Biology subjects

Elliott, R. J. R.

Publications and source records attributed to Elliott, R. J. R..

6 recordsLinked to original sources

bioRxiv2026

SS18-SSX co-opts P300 to sustain oncogenic transcription independent of SWI/SNF activity

Synovial sarcoma is driven by the SS18-SSX fusion oncoprotein, which has been assumed to promote tumorigenesis through its incorporation into the SWI/SNF chromatin remodeling complexes. Accordingly, therapeutic efforts have focused on targeting SS18-SSX containing SWI/SNF assemblies, yet these approaches have produced limited clinical benefit. Here, we demonstrate that SS18-SSX sustains oncogenic transcription independent of SWI/SNF activity. Despite efficient degradation and dismantling of SWI/SNF complexes, fusion occupancy at target loci and associated gene expression programs remain largely intact. Instead, we identify the acetyltransferase P300 as an essential co-factor supporting SS18-SSX chromatin binding and transcriptional activation. Targeting P300 displaces the fusion from chromatin, suppresses its transcriptional output, compromising synovial sarcoma viability. Notably, dual PROTAC mediated degradation of P300 and SWI/SNF produces strong synergistic effects, broadly disrupting SS18-SSX localization and function. These findings redefine the mechanistic basis of synovial sarcoma and reveal a mechanistically anchored therapeutic strategy for targeting its core oncogenic driver.

cancer biology↗

bioRxiv2025

Dyr726, a brain-penetrant inhibitor of PI3Kα, Type III receptor tyrosine kinases, and WNT signalling

The vast majority of clinical small molecule multi-kinase inhibitors (mKI) report abject failures in targeting cancers with high stem cell contents like high-grade glioma and colorectal cancers. The FDA-approved mKIs to date ablate receptor tyrosine kinase signaling but do not target the paradoxical WNT signaling which is a key survival driver for the self-renewing cancer stem cells. The WNT pathway enhances cancer plasticity and triggers relapse of highly heterogenous tumours. Using de novo synthesis and structure-activity-relationship (SAR) studies with blood-brain-barrier (BBB) penetrant mKI scaffolds, we designed a highly potent and selective small molecule inhibitor of PI3K, PDGFR/KIT, and the WNT pathway denoted Dyr726. Dyr726 is superior to clinical mKIs and inhibits PI3K-AKT-mTOR and WNT-pathway signaling at multiple nodes thereby impeding proliferation, invasion, and tumour growth. Phospho-proteomic, structural, and target engagement analyses, combined with in vitro, in vivo efficacy, and pharmacokinetic studies reveal that Dyr726 is a brain-penetrant small molecule which effectively reduces tumour volume and extends survival of murine orthotopic models. Our current work establishes a first-in-class brain penetrant small molecule mKI which simultaneously antagonize the PI3K-AKT-mTOR and WNT pathways in preclinical cancer stem cell cultures, adult and pediatric primary organoids, and orthotopic murine models with positive efficacy in combination with clinical standard of care.

cancer biology↗

bioRxiv2025

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models

Glioblastoma multiforme (GBM) is an aggressive primary brain tumour that presents significant treatment challenges due to its complex pathology and heterogeneity. The lack of validated molecular targets is a major obstacle for discovering new therapeutic candidates, with no new effective GBM therapies delivered to patients in over two decades. Here, we report the identification of compounds that target the GBM stem cell survival phenotype. Our approach employs machine learning (ML) predictors of cell survival trained on high-throughput, image-based, phenotypic screening data for 3,561 compounds, at multiple concentrations, across a panel of six heterogeneous, patient-derived, GBM stem cell lines. We computationally screened more than 12,000 compounds spanning various chemical classes. Experimental validation of ML-identified candidates across the GBM stem cell lines led to the identification of three compounds with activity against the GBM phenotype. Notably, one of our validated hits, the Hsp90 inhibitor XL888, displayed targeted elimination of all six GBM stem cell lines with IC50 in the nanomolar range. The other two compounds, which displayed broad activity across multiple GBM cell lines with distinct cell line sensitivities, offer routes for future personalised medicine campaigns. Our work demonstrates the use of phenotypic screening in tandem with ML can effectively identify therapeutic leads for personalised treatments in highly heterogeneous indications with few known molecular targets.

