Biology subjects

Triche, T. J.

Publications and source records attributed to Triche, T. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Enrichment of methylated cell-free placental DNA

Abstract. Introduction: Preterm birth drives adverse perinatal maternal and infant health outcomes through heterogeneous symptoms, severity, and etiologies. Delivery prior to reaching 37 weeks of gestation may result from medically indicated intervention for pregnancy complications or spontaneously in the absence of prior symptoms. Placental tissue collected following preterm birth exhibits differential DNA methylation compared to full-term placentas and may indicate pregnancy health during gestation. Placental DNA currently has limited utility for assessing health of ongoing pregnancy, as sampling placental tissue during gestation increases the risk of infection and miscarriage. Risks associated with placental sampling during pregnancy limit the use of DNA methylation in clinical preterm birth prediction. Assessing preterm birth risk during gestation requires non-invasive methods for characterizing placental DNA methylation. Results: We quantified genome-wide DNA methylation patterns of hypermethylated cell-free DNA in pregnant (n = 99) and non-pregnant (n = 93) plasma using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). In each sample, we assessed DNA methylation status in 300-bp genomic windows, examining both sequencing read counts and calculated absolute molar DNA amount. Known hypermethylated placental regions, including RASSF1, STAT5A, and ERG promoters showed significantly increased odds of detection in pregnant samples, suggesting enrichment of cell-free placental DNA. Of the 536,444 300-bp windows examined, 173,071 (32%) showed significant enrichment in pregnant plasma. Linear modeling identified 107,505 differentially methylated regions (DMRs) associated with pregnancies later diagnosed with intrauterine growth restriction (IUGR) (n = 22). Alu elements showed increased representation in these DMRs than expected, while other repetitive elements exhibited underrepresentation. Discussion: These results demonstrate cfMeDIP-seq's ability to enrich for cell-free placental DNA and characterize cell-free DNA methylation signatures of pregnancies complicated by IUGR. Enrichment of cell-free placental DNA enables non-invasive profiling of placental DNA methylation from maternal plasma. Detectable epigenetic signatures in maternal plasma may identify pregnancies at elevated risk for preterm birth before clinical symptoms appear. Our findings further highlight the potential of cell-free placental DNA for monitoring pregnancy health.

genomics↗

bioRxiv2025

Scalable Non-negative Matrix Factorization of the Human Cell Census Reveals Interpretable Transcriptional Programs

Large single-cell atlases now span tens of millions of cells, yet few provide reusable and interpretable reference representations that support direct biological reasoning at atlas-scale. Here, we present an interpretable Non-negative Matrix Factorization reference embedding of 28.5 million healthy cells and approximately 60,000 genes from the Human Cell Census. The resulting gene and cell weights define additive transcriptional programs that align with annotated cell types and organized biological pathways. New datasets can be projected into this fixed reference space without fine-tuning or retraining, as we demonstrate using an independent cystic fibrosis dataset. This resource provides a transparent coordinate system for exploratory analysis and hypothesis generation, complementing deep embeddings that prioritize integration or prediction with a representation designed for interpretability and reuse.

bioinformatics↗

bioRxiv2025

Targeted Nanoparticles: an Innovative Modality in the Treatment of Cancer

Despite progress made in the development of anticancer therapeutics, traditional small-molecule chemotherapeutics often struggle to overcome toxicity, efficacy, and off-target effects. These specific issues can be overcome by either encapsulating the drug or by targeting it directly to the tumor cell. Here, we describe a novel targeted nanoparticle (referred to as a nano-antibody-drug conjugate Targeted Nanosphere or nADC/TNS), based cancer therapeutic platform that can improve the efficacy of a broad range of existing therapeutics. Targeting is antibody-directed, as with antibody-drug conjugates (ADCs). Still, the payload per antibody is vastly greater by orders of magnitude (a thousand for nADC/TNS versus two to eight for ADCs). The nADC/TNS consists of an approximately 80 nm drug-filled nanoparticle composed of phospholipids, cholesterol, and UV cross-linkable diacetylene lipids. We describe the preparation, characterization, and evaluation of nADC/TNS as a novel, versatile, and effective treatment modality for cancer and potentially other diseases. This report focuses on data with nADC/TNS variants NV101 (anti-CD99 targeted, doxorubicin-filled), NV102 (anti-CD19 targeted, doxorubicin-filled), and NV103 (anti-CD99 targeted, irinotecan-filled). We investigated NV101 and NV103 in a mouse model with implanted and metastatic Ewing tumors (ES). NV101 demonstrated significant tumor burden reduction while NV103 induced complete ablation of ES tumors. NV102 demonstrated complete ablation of chemotherapy-resistant relapsed adult lymphocytic leukemia (ALL). These results document the potential superior efficacy of antibody-targeted nanoparticles containing a variety of small-molecule payloads, compared to their free molecule equivalents.

