Biology subjects

Lannagan, T.

Publications and source records attributed to Lannagan, T..

5 recordsLinked to original sources

bioRxiv2024

Viral mimicry redirects immunosuppressed colorectal tumour landscapes towards a proinflammatory and CMS1-like regenerative state

In colorectal cancer (CRC), tumours classifier as consensus molecular subtype 4 (CMS4) have the worst prognosis and derive negligible benefit from chemotherapy. We previously described how repressed interferon-related signalling is associated with increased relapse in CMS4 tumours. Although the viral mimetic poly(I:C) can reduce liver metastasis in vivo, the initial phenotypic changes that underpin its anti-metastatic response remain poorly described, particularly in the immunosuppressed CMS4 tumour microenvironment. Here we characterise lineage-specific anti-metastatic responses induced by poly(I:C), including acute macrophage polarisation and a novel CMS1-like regenerative stem cell state, which drive pro-inflammatory microenvironmental changes in CRC. These insights enabled the development of tractable biomarkers that identify an "immune-warm" patient subset most likely to respond to poly(I:C), enriched for mismatch-repair proficient (pMMR), anti-inflammatory macrophages and CMS4-like features. The viral mimetic poly(I:C) offers a tailored treatment option for CMS4 tumours, by reprogramming stem cell states and activation of an innate-adaptive anti-metastatic response.

cancer biology↗

bioRxiv2023

A Mutation-driven oncofetal regression fuels phenotypic plasticity in colorectal cancer

Targeting cancer stem cells (CSCs) is crucial for effective cancer treatment1. However, the molecular mechanisms underlying resistance to LGR5+ CSCs depletion in colorectal cancer (CRC)2,3 remain largely elusive. Here, we unveil the existence of a primitive cell state dubbed the oncofetal (OnF) state, which works in tandem with the LGR5+ stem cells (SCs) to fuel tumor evolution in CRC. OnF cells emerge early during intestinal tumorigenesis and exhibit features of lineage plasticity. Normally suppressed by the Retinoid X Receptor (RXR) in mature SCs, the OnF program is triggered by genetic deletion of the gatekeeper APC. We demonstrate that diminished RXR activity unlocks an epigenetic circuity governed by the cooperative action of YAP and AP1, leading to OnF reprogramming. This high-plasticity state is inherently resistant to conventional chemotherapies and its adoption by LGR5+ CSCs enables them to enter a drug-tolerant state. Furthermore, through phenotypic tracing and ablation experiments, we uncover a functional redundancy between the OnF and stem cell (SC) states and show that targeting both cellular states is essential for sustained tumor regression in vivo. Collectively, these findings establish a mechanistic foundation for developing effective combination therapies with enduring impact on CRC treatment.

cancer biology↗

bioRxiv2023

Loss of Grem1-articular cartilage progenitor cells causes osteoarthritis.

Osteoarthritis (OA), which carries an enormous disease burden across the world, is characterised by irreversible degeneration of articular cartilage (AC), and subsequently bone. The cellular cause of OA is unknown. Here, using lineage tracing in mice, we show that the BMP-antagonist Gremlin 1 (Grem1) marks a novel chondrogenic progenitor (CP) cell population in the articular surface that generates joint cartilage and subchondral bone during development and adulthood. Notably, this CP population is depleted in injury-induced OA, and with age. OA is also induced by toxin-mediated ablation of Grem1 CP cells in young mice. Transcriptomic analysis and functional modelling in mice revealed articular surface Grem1-lineage cells are dependent on Foxo1; ablation of Foxo1 in Grem1-lineage cells led to early OA. This analysis identified FGFR3 signalling as a therapeutic target, and injection of its activator, FGF18, caused proliferation of Grem1-lineage CP cells, increased cartilage thickness, and reduced OA pathology. We propose that OA arises from the loss of CP cells at the articular surface secondary to an imbalance in progenitor cell homeostasis and present a new progenitor population as a locus for OA therapy.

cell biology↗

bioRxiv2022

MmCMS: Mouse models' Consensus Molecular Subtypes of colorectal cancer

BACKGROUNDColorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODSUsing transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n=577) and mouse (n=57 across n=8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTSWe developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONSWhen applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package with three options helps researchers select suitable mouse models of human CRC subtype for their experimental testing.

cancer biology↗

bioRxiv2021

The Molecular Subtyping Resource (MouSR): a user-friendly tool for rapid biological discovery from human or mouse transcriptional data

Generation of transcriptional data has dramatically increased in the last decade, driving the development of analytical algorithms that enable interrogation of the biology underpinning the profiled samples. However, these resources require users to have expertise in data wrangling and analytics, reducing opportunities for biological discovery by "wet-lab" users with a limited programming skillset. Although commercial solutions exist, costs for software access can be prohibitive for academic research groups. To address these challenges, we have developed an open source and user-friendly data analysis platform for on-the-fly bioinformatic interrogation of transcriptional data derived from human or mouse tissue, called "MouSR". This internet-accessible analytical tool, https://mousr.qub.ac.uk/, enables users to easily interrogate their data using an intuitive "point and click" interface, which includes a suite of molecular characterisation options including QC, differential gene expression, gene set enrichment and microenvironmental cell population analyses from RNA-Seq. Users are provided with adjustable options for analysis parameters to generate results that can be saved as publication-quality images. To highlight its ability to perform high quality data analysis, we utilise the MouSR tool to interrogate our recently published tumour dataset, derived from genetically engineered mouse models and matched organoids, where we rapidly reproduced the key transcriptional findings. The MouSR online tool provides a unique freely-available option for users to perform rapid transcriptomic analyses and comprehensive interrogation of the signalling underpinning transcriptional datasets, which alleviates a major bottleneck for biological discovery.

bioinformatics↗