Biology subjects

Waddell, N.

Publications and source records attributed to Waddell, N..

15 recordsLinked to original sources

bioRxiv2026

Genomic alterations enable BRCA1 methylation loss and promoter bypass to drive resistance in high-grade serous ovarian cancer

BRCA1 promoter methylation predicts sensitivity to PARP inhibitors in high-grade serous ovarian cancer, yet therapeutic resistance is common and mechanistically unresolved. Using long-read direct DNA sequencing of patient-derived xenografts and cell lines, we resolved BRCA1 methylation at single-molecule resolution with structural and transcriptomic analyses. We revealed two convergent PARP inhibitor and platinum resistance mechanisms, validated in patient tumors. First, focal, allele-specific loss of BRCA1 methylation arose through local cis-acting genomic alterations, instead of global epigenetic reprogramming. Engineered in cis sequence alterations near the methylated BRCA1 promoter were sufficient to induce methylation loss, restore homologous recombination, and confer resistance. Similar associations were observed across the genome, suggesting this mechanism extends beyond BRCA1. Second, BRCA1 expression was restored despite intact promoter methylation via structural variant-mediated promoter bypass or alternative transcription initiation. Together, these findings redefine BRCA1 methylation loss as a locus-restricted process and reveal multiple routes by which tumors escape PARP inhibitor therapy. Statement of SignificanceWe show that high-grade serous ovarian cancers can restore BRCA1 expression after therapy through multiple genomic mechanisms, including local methylation loss and promoter bypass, thereby re-establishing homologous recombination and driving PARP inhibitor resistance. These findings challenge reliance on BRCA1 methylation alone as a predictive biomarker and support rational combination therapies for more durable responses.

cancer biology↗

bioRxiv2026

Mapping Functional Tumor Suppressor Networks in Esophageal Adenocarcinoma Using In Vivo CRISPR Screening and Perturb-sequencing

Esophageal adenocarcinoma (EAC) is a genetically heterogeneous malignancy with few recurrent drivers, limiting effective targeted therapies. Although EAC arises from Barretts esophagus (BE), mechanisms driving progression from this premalignant state to invasive cancer remain unclear. We combined pooled CRISPR-Cas9 loss-of-function screening, in vivo tumorigenicity assays, and Perturb-seq profiling to define functional drivers of BE transformation. We identified 37 tumor suppressors whose loss promotes progression to EAC, defining a functional landscape of tumor initiation. Despite genetic diversity, these losses converged on four transcriptional programs involving metabolic reprogramming, cell cycle progression, RNA processing, and cellular motility. Furthermore, we identify loss of NIPBL, TGFBR2, and RPL22 as key mediators of resistance to platinum- and taxane-based chemotherapy. Collectively, these findings provide a unifying framework for genomic heterogeneity in EAC, uncover underappreciated tumor suppressor pathways, and establish a resource to guide mechanistic and translational studies aimed at improving treatment strategies in this aggressive cancer.

cancer biology↗

bioRxiv2025

Agent SPI-WSI: In context learning for computationally spatial pathwayinferring on whole slide histopathology images conditioned on bulk RNAsequencing using pathologist in the loop.

Bulk RNA sequencing, while cost-effective compared to high-resolution spatial transcriptomics, averages gene expression across heterogeneous cell populations and thus lacks spatial context. To address this limitation, we introduce a structured, human-guided, multi-stage computational AI agent SPI-WSI, that iteratively generates, evaluates, and refines biologically informative natural-language prompts, thereby localizing bulk-derived pathway activity within histopathology slides. Our pipeline uses in-context prompting in large language models (LLMs) to adapt dynamically to the task of prompt generation. Candidate prompts are first produced by the LLM and then subjected to a secondary pathologist critiquing by the same LLM that cross-references PubMed to ensure both biological plausibility and specificity. Each approved prompt against their image tile is scored using the vision language foundation model (CONCH). We benchmarked different LLMs, Gemini 2.0, Gemini 2.5, Claude 3.7 and Claude 4.0, and found that Claude 4.0 achieves the highest cosine similarity (0.7) between image and prompt embeddings. Pathologist-driven scoring and manual segmentation confirm that our method accurately identifies clusters of spatial pathological morphologies. In addition, we have validated the method against ground-truth spatial transcriptomic spots using inhouse and public datasets. Overall, the trend emphasized by ground-truth spatial RNA sequencing prompts is closely aligned with those from bulk prompt. This pathologist-in-the-loop workflow enables large-scale, reproducible tissue profiling and grounds AI-driven spatial annotations.

bioinformatics↗

bioRxiv2025

Chromosomal instability shapes the tumor microenvironment of esophageal adenocarcinoma via a cGAS-chemokine-myeloid axis

