Biology subjects

Bishop, C. L.

Publications and source records attributed to Bishop, C. L..

4 recordsLinked to original sources

bioRxiv2025

SAMP-Score: A morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells

BackgroundSenescence identification is rendered challenging due to a lack of universally available biomarkers. This represents a bottleneck in efforts to develop pro-senescence therapeutics - agents designed to induce the arrest of cellular proliferation associated with a senescence response in cancer cells for therapeutic gain. This is particularly true in contexts such as basal-like breast cancer (BLBC), which often express high levels of widely reported senescence hallmarks, which has led to the designation of these subtypes as senescence marker positive (Sen-Mark+). Unfortunately, these are often cancers with the most limited treatment options, where novel pro-senescence compounds would be of potential clinical utility. ResultsTo address these challenges, we have developed SAMP-Score, a machine learning classification tool for identifying senescence induction in Sen-Mark+ cancers. This technique builds upon our previous observation that senescent cells develop distinct senescence-associated morphological profiles (SAMPs), which can be assessed readily in traditionally challenging contexts for senescence identification, including high-throughput screens. ConclusionsThrough application of SAMP-Score, we have identified QM5928, a novel pro-senescence compound, that is able to induce senescence in a variety of Sen-Mark+ cancers and has potential utility as a tool molecule to explore the mechanisms and pathways through which senescence induction occurs in these cells.

cancer biology↗

bioRxiv2023

SenPred: A single-cell RNA sequencing-based machine learning pipeline to classify senescent cells for the detection of an in vivo senescent cell burden

Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required for senescent cell identification. However, emerging scRNA-seq datasets have enabled increased understanding of the heterogeneity of senescence. Here we present SenPred, a machine-learning pipeline which can identify senescence based on single-cell transcriptomics. Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental and inter-experimental fibroblast senescence to a high degree of accuracy (>99% true positives). We position this as a proof-of-concept study, with the goal of building a holistic model to detect multiple senescent subtypes. Importantly, utilising scRNA-seq datasets from deeply senescent fibroblasts grown in 3D refines our ML model leading to improved detection of senescent cells in vivo. This has allowed for detection of an in vivo senescent cell burden, which could have broader implications for the treatment of age-related morbidities.

cell biology↗

bioRxiv2020

Early growth response 2 (EGR2) is a novel regulator of the senescence program

Senescence, a state of stable growth arrest, plays an important role in ageing and age-related diseases in vivo. Although the INK4/ARF locus is known to be essential for senescence programs, the key regulators driving p16 and ARF transcription remain largely underexplored. Using siRNA screening for modulators of the p16/pRB and ARF/p53/p21 pathways in deeply senescent human mammary epithelial cells (DS HMECs) and fibroblasts (DS HMFs), we identified EGR2 as a novel regulator of senescence. EGR2 expression is up-regulated during senescence and its ablation by siRNA in DS HMECs and HMFs transiently reverses the senescent phenotype. We demonstrate that EGR2 activates the ARF and p16 promoters and directly binds to the ARF promoter. Loss of EGR2 downregulates p16 levels and increases the pool of p16- p21- reversed cells in the population. Moreover, EGR2 overexpression is sufficient to induce senescence. Our data suggest that EGR2 is a regulator of the p16/pRB and direct transcriptional activator of the ARF/p53/p21 pathways in senescence and a novel marker of senescence.

cell biology↗

bioRxiv2018

Ribosomal stress-induced senescence as a novel pro-senescence strategy for p16 positive basal-like breast cancer

Re-engaging the senescent programme represents an attractive yet underexplored strategy for cancer therapy, particularly for those tumour subtypes where targeted agents are limited or unavailable. Here, we identify a specific subset of ribosomal proteins as novel pro-senescence therapeutic targets for a highly aggressive subtype of breast cancer, p16 positive basal-like breast cancer. Mechanistically, ribosomal stress-induced senescence generates a stable cell cycle arrest, is dependent on endogenous p16 triggering a resensitisation to the p16/RB tumour suppressor axis, followed by establishment of a senescence-associated secretory phenotype, and is independent of DNA damage. Conversely, ribosomal protein knockdown in a p16 negative breast cancer model results in caspase-mediated apoptosis. Importantly, individual ribosomal protein loss is well tolerated by a panel of normal human cells. We demonstrate a reciprocal feedback loop between loss of RPS3A and RPS7 at both the transcriptional and post-transcriptional level during ribosomal stress-induced senescence. Further, our ribosomal hits are co-ordinately dysregulated in breast cancer, with elevated expression associated with a poor prognosis. Clinical relevance is demonstrated in tissue microarrays, and a RPS3AHIGHRPS7HIGH signature is associated with an earlier disease onset and synergises with p16 to further worsen patient outcome. We conclude that dysregulation of ribosomal proteins constitutes a cancer cell-specific mechanism of senescence evasion and that engaging ribosomal stress-induced senescence may be relevant for future pro-senescence therapies.

cancer biology↗