biochemistry↗

bioRxiv2025

BifurcatoR: A Framework for Revealing Clinically Actionable Signal in Variance Masquerading as Noise

BackgroundDisease heterogeneity is a persistent challenge in medicine, complicating both research and treatment. Standard analytical pipelines often assume patient populations are homogeneous, overlooking variance patterns that may signal biologically distinct subgroups. Variance heterogeneity (VH)--including skewness, outliers, and multimodal distributions--offers a powerful but underused lens for detecting latent etiological structures relevant to prognosis and therapeutic response. MethodsA major barrier to VH analysis is the fragmented landscape of available methods, many of which rely on normality assumptions that biological data frequently violate. In addition, existing tools often require programming expertise, and clear guidance on study design considerations--such as sample size and method selection--is lacking. To address these issues, we developed BifurcatoR, an open-source software platform that simplifies the detection, modeling, and interpretation of VH. BifurcatoR integrates simulation-based method evaluation, study design recommendations, and a user-friendly web interface to support VH analysis across a range of data distributions. We benchmarked VH methods through simulation and applied BifurcatoR to two clinical datasets: acute myeloid leukemia (AML) and obesity. ResultsSimulation studies revealed that VH method performance is highly context-specific, varying with distribution shape, mean-variance coupling, and underlying subgroup structure. In AML, BifurcatoR identified two molecularly distinct subgroups with different treatment responses, including an EVI1-high group with significantly poorer prognosis (p < 0.005) among KMT2A-rearranged cases. In a separate study, VH analysis uncovered immunophenotypic subgroups in obesity based on gene-level discordance across monozygotic twin pairs, highlighting latent variation in adipose immune cell composition. ConclusionsVH is not "noise", biological variation without clinical relevance. Instead, VH is a structured signal that can reveal latent and clinically meaningful subtypes. BifurcatoR offers a practical, accessible framework for incorporating VH into biomedical research, with implications for biomarker discovery, patient stratification, and precision medicine.

bioinformatics↗

bioRxiv2024

Bone marrow microenvironment signatures associate with patient survival after guadecitabine and atezolizumab therapy in HMA-resistant MDS

Almost 50% of patients with myelodysplastic syndrome (MDS) are refractory to first-line hypomethylating agents (HMAs), which presents a significant clinical challenge considering the lack of options for salvage. Past work revealed that immune checkpoint molecules on peripheral myeloblasts and immune cells are up-regulated after HMA treatment. Therefore, we conducted a Phase I/II clinical trial combining guadecitabine (an HMA) and atezolizumab (an immune checkpoint inhibitor) to treat HMA-relapsed or refractory (HMA-R/R) MDS patients. This combination therapy showed median overall survival of 15.1 months relative to historical controls (4-6 months). Here, we profiled the cell composition and gene expression signatures of cells from bone marrow aspirates from trial participants with short-term (<15 months) or long-term (>15 months) survival at single-cell resolution. Long-term survivors showed a significant reduction of immunosuppressive monocytes, and an expansion of effector lymphocytes after combination therapy. Further immune profiling suggests that gamma delta T cell activation through primed dendritic cells was associated with global interferon activation in the bone marrow microenvironment of long-term survivors. Short-term survivors exhibited elevated inflammation and senescence-like gene signatures that were not resolved by combination therapy. We propose that distinct bone marrow microenvironment features, such as senescence-associated inflammation or immunosuppressive monocyte presence, could improve patient stratification for HMA and immunotherapy combinations in HMA-R/R MDS patients.

cancer biology↗

bioRxiv2024

EZH2-driven immune evasion defines high-risk pediatric AML with t(16;21) FUS::ERG gene fusion.

