Biology subjects

Woodcock, D. J.

Publications and source records attributed to Woodcock, D. J..

10 recordsLinked to original sources

bioRxiv2026

Novel Methylation Markers in a Prostate Cancer Cohort are Associated with Disease Development and Relapse

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

cancer biology↗

bioRxiv2026

Assessing the effects of a 3D pathology tissue-processing workflow on downstream molecular analyses

Non-destructive 3D pathology methods have emerged in recent years with the potential to enhance standard 2D histopathology by greatly increasing the amount of tissue sampled by imaging and by providing volumetric morphological context. Another key advantage is that tissues remain intact, allowing re-embedding after imaging for potential long-term storage and future histological or molecular analyses. However, the impact of 3D pathology protocols on biomolecules -- including DNA, RNA, and proteins -- and their compatibility with downstream assays, has not been systematically evaluated. Here, we applied a previously optimized 3D pathology protocol -- involving deparaffinization, fluorescent H&E-analog staining, optical clearing, and open-top light-sheet microscopy -- to formalin-fixed paraffin-embedded (FFPE) specimens of breast, prostate, and head and neck cancer. Following the protocol, tissues were re-embedded in paraffin and compared with paired FFPE controls that did not undergo 3D pathology processing. DNA and RNA were extracted and subjected to quality assessments. Amplifiability was tested by PCR and reverse transcription quantitative PCR (RT-qPCR) of housekeeping genes. Although the results showed a slight decrease in the average yield and increased fragmentation of both DNA and RNA, amplifiability was largely preserved. Sanger sequencing of the PCR products confirmed accurate sequence determinations, while total RNA sequencing indicated that the global transcriptomic profile was largely unchanged. IHC staining of common biomarkers produced comparable signals, suggesting those proteins are well preserved after the 3D pathology workflow. These results demonstrate the feasibility of combining 3D pathology with downstream molecular applications.

pathology↗

bioRxiv2025

Redefining the topology of the human bone marrow using augmented spatial transcriptomic analysis

The bone marrow (BM) is the main site of haematopoiesis in adult life. Our understanding of the pathogenesis of BM-derived blood cancers is limited by lack of spatial contextualisation. While emerging spatial transcriptomic (ST) platforms offer unprecedented opportunities for spatially-resolved cellular phenotyping, we recognise and incorporate the power of AI-based tissue feature detection to enhance ST workflows. We perform ST analysis to define the topology of the normal bone marrow (BM) and BM in myeloproliferative neoplasms (MPNs), profiling 5,104,452 cells across 30 human BM samples. Following rigorous histology-based QC, we identify spatially-restricted trajectories of haematopoiesis and extend our understanding of the haematopoietic stem cell (HSC) niche. We find that BM fibrosis in MPN is associated with expansion of distinct immune and stromal co-enriched cell neighbourhoods. We then present a machine learning (ML)-based model trained on ST data that quantifies BM microenvironmental deviation, identifying heretofore unrecognised inter- and intra-individual sample heterogeneity in MPN. Our study demonstrates the potential for AI-based augmented ST analysis, and redefines our understanding of human BM topology.

cancer biology↗

bioRxiv2025

3D pathology-guided microdissection

Traditional micro- and macro-dissection techniques enable the extraction of localized regions in thin tissue sections for molecular analysis. Despite the growing use of 3D microscopy, analogous methods for volumetric microdissection are lacking. We have developed a 3D microdissection method based on computer numerical controlled (CNC) milling integrated with open-top light-sheet microscopy. We demonstrate the ability to study tumor evolution along convoluted 3D branching architectures, which is inaccessible to 2D methods.

bioengineering↗

bioRxiv2023

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx(R), Decipher(R), Prostadiag(R)) using a step-wise approach increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses. Patient summaryProstate cancers are very variable, including within a single tumour. Current genetic scoring systems, which are sometimes used to make decisions for how to treat patients with prostate cancer, are based on sampling methods which do not reflect these variations. We found, using state-of-the-art spatial genetic technology to simulate accurate assessment of variation in biopsies, that the current approaches miss important details which could negatively impact clinical decisions. Take home messageVirtual biopsies from spatial transcriptomic analysis of a whole prostate reveal that current genomic risk scores potentially deliver misleading results as they are based on bulk analysis of prostate biopsies and ignore tumour heterogeneity.

