRxiv2023

INDOCYANINE GREEN IMAGING BY VITAL TISSUE SLICES SCANNING ALLOWS FOR THE ISOLATION OF INTACT LIVER MICRO-METASTASIS FOR SINGLE-CELL ANALYSIS

Metastasis represents the deadliest outcome in cancer, leading to the vast majority of cancer-related deaths. Understanding the progression from micro-to macro-metastasis might improve future therapeutic strategies aimed at blocking metastatic disease. However, the difficulty of investigating vital, clinically undetectable, micro-metastases hindered our capacity to unravel phenotypic determinants of micro-metastases. In this work, we leveraged indocyanine green (ICG) dye to detect small sized liver micro-metastases across several cancer models. We exploited a method for infrared fluorescence scanning of fresh tissue and coring of cancer micro-metastases and succeeded in processing them for single-cell RNA sequencing. Our analysis revealed that distinct liver micro-metastases upregulate both shared and specific genes that can successfully predict breast cancer patient prognosis. Moreover, the ontology classification of these genes allowed the validation of several pathways, namely interferon response, extracellular matrix remodeling, and antioxidant response in metastatic progression. Ultimately, we showed that ICG can be successfully used to quantify breast cancer micro- and macro-metastases to lungs, which we showed to be abrogated through inhibition of H2O2-producing enzyme monoamine oxidase. Therefore, the ICG approach allowed us to identify not only determinant of breast cancer metastatization, but also to assess the therapeutic efficacy of targeting these genes which can be further investigated in clinic.

cancer biology↗

bioRxiv2023

GASOLINE: detecting germline and somatic structural variants from long-reads data.

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30x sequencing coverage experiments requires 4-5 hours with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified 6 genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

bioinformatics↗

bioRxiv2022

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias.

Aberrant DNA methylation at CpG dinucleotides is a hallmark of cancer and is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

genomics↗

bioRxiv2022

The breast cancer pro-metastatic phenotype requires concomitant hyper-activation of ECM remodeling and dsRNA-IFN1 signaling in rare clone cells

The molecular determinants of breast cancer (BC) pro-metastatic phenotype are largely unknown. Here, we leveraged lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during BC metastatization. We showed that metastases derive from rare pro-metastatic clones that are under-represented in primary tumors. Both low clonal-fitness and high metastatic-potential are independent of clonal origin. Differential expression and classification analyses revealed that the pro-metastatic phenotype is acquired in rare cells by concomitant hyper-activation of extracellular-matrix remodeling, dsRNA-interferon signaling, and stress-response pathways. Notably, genetic silencing of single pro-metastatic genes from different pathways significantly impairs migration in vitro and metastatization in vivo, with negligible effects on cell proliferation and tumor growth. In addition, gene-expression signatures from identified pro-metastatic genes predicts metastatic progression in BC patients, independently of known prognostic factors. This study elucidates previously unknown mechanisms of BC metastatization, and provides novel prognosis predictors and therapeutic targets for metastasis prevention.

cancer biology↗

bioRxiv2022

Goat PRP14 (gPRP14) has xeno-antigenic properties and works as a vaccine in preclinical models of cancer.

We studied the activity of recombinant goat PRP14 (gPRP14), a member of the RID protein family, as a xeno-antigen in preclinical models of cancer. Antisera from rabbits and mice immunized with gPRP14 showed strong reactivity against several tumor cell types, which was absent towards normal cells: the tumor selectivity was related to surface and intra-cellular expression in tumor cells, and to an exclusively intra-cellular localization in normal cells. In vitro, binding to tumor cells was followed by cytotoxicity which could be rescued by the addition of excess soluble antigen. In vivo, an anti-tumor activity of immunization with gPRP14 was observed in murine syngeneic models of breast cancer and melanoma: the anti-tumor response was present when gPRP14 was administered in a preventive setting, and persisted upon repeated challenges with tumor cells in long-term survivor mice. Finally, we showed that both the humoral and T-cell mediated responses are needed for the optimal anti-tumor effect in the murine melanoma model. Thus, we have performed an initial characterization of gPRP14 as a cancer vaccine, which -given the potential wide range of tumor cells positive for the antigen-appears as a promising, novel immunotherapy.

cancer biology↗

bioRxiv2022

High-fat diet promotes Acute Promyelocytic Leukemia through PPARδ-enhanced self-renewal of preleukemic progenitors

Obesity is associated with a higher risk of developing many cancer types including acute promyelocytic leukaemia (APL), a subset of acute myeloid leukemias (AML) characterized by expression of the PML-RAR oncogene. The molecular mechanisms linking obesity and APL development are not known. To model clinical observations, we established a mouse model of diet-induced obesity using transgenic mice constitutively expressing PML-RARA in the hematopoietic system (PML-RAR KI mice) fed either standard (SD) or high-fat (HFD) diets. HFD-fed PML-RAR KI mice developed leukaemia with reduced latency and increased penetrance, as compared to SD-fed mice. HFD leads to accumulation of DNA damage in hematopoietic stem cells (HSCs), but, surprisingly, this was not associated with mutational load gain, as shown by whole genome/exome sequencing of pre-leukemic and leukemic cells. Importantly, very few of the observed mutations were predicted to act as cancer drivers, suggesting the relevance of nongenetic mechanisms. HFD led to an expansion of hematopoietic progenitor cells with a concomitant reduction in long-term hematopoietic stem cells, and in the presence of PML-RAR this was also accompanied by an enhancement of in vitro and in vivo self-renewal. Interestingly, Linoleic Acid (LA), abundant in HFD, recapitulates the effect of HFD on the self-renewal of PML-RAR HPCs by activating the peroxisome proliferator-activated receptor delta (PPAR{delta}), a central regulator of fatty acid metabolism involved in the promotion of cancer progression. Our findings have implications for dietary or pharmacological interventions aimed at counteracting the cancer-promoting effect of obesity. Key pointsO_LIhigh fat diet (HFD) promotes APL leukemogenesis in mouse models, reproducing the exquisite sensitivity to obesity observed in humans C_LIO_LIalthough HFD leads to DNA damage and mutations, the molecular mechanism is nongenetic and linked to the transcription factor PPAR{delta} C_LI