The past 25 years of clinical trials have produced few improvements in pediatric AML (pAML) outcomes. This is acutely evident in patients with t(16;21)(p11;q22), yielding FUS::ERG. Patients with FUS::ERG-positive AML relapse quickly and do not respond to transplantation. Major histocompatibility complex (MHC) class I & II receptors and costimulatory molecules are absent at diagnosis in FUS::ERG-positive AML, mirroring the phenotype and outcomes of post-transplant relapse. We show that this is driven by overexpression of EZH2, in vitro and in multiple clinical cohorts. While FUS::ERG AML is the most extreme example, this phenotype is shared by lethal CBFA2T3::GLIS2-driven AML, and patients with RUNX1::RUNX1T1 have significantly worse outcomes when EZH2 overexpression co-occurs. The FDA-approved EZH2 inhibitor tazemetostat reverses this phenotype, re-establishes MHC presentation, and elicits immune effector cell-mediated elimination. EZH2 inhibitors may provide the first targeted therapeutic frontline option for AML patients with FUS::ERG, with the potential for broader frontline immunostimulatory benefits. STATEMENT OF SIGNIFICANCEHere we show an immune-evasive phenotype, present at diagnosis and characterized by elevated EZH2 levels and loss of MHC class I and II, defines a high-risk subtype of acute leukemia. Treatment with the EZH2 inhibitor tazemetostat and IFN-{gamma} reverses this phenotype and results in immune cell engagement and blast elimination.

cancer biology↗

bioRxiv2023

bamSliceR: cross-cohort variant and allelic bias analysis for rare variants and rare diseases

The NCI Genomic Data Commons (GDC) provides controlled access to sequencing data from thousands of subjects, enabling large-scale study of impactful genetic alterations such as simple and complex germline and structural variants. However, efficient analysis requires significant computational resources and expertise, especially when recalling variants from raw sequence reads. We thus developed bamSliceR, an R/Bioconductor package that builds upon the GenomicDataCommons package to extract aligned sequence reads from cross-GDC meta-cohorts, followed by targeted analysis of variants and effects (including transcript-aware variant annotation from transcriptome-aligned GDC RNA data). Here we demonstrate population-scale genomic & transcriptomic analyses with minimal compute burden via bamSliceR, identifying recurrent, clinically relevant sequence and structural variants in the TARGET AML and BEAT-AML cohorts. We then validate results in the (non-GDC) Leucegene cohort, demonstrating how the bamSliceR pipeline can be seamlessly applied to replicate findings in non-GDC cohorts. These variants directly yield clinically impactful and biologically testable hypotheses for mechanistic investigation. bamSliceR has been submitted to the Bioconductor project, where it is presently under review, and is available on GitHub at https://github.com/trichelab/bamSliceR.

bioinformatics↗

bioRxiv2023

Developmental priming of cancer susceptibility

DNA mutations are necessary drivers of cancer, yet only a small subset of mutated cells go on to cause the disease. To date, the mechanisms that determine which rare subset of cells transform and initiate tumorigenesis remain unclear. Here, we take advantage of a unique model of intrinsic developmental heterogeneity (Trim28+/D9) and demonstrate that stochastic early life epigenetic variation can trigger distinct cancer-susceptibility states in adulthood. We show that these developmentally primed states are characterized by differential methylation patterns at typically silenced heterochromatin, and that these epigenetic signatures are detectable as early as 10 days of age. The differentially methylated loci are enriched for genes with known oncogenic potential. These same genes are frequently mutated in human cancers, and their dysregulation correlates with poor prognosis. These results provide proof-of-concept that intrinsic developmental heterogeneity can prime individual, life-long cancer risk.

cancer biology↗

bioRxiv2023

Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes

BackgroundDeconvolution is used to estimate the proportion of mixed cell types from tissue or blood samples based on genomic profiling. DNA methylation is commonly used because specific CpG positions reflect cell type identity and can be accurately measured at either the population or single-molecule level. Methylation sequencing techniques can profile multiple individual CpGs on a single DNA molecule, but few deconvolution models have been developed to exploit these single-molecule methylation haplotypes for cell type deconvolution. Results and ConclusionsWe used simulated whole-genome methylation data and in silico mixtures of real data to compare existing deconvolution tools with two new models developed here. We found that adapting an existing model CelFiE to incorporate methylation haplotype information improved deconvolution accuracy by [~]30% over other tools, including the original CelFiE. In addition to overall higher accuracy, our new tool CelFiE Integrated Single-molecule Haplotypes (or CelFiE-ISH) outperformed others in detecting rare cell types present at 0.1% and below. Detection of rare cell types is important for the analysis of circulating DNA, which we demonstrate using a patient-derived plasma sequencing dataset.Finally,we show that marker selection strategy has a strong effect on deconvolution accuracy, concluding that haplotype-aware deconvolution can take advantage of markers tailored for that purpose.

