Biology subjects

Gendoo, D. M. A.

Publications and source records attributed to Gendoo, D. M. A..

5 recordsLinked to original sources

bioRxiv2026

An Organotypic Oral Squamous Cell Carcinoma Model Recapitulates Epithelial-Stromal Complexity at Single-cell Resolution and Reveals Matrix-derived Signalling as a Therapeutic Target.

Three-dimensional (3D) organotypic cultures recapitulate key structural features of oral tumours and provide controlled, ethical, and reproducible research platforms. However, their ability to faithfully recapitulate in vivo tissue composition and their translational relevance require rigorous validation. Here, we characterised a head and neck squamous cell carcinoma (HNSCC) model using single-cell RNA sequencing to assess maturation, cellular heterogeneity, functionality, and inter- and intra-tissue interactions within epithelial and stromal compartments. The epithelial layer of the model differentiated into populations closely resembling the tissue of tumour origin, including dividing and precursor cells, heterogeneous basal layer, suprabasal cells, and metabolically specialised populations. All epithelial lineages emerged from proliferative progenitors, driven by dynamic transcription factor programmes. The collagen-rich stroma contained functionally diverse fibroblasts reflecting the heterogeneity of cancer-associated fibroblasts in vivo, including dividing, matrix-producing, immune-responsive, and tumour-like populations. Importantly, extensive epithelial-stromal communication networks developed, essential for cancer epithelium maintenance and tumour microenvironment regulation. Matrix-derived signals, particularly fibronectin, osteopontin, and laminins, constituted dominant inputs to CD44-expressing cancer cells and are associated with patient survival in HNSCC, highlighting potential therapeutic targets. Overall, this organotypic HNSCC model exhibits high functional fidelity, captures key tumour elements relevant for therapy and resistance and brings confidence in non-animal drug testing.

cancer biology↗

bioRxiv2025

Integrated genetic screening reveals FEN1 as a driver of stemness and temozolomide resistance in glioblastoma

Glioblastoma (GBM) is a lethal brain tumor with limited response to standard of care chemoradiotherapy. In this study, we conducted genome-wide CRISPR knockout screening in patient-derived glioblastoma stem cells (GSCs) to identify genetic dependencies of cell survival and therapy resistance. Our screening identified flap endonuclease 1 (FEN1) as a key driver of GSC survival, with enhanced dependency under temozolomide (TMZ) treatment. Genetic perturbation of FEN1 reduced GSC self-renewal and proliferation in vitro, and prolonged survival in a patient-derived xenograft model of GBM. FEN1 inhibition (FEN1i) preferentially affected highly aggressive or recurrent GBM models compared with less aggressive GBMs and healthy neural stem cells. Moreover, FEN1 inhibition synergized with TMZ only in these aggressive FEN1i-sensitive GSCs, providing cancer-selective killing and TMZ sensitization in the most untreatable of GBMs. Mechanistically, FEN1i-sensitive GSCs exhibited greater proliferation and sphere formation, while stalling their proliferation conferred resistance to FEN1 inhibition. Single-cell transcriptomics further linked FEN1 expression to stemness and the DNA damage response, elucidating broader determinants of FEN1 dependency. These findings establish FEN1 as a promising therapeutic target in GBM, offering a strategy for both selective targeting and enhancement of TMZ efficacy in aggressive cancers. Statement of SignificanceThis study identifies FEN1 as a key vulnerability of glioblastoma stem cells, revealing its role in therapy resistance and stemness, and proposes FEN1 inhibition as a strategy to enhance temozolomide efficacy.

cancer biology↗

bioRxiv2025

Integrative Multi-omics and Supervised Learning Identifies an Epithelial Signature for Radiotherapy Response in Colorectal Cancer

Colorectal cancer (CRC) is the third most diagnosed cancer globally, accounting for 9.6% of all cancer cases, and the second leading cause of cancer deaths. Radiotherapy is a common treatment, but can demonstrate dangerous side effects and varying patient outcomes that reflects on CRC cancer heterogeneity at genetic, epigenetic, transcriptomics and proteomic levels. Identifying biomarkers capable of effectively predicting CRC patient responses to radiotherapy remains paramount, and an unmet need. We channeled both unsupervised and supervised approaches to assess radiotherapy response for 233 patients of the S:CORT Consortium. Splitting the cohort, we first integrated matched RNA, CNA, mutation, and methylation profiles of 117 patients using multi-omics factor analysis (MOFA). We identified a new radiotherapy signature of 101 biomarkers associated with patients who demonstrate a complete response to radiotherapy, and validated the signature using a random forest classifier on the internal validation dataset, and an independent testing cohort. Our signature effectively predicted treatment outcomes, achieving 89% accuracy with strong discriminatory performance (ROC_AUC = 0.85; PR_AUC = 0.71) to differentiate patients with complete response to radiotherapy compared to incomplete responders. Assessing human and murine scRNAseq datasets underscores that the signature is predominantly expressed in CRC epithelial cells, which underpin CRC heterogeneity and cellular diversity. Our identified signature enables pre-treatment identification of CRC patients that are unlikely to achieve a complete response to radiotherapy, thereby sparing these patients from unnecessary radiation exposure and off-target damage effects.

bioinformatics↗

bioRxiv2017

Whole Genomes Define Concordance of Matched Primary, Xenograft, and Organoid Models of Pancreas Cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 trios of matched primary tumour, PDX, and PDO. We developed a new pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Comparison of genomic events in the tumours and matched disease models displayed single-gene concordance across major PDAC driver genes, and genome-wide similarities of copy number changes. Genome-wide and chromosome-centric analysis of structural variation (SV) events revealed high variability across tumours and disease models, but also highlighted previously unrecognized concordance across chromosomes that demonstrate clustered SV events. Our approach and results demonstrate that PDX and PDO recapitulate PDAC tumourigenesis with respect to simple somatic mutations and copy number changes, and capture major SV events that are found in both resected and metastatic tumours.

bioinformatics↗

bioRxiv2017

Consensus on Molecular Subtypes of Ovarian Cancer

INTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.\n\nMETHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.\n\nRESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.\n\nCONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

cancer biology↗