cancer biology↗

bioRxiv2021

EVALUATION OF STED SUPER-RESOLUTION IMAGE QUALITY BY IMAGE CORRELATION SPECTROSCOPY (QuICS)

Quantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

biophysics↗

bioRxiv2021

Inhibiting LSD1 suppresses coronavirus-induced inflammation but spares innate antiviral activity

Tissue-resident macrophages exert critical but conflicting effects on the progression of coronavirus infections by secreting both anti-viral type I Interferons and tissue-damaging inflammatory cytokines. Steroids, the only class of host-targeting drugs approved for Covid19, indiscriminately suppress both responses, possibly impairing viral clearance, and provide limited clinical benefit. Here we set up a mouse in vitro co-culture system that reproduces the macrophage response to SARS-CoV2 seen in patients and allows quantitation of inflammatory and antiviral activities. We show that the NFKB-dependent inflammatory response can be selectively inhibited by ablating the lysine-demethylase LSD1, which additionally unleashed interferon-independent ISG activation and blocked viral egress through the lysosomal pathway. These results provide a rationale for repurposing LSD1 inhibitors, a class of drugs extensively studied in oncology, for Covid-19 treatment. One-Sentence SummaryTargeting a chromatin-modifying enzyme in coronavirus infections curbs tissue-damage without affecting antiviral response

immunology↗

bioRxiv2019

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence in the analysed samples of a mixture of cells arising from the host and the graft.\n\nWe have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes. We show its application on a single cell dataset composed by human and mouse cells.\n\nAvailability and implementationXenoCell is available for non-commercial use on GitLab: https://gitlab.com/XenoCell/XenoCell

bioinformatics↗

bioRxiv2019

The Endless Frontier? The Recent Upsurge of R&D Productivity in Pharmaceuticals

Analyses of pharmaceutical pipelines of drug development in the 1990-2010 documented progressively increasing attrition rates and duration of clinical trials, leading to a diffuse perception of a \"productivity crisis\". We produced a new set of analyses for the last decade, using an extensive data of more than 45,000 projects between 1990 and 2017, and report a recent upsurge of R&D productivity within the industry. First, we investigated how R&D projects are allocated across therapeutic areas and found a polarization towards high-risk/high-reward indications, with a strong focus on oncology. Importantly, attrition rates have been decreasing at all clinical stages in recent years. In parallel, we observed an increase of early failures in preclinical research, and a significant reduction of time required to identify projects to be discontinued. Notably, more recent projects are increasingly based on novel mechanisms of action and target indications with small patient populations. Finally, by analyses of the relative contribution of different institutional types and development companies, we show that the observed increased performance in clinical trials is mostly due to the contribution of biotech-nological companies, while pharmaceutical companies have significantly improved their performances in identifying false positives in preclinical research.

pharmacology and toxicology↗

bioRxiv2019

Cerebro: Interactive visualization of scRNA-seq data

SummaryDespite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user.\n\nThrough an interactive and intuitive graphical interface, users can i) explore similarities and heterogeneity between samples and cells clusters in 2D or 3D projections such as t-SNE or UMAP, ii) display the expression level of single genes or genes sets of interest, iii) browse tables of most expressed genes and marker genes for each sample and cluster.\n\nWe provide a simple example to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists which facilitates effective interaction to shorten the gap between analysis and interpretation of the data.\n\nAvailabilityCerebro and example data sets are available at https://github.com/romanhaa/Cerebro. Similarly, the R packages cerebroApp and cerebroPrepare R packages are available at https://github.com/romanhaa/cerebroApp and https://github.com/romanhaa/cerebroPrepare, respectively. All components are released under the MIT License.

bioinformatics↗

bioRxiv2018

WDR5 regulates epithelial-to-mesenchymal transition in breast cancer cells via TGFB

Even if the mortality rate in breast cancer (BC) has recently decreased, development of metastases and drug resistance are still challenges to successful systemic treatment. The epithelial-to-mesenchymal transition (EMT), as well as epigenetic dynamic modifications, plays a pivotal role in invasion, metastasis, and drug resistance. Here, we report that WDR5, the core subunit of histone H3 K4 methyltransferase complexes, is crucial in coordinating EMT and regulating epigenetic changes that drive metastasis. We show that silencing of WDR5 in BC up-regulates an epithelial signature in triple negative and luminal B like patients by transcriptional repression of mesenchymal genes and reduction of the metastatic properties of these cells. Moreover, we demonstrate that this regulation is mediated by inhibition of the TGF{beta} signaling both at the transcriptional and post-translational level, suggesting an active role of WDR5 in guiding tumor plasticity upon oncogenic insults, regardless of the pathological BC subtypes.\n\nWe therefore suggest that WDR5 inhibition could be a successful pharmacologic approach to inhibit EMT and sensitize breast cancer cells to chemotherapy.

cancer biology↗