bioinformatics↗

bioRxiv2022

STORM-seq Reveals Differentiation Trajectories of Primary Human Fallopian Tube Epithelium

Despite significant advances, current single-cell RNA sequencing (scRNA-seq) technologies often struggle with accurately detecting non-coding transcripts, achieving full-length RNA coverage, and/or resolving transcript-level complexity. Many are also difficult to implement or inaccessible without specialized liquid handlers, further limiting their utility. We present Single-cell TOtal RNA-seq Miniaturized (STORM-seq), a random- hexamer primed, ribo-reduced single-cell total RNA sequencing (sc-total-RNA-seq) protocol using standard laboratory equipment. Adapted as a kit, STORM-seq constructs sequence-ready libraries in one working day, producing the highest complexity scRNA- seq libraries to-date, robustly measuring transcript isoforms and clinically relevant gene fusions in single cells. STORM-seq faithfully reconstructs expression profiles of locus- level transposable elements (TEs), and provides high-resolution profiling of transient, low- abundance enhancer RNAs (eRNAs), offering a powerful tool to dissect single-cell gene regulatory networks in unprecedented detail. Applied to human fallopian tube epithelium, the improved transcriptional resolution reveals a putative progenitor-like population and intermediate cell states, shaped by TEs and non-coding RNAs.

molecular biology↗

bioRxiv2021

Fast and robust non-negative matrix factorization for single-cell experiments

Non-negative matrix factorization (NMF) is a popular method for analyzing strictly positive data due to its relatively straightforward interpretation. However, NMF has a reputation as a less efficient alternative to the singular value decomposition (SVD), a standard operation that is highly optimized in most linear algebra libraries. Sparse single-cell sequencing assays, now feasible in thousands of subjects and millions of cells, generate data matrices with tens of thousands of strictly non-negative transcript abundance entries. We present an extremely fast NMF implementation made available in the RcppML (Rcpp Machine Learning library) R package that rivals the runtimes of state-of-the-art Singular Value Decomposition (SVD). NMF can now be run quickly on desktop computers to analyze sparse single-cell datasets consisting of hundreds of thousands of cells. Our method improves upon current NMF implementations by introducing a scaling diagonal to increase interpretability, guarantee consistent regularization penalties across different random initializations, and symmetry in symmetric factorizations. We use our method to show how NMF models learned on standard log-normalized count data are interpretable dimensional reductions, describe interpretable patterns of coordinated gene activities, and explain biologically relevant metadata. We believe NMF has the potential to replace PCA in most single-cell analyses, and the presented NMF implementation overcomes previous challenges with long runtime.

bioinformatics↗

bioRxiv2021

Dysregulated heparan sulfate proteoglycan metabolism promotes Ewing sarcoma tumor growth

The Ewing sarcoma family of tumors is a group of malignant small round blue cell tumors (SRBCTs) that affects children, adolescents and young adults. The tumors are characterized by reciprocal chromosomal translocations that generate chimeric fusion oncogenes, the most common of which is EWSR1-FLI1. Survival is extremely poor for patients with metastatic or relapsed disease, and no molecularly-targeted therapy for this disease currently exists. The absence of a reliable genetic animal model of Ewing sarcoma has impaired investigation of tumor cell/microenvironmental interactions in vivo. We have developed a new genetic model of Ewing sarcoma based on Cre-inducible expression of human EWSR1-FLI1 in wild type zebrafish, which causes rapid onset of SRBCTs at high penetrance. The tumors express canonical EWSR1-FLI1 target genes and stain for known Ewing sarcoma markers including CD99. Growth of tumors is associated with activation of the MAPK/ERK pathway, which we link to dysregulated extracellular matrix metabolism in general and heparan sulfate catabolism in particular. Targeting heparan sulfate proteoglycans with the specific heparan sulfate antagonist Surfen reduces ERK1/2 signaling and decreases tumorigenicity of Ewing sarcoma cells in vitro and in vivo. These results highlight the important role of the extracellular matrix in Ewing sarcoma tumor growth and the potential of agents targeting proteoglycan metabolism as novel therapies for this disease.

cancer biology↗

bioRxiv2021

Compartmap enables inference of higher-order chromatin structure in individual cells from scRNA-seq and scATAC-seq