cancer biology↗

bioRxiv2024

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

Senescence is a cell-intrinsic tumour suppressive response. A one-two-punch cancer treatment strategy aims to induce senescence in cancerous cells before removing them with a senolytic. It is important to accurately recognise senescent cells to investigate the feasibility of such a treatment strategy and identify compounds that induce senescence in cancer. We focus specifically on the terminal brain cancer glioblastoma, firstly identifying senescent glioblastoma cells with conventional stains, before training a machine learning model to distinguish senescent cells using only a DAPI nuclear stain. To demonstrate how our method can aid drug discovery, we apply our pipeline to existing glioblastoma high-throughput phenotypic drug screening imaging data to identify compounds that induce senescence in glioblastoma and verify these predictions experimentally. Author SummaryDamaged cells can enter a senescent cell state, in which they do not divide, but continue to interact with the environment around them. A novel potential cancer treatment strategy is to make tumor cells senescent, before removing senescent cancer cells with a targeted drug. To investigate this treatment strategy in the brain cancer glioblastoma, it is important to be able to accurately recognise senescent glioblastoma cells. As identifying senescent cells is challenging, we create a machine learning pipeline which can detect senescent glioblastoma cells in imaging data. We show that by applying our method to existing data we can discover compounds that induce senescence in glioblastoma. We verify our predictions by testing the compounds experimentally.

cell biology↗

bioRxiv2023

A cautionary note on the use of N-acetylcysteine as a reactive oxygen species antagonist to assess copper mediated cell death.

A new form of cell death has recently been proposed involving copper-induced cell death, termed cuproptosis. This new form of cell death has been widely studied in relation to a novel class of copper ionophores, including elesclomol and disulfiram. However, the exact mechanism leading to cell death remains contentious. The oldest and mostly widely accepted biological mechanism is that the accumulated intracellular copper leads to excessive build-up of reactive oxygen species and that this is what ultimately leads to cell death. Most of this evidence is largely based on studies using N-acetylcysteine (NAC), an antioxidant, to relieve the oxidative stress and prevent cell death. However, here we have demonstrated using inductively coupled mass-spectrometry, that NAC pretreatment significantly reduces intracellular copper uptake triggered by the ionophores, elesclomol and disulfiram, suggesting that reduction in copper uptake, rather than the antioxidant activity of NAC, is responsible for the diminished cell death. We present further data showing that key mediators of reactive oxygen species are not upregulated in response to elesclomol treatment, and further that sensitivity of cancer cell lines to reactive oxygen species does not correlate with sensitivity to these copper ionophores. Our findings are in line with several recent studies proposing the mechanism of cuproptosis is instead via copper mediated aggregation of proteins, resulting in proteotoxic stress leading to cell death. Overall, it is vital to disseminate this key piece of information regarding NACs activity on copper uptake since new research attributing the effect of NAC on copper ionophore activity to quenching of reactive oxygen species is being published regularly and our studies suggest their conclusions may be misleading.

biochemistry↗

bioRxiv2020

High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery

Oesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies contributing to poor outcomes for patients diagnosed with OAC. We describe the development and application of a phenotypic-led OAC drug discovery platform incorporating image-based, high-content cell profiling and associated image-informatics tools to classify drug mechanism-of-action (MoA). We applied a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six OAC cell lines representing the genetic heterogeneity of disease, a pre-neoplastic Barretts oesophagus line and a non-transformed squamous oesophageal line. We built an automated phenotypic screening and high-content image analysis pipeline to identify compounds that selectively modified the phenotype of OAC cell lines. We further trained a machine-learning model to predict the MoA of OAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes e.g. Methotrexate and three other antimetabolites which are highly selective for OAC cell lines. We further identify a small number of hits from our diverse chemical library which show potent and selective activity for OAC cell lines and which do not cluster with the reference library of known MoA, indicating they may be selectively targeting novel oesophageal cancer biology. Our results demonstrate that our OAC phenotypic screening platform can identify existing pharmacological classes and novel compounds with selective activity for OAC cell phenotypes.

cancer biology↗