genomics↗

bioRxiv2023

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole genome sequencing with histopathological features

Extension of prostate cancer beyond the primary site into the surrounding organs by local invasion or nodal metastasis is associated with poor prognosis. The emergence and evolution of cancer clones at this early stage of expansion and spread has not been studied in detail. We performed whole genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and regional lymph nodes of five treatment-naive patients with locally advanced disease who underwent radical prostatectomy. Using cancer cell fractions computed from single nucleotide variants and copy number alterations, we reconstructed the tumour phylogenies, which in turn allowed us to infer key molecular steps in the progression of prostate cancer in these individuals. We mapped the clonal composition of cancer sampled across the prostate in each individual and inferred the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes. Based on these data, we delineated the route of tumour progression and metastasis following the transformation of adenocarcinoma to amphicrine morphology, the molecular events leading to whole genome duplication associated with a single clonal expansion and identified putative driver events associated with local invasion and lymph node metastasis. We also correlated genomic changes associated with differences in morphology and identified putative driver events associated with spread to seminal vesicle invasion and lymph node metastasis. Taken together, these findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes associated with morphological transformation. Our results demonstrate the value of integrating multi-region sequencing with histopathological data to study tumour evolution and identify mechanisms of prostate cancer spread.

cancer biology↗

bioRxiv2021

The spatial landscape of clonal somatic mutations in benign and malignant tissue

Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to gain molecular insight into clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120 000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

genomics↗

bioRxiv2020

Genomic copy number predicts oesophageal cancer years before transformation

Cancer arises through a process of somatic evolution and recent studies have shown that aneuploidies and driver gene mutations precede cancer diagnosis by several years to decades1-4 Here, we address the question whether such genomic signals can be used for early detection and pre-emptive cancer treatment. To this end we study Barretts oesophagus, a genomic copy number driven neoplastic precursor lesion to oesophageal adenocarcinoma5. We use shallow whole genome sequencing of 777 biopsies sampled from 88 patients in surveillance for Barretts oesophagus over a period of up to 15 years. These data show that genomic signals exist that distinguish progressive from stable disease with an AUC of 0.87 and a sensitivity of 50% even ten years prior to histopathological disease transformation. These finding are validated on two independent cohorts of 75 and 248 patients. Compared against current patient management guidelines genomic risk classification enables earlier treatment for high risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.

genomics↗

bioRxiv2019

Multiplexing information flow through dynamic signalling systems

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-{kappa}B transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-{kappa}B that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback-Leibler divergences and sensitivity matrices, and link these methods to new theory about multiplexing information. We show that many current models such as those of the NF-{kappa}B system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.

systems biology↗

bioRxiv2017

Paupar LncRNA Promotes KAP1 Dependent Chromatin Changes And Regulates Subventricular Zone Neurogenesis

Many long non-coding RNAs (lncRNAs) are expressed during central nervous system (CNS) development, yet their in vivo roles and molecular mechanisms of action remain poorly understood. Paupar, a CNS expressed lncRNA, controls neuroblastoma cell growth by binding and modulating the activity of genome-wide transcriptional regulatory elements. We show here that Paupar transcript directly binds KAP1, an essential epigenetic regulatory protein, and thereby regulates the expression of shared target genes important for proliferation and neuronal differentiation. Paupar promotes KAP1 chromatin occupancy and H3K9me3 deposition at a subset of distal targets, through formation of a DNA binding ribonucleoprotein complex containing Paupar, KAP1 and the PAX6 transcription factor. Paupar-KAP1 genome-wide co-occupancy reveals a 4-fold enrichment of overlap between Paupar and KAP1 bound sequences. Furthermore, both Paupar and Kap1 loss of function in vivo accelerates lineage progression in the mouse postnatal subventricular zone (SVZ) stem cell niche and disrupts olfactory bulb neurogenesis. These observations provide important conceptual insights into the trans-acting modes of lncRNA-mediated epigenetic regulation, the mechanisms of KAP1 genomic recruitment and identify Paupar and Kap1 as regulators of SVZ neurogenesis.

neuroscience↗