Single-cell profiling of chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap to reconstruct higher-order chromatin domains in individual cells from transcriptomic (RNAseq) and epigenomic (ATACseq) assays. In cell lines and primary human samples, compartmap infers higher-order chromatin structure comparable to specialized chromatin capture methods, and identifies clinically relevant structural alterations in single cells. This provides a common lens to integrate transcriptional and epigenomic results, linking higher-order chromatin architecture to gene regulation and to clinically relevant phenotypes in individual cells.

genomics↗

bioRxiv2021

Emerging variants of concern in SARS-CoV-2 membrane protein: a highly conserved target with potential pathological and therapeutic implications

Mutations in the SARS-CoV-2 Membrane (M) gene are relatively uncommon. The M gene encodes the most abundant viral structural protein, and is implicated in multiple viral functions, including initial attachment to the host cell via heparin sulfate proteoglycan, viral protein assembly in conjunction with the N and E genes, and enhanced glucose transport. We have identified a recent spike in the frequency of reported SARS-CoV-2 genomes carrying M gene mutations. This is associated with emergence of a new sub-B.1 clade defined by the previously unreported M:I82T mutation within TM3, the third of three membrane spanning helices implicated in glucose transport. The frequency of this mutation increased in the USA from 0.014% in October 2020 to 1.62% in February 2021, a 116-fold change. While constituting 0.7% of the isolates overall, M:I82T sub-B.1 lineage accounted for 14.4% of B.1 lineage isolates in February 2021, similar to the rapid initial increase previously seen with the B.1.1.7 and B.1.429 lineages, which quickly became the dominant lineages in Europe and California over a period of several months. A similar increase in incidence was also noted in another related mutation, V70L, also within the TM2 transmembrane helix. The rapid emergence of this sub-B.1 clade with recurrent I82T mutation suggests that this M gene mutation is more biologically fit, perhaps related to glucose uptake during viral replication, and should be included in ongoing genomic surveillance efforts and warrants further evaluation for potentially increased pathogenic and therapeutic implications.

genomics↗

bioRxiv2021

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

BackgroundCell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. MethodsTo provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from acute myeloid leukemia (AML) patients. ResultsWe show that cfMeDIP-seq enriches for highly methylated regions, capturing [≥] 97% of methylated spike-in control fragments with [≤] 3% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA in each sample provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to [≤] 1% of the total variance within the data. DiscussionIncorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from methylated DNA immunoprecipitation-sequencing (MeDIP-seq) experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky, to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on Bioconductor (https://bioconductor.org/packages/spiky) and GitHub (https://github.com/trichelab/spiky). Contactmichael.hoffman@utoronto.ca

genomics↗

bioRxiv2020

Children's Hospital Los Angeles COVID-19 Analysis Research Database (CARD) - A Resource for Rapid SARS-CoV-2 Genome Identification Using Interactive Online Phylogenetic Tools

Effective response to the Coronavirus Disease 2019 (COVID-19) pandemic requires genomic resources and bioinformatics tools for genomic epidemiology and surveillance studies that involve characterizing full-length viral genomes, identifying origins of infections, determining the relatedness of viral infections, performing phylogenetic analyses, and monitoring the continuous evolution of the SARS-CoV-2 viral genomes. The Childrens Hospital, Los Angeles (CHLA) COVID-19 Analysis Research Database (CARD) (https://covid19.cpmbiodev.net/) is a comprehensive genomic resource that provides access to full-length SARS-CoV-2 viral genomes and associated meta-data for over 30,000 (as of May 20, 2020) isolates collected from global sequencing repositories and the sequencing performed at the Center for Personalized Medicine (CPM) at CHLA. Reference phylogenetic trees of global and USA viral isolates were constructed and are periodically updated using selected high quality SARS-CoV-2 genome sequences. These provide the baseline and analytical context for identifying the origin of a viral infection, as well as the relatedness of SARS-CoV-2 genomes of interest. A web-based and interactive Phylogenetic Tree Browser supports flexible tree manipulation and advanced analysis based on keyword search while highlighting time series animation, as well as subtree export for graphical representation or offline exploration. A Virus Genome Tracker accepts complete or partial SARS-CoV-2 genome sequence, compares it against all available sequences in the database (>30,000 at time of writing), detects and annotates the variants, and places the new viral isolate within the global or USA phylogenetic contexts based upon variant profiles and haplotype comparisons, in a few seconds. The generated analysis can potentially aid in genomic surveillance to trace the transmission of any new infection. Using CHLA CARD, we demonstrate the identification of a candidate outbreak point where 13 of 31 CHLA internal isolates may have originated. We also discovered multiple indels of unknown clinical significance in the orf3a gene, and revealed a number of USA-specific variants and haplotypes.

genomics↗

bioRxiv2019

Direct therapeutic targeting of SWI/SNF induces epigenetic reprogramming and durable tumor regression in rhabdoid tumor

PurposeRhabdoid tumor is a pediatric cancer characterized by the biallelic inactivation of SMARCB1, a subunit of the SWI/SNF chromatin remodeling complex. SMARCB1 inactivation leads to SWI/SNF redistribution to favor a proliferative dedifferentiated cellular state. Although this deletion is the known oncogenic driver, SWI/SNF therapeutic targeting remains a challenge. Experimental DesignWe define a novel epigenetic mechanism for mithramycin using biochemical fractionation, chromatin immunoprecipitation sequencing (ChIP-seq), and a dual spike-in assay for transposase accessible chromatin sequencing (ATAC-seq). We correlate epigenetic reprogramming with changes with chromatin A/B compartments and promoter accessibility with chromHMM models and RNA-seq. Finally, we demonstrate durable, marked tumor response in an intramuscular rhabdoid tumor xenograft model. ResultsHere we show mithramycin and a second-generation analogue EC8042 evict mutated SWI/SNF from chromatin and are effective in rhabdoid tumor. SWI/SNF blockade triggers chromatin compartment remodeling and promoter reprogramming leading to differentiation and amplification of H3K27me3, the catalytic mark of PRC2. Treatment of rhabdoid rumor xenografts with EC8042 leads to marked, durable tumor regression and differentiation of the tumor tissue into benign mesenchymal tissue, including de novo bone formation. ConclusionOverall, this study identifies a novel therapeutic candidate for rhabdoid tumor and an approach that may be applicable to the 20% of cancers characterized by mutated SWI/SNF. STATEMENT OF TRANSLATIONAL RELEVANCEThere is a tremendous need for novel therapeutic approaches for rhabdoid tumor and the more than 20% of human cancers characterized by dysregulation of the SWI/SNF chromatin remodeling complex. While approaches to target associated complexes, such as PRC2, known to be influenced by dysregulated SWI/SNF are currently being evaluated in the clinic, the direct therapeutic targeting of SWI/SNF has not been explored. Here we identify an inhibitor of SWI/SNF and thoroughly explore the therapeutic development of this compound from a mechanistic and translational perspective thus providing insight into the targeting of this complex as well as a dose, schedule, and biomarker of target inhibition that is immediately clinically translatable.

cancer biology↗

bioRxiv2019

Distinctive epigenomic alterations in NF1-deficient cutaneous and plexiform neurofibromas drive differential MKK/P38 signaling

Benign peripheral nerve sheath tumors are the clinical hallmark of Neurofibromatosis Type 1. They account for substantial morbidity in NF1 and are difficult to manage. Cutaneous (CNF) and plexiform neurofibromas (PNF) share identical histology, but maintain different growth rates and risk of malignant conversion. The reasons for their disparate clinical behavior are not well explained on the basis of recent genome or transcriptome profiling studies. We hypothesized that CNFs and PNFs are epigenetically distinct tumor types that exhibit differential signaling due to genome-wide and site-specific methylation events. We interrogated the methylation profiles of 45 CNFs and 17 PNFs (Illumina EPIC 850K) using normal tissue controls from NF1 subjects. Based on these profiles, we confirm that CNFs and PNFs are epigenetically distinct tumors with broad differences in higher order chromatin states, and specific methylation events altering genes involved in key biological and cellular processes such as inflammatory mediator regulation of TRP channels, RAS/MAPK signaling, actin cytoskeleton rearrangement, and oxytocin signaling. Based our identification of 2 separate DMRs associated with alternative leading exons in MAP2K3, we demonstrate differential RAS/MKK3/P38 signaling between CNFs and PNFs. Epigenetic reinforcement of RAS/MKK/P38 was a defining characteristic of CNFs leading to pro-inflammatory signaling and chromatin conformational changes, whereas PNFs signaled predominantly through RAS/ERK. Tumor size also correlated with specific CpG methylation events. Taken together, these findings confirm that epigenetic regulation of RAS signaling fates accounts for observed differences in CNF and PNF clinical behavior. CNFs may also respond differently than PNFs to RAS-targeted therapeutics raising the possibility of targeting P38-mediated inflammation for CNF treatment.

genomics↗