Chromosomal instability (CIN), a characteristic feature of esophageal adenocarcinoma (EAC), drives tumor aggressiveness and therapy resistance, presenting an intractable problem in cancer treatment. CIN leads to constitutive stimulation of the innate immune cGAS-STING pathway, which has been typically linked to anti-tumor immunity. However, despite the high CIN burden in EAC, the cGAS- STING pathway remains largely intact. To address this paradox, we developed novel esophageal cancer models, including a CIN-isogenic model, discovering myeloid-attracting chemokines - with the chemokine CXCL8 (IL-8) as a prominent hit - as conserved CIN-driven targets in EAC. Using high-resolution multiplexed immunofluorescence microscopy, we quantified the extent of ongoing cGAS-activating CIN in human EAC tumors by measuring cGAS-positive micronuclei in tumor cells, validated by orthogonal whole-genome sequencing-based CIN metrics. By coupling in situ CIN assessment with single-nucleus RNA sequencing and multiplex immunophenotypic profiling, we found tumor cell-intrinsic innate immune activation and intratumoral myeloid cell inflammation as phenotypic consequences of CIN in EAC. Additionally, we identified increased tumor cell-intrinsic CXCL8 expression in CINhigh EAC, accounting for the inflammatory tumor microenvironment. Using a novel signature of CIN, termed CINMN, which captures ongoing CIN-associated gene expression, we confirm poor patient outcomes in CINhigh tumors with signs of aberrantly rewired cGAS-STING pathway signaling. Together, our findings help explain the counterintuitive maintenance and expression of cGAS-STING pathway components in aggressive, CINhigh tumors and emphasize the need to understand the contribution of CIN to the shaping of a pro-tumor immune landscape. Therapeutic strategies aimed at disrupting the cGAS-driven inflammation axis may be instrumental in improving patient outcomes in this aggressive cancer.

cancer biology↗

bioRxiv2025

Demethylating agents drive PARP inhibitor resistance in ovarian carcinomas with BRCA1 gene silencing

DNA methyltransferase 1 inhibitor (DNMT1i) therapy is a promising option for increasing immune response as part of combination cancer therapy. High-grade serous ovarian carcinoma (HGSOC) is a highly aggressive cancer with poor survival outcomes, where DNMT1i therapy is being increasingly explored. HGSOC with epigenetically silenced BRCA1 has been shown to respond to PARP inhibitor (PARPi) treatment - a core targeted therapy for HGSOC. However, loss of silencing of even a single BRCA1 allele causes PARPi and platinum chemotherapy resistance. We tested whether BRCA1 silencing was robust to DNMT1i therapy, or would be reversed, thus driving PARPi resistance. We previously generated two homozygously silenced BRCA1 HGSOC cell lines: WEHI-CS62 and an OVCAR8 derivative. DNMT1i treatment caused sustained BRCA1 promoter methylation loss, gene re-expression and PARPi resistance in both of these silenced BRCA1 lines, but not in mutated BRCA1/2 or RAD51C lines. Methylation arrays confirmed transient global CpG methylation losses following DNMT1i. CRISPR deletion of the re-expressed BRCA1 copy in WEHI-CS62 restored silencing and PARPi sensitivity. Furthermore, DNMT1i treatment of a silenced BRCA1 PDX caused heterogeneous BRCA1 promoter methylation loss. In summary, DNMT1 inhibitors caused sustained reduction of BRCA1 promoter methylation in HGSOC cells. This resulted in BRCA1 re-expression and PARP inhibitor resistance, presenting a significant risk for up to 17% of HGSOC patients with BRCA1 gene silencing who could benefit from PARP inhibitor therapy. We conclude that DNA demethylation therapy should be avoided for HGSOC patients with epigenetically silenced BRCA1.

cancer biology↗

bioRxiv2025

Explainable machine learning identifies features and thresholds predictive of immunotherapy response

BackgroundImmunotherapy has improved patient survival for multiple cancer types, including melanoma. While a variety of molecular features have been linked to response to immune checkpoint inhibitors (ICI) treatment, clinically established biomarkers, such as tumour mutation burden (TMB) and PD-L1 expression, have shown limitations in accurately categorising responders versus non-responders. Due to the complex nature of ICI response, which includes cancer intrinsic and extrinsic features within the tumour microenvironment (TME), using a single biomarker to predict response is insufficient, necessitating the need to identify accurate clinical and multi-omic molecular predictors. MethodsWe integrate clinical, DNA and RNA sequencing data from four datasets, comprising 138 melanoma patients treated with ICI, to develop machine learning models for predicting ICI response. The performance of each model was evaluated using an independent dataset of patients with cutaneous melanoma (n=53). Interactions between trained models and features were rationalised using the explainability method SHAP. ResultsThe most optimal model was the multi-omic random forest model, with AUC-ROC of 0.78 when predicting response in the independent test dataset. Using SHAP, we predicted thresholds for mutational signatures, neoantigen load, immune cell-type abundance and immune receptor LAG3 expression. The relationship between these influential features and their SHAP scores revealed numerical thresholds constituting good and poor patient response. ConclusionsThis approach highlights patient response to ICI is influenced by both cancer intrinsic and extrinsic features, as well as identifies candidate biomarkers that could inform the use of ICI and potentially assist in the selection of combination therapy in melanoma treatment.

bioinformatics↗

bioRxiv2024

Generation of a PARPi-sensitive homozygous BRCA1-methylated OVCAR8 cell line using targeted CRISPR gene editing

Up to 17% of high grade serous ovarian carcinomas (HGSOC) harbour BRCA1 promoter methylation (meBRCA1), making them susceptible to treatment with targeted PARP inhibitor (PARPi) therapy. Unfortunately, meBRCA1 loss can be acquired following PARPi or platinum chemotherapy, resulting in BRCA1 re-expression and PARPi resistance. Our understanding of meBRCA1 stability in HGSOC is currently limited, in part due to a paucity of pre-clinical models with homozygous meBRCA1. Herein, we describe the generation of a several OVCAR8 cell line derivatives containing landing pad constructs, for future functional studies, and representing various BRCA1 states, including a homozygous meBRCA1 variant. Our PARPi resistant OVCAR8 has two methylated BRCA1 copies and one unmethylated copy, enabling BRCA1 expression. CRISPR-Cas9 gene editing was used to delete copies of the BRCA1 gene in landing pad-containing clones of this cell line (A6 and H4). We produced one variant with deletion of all BRCA1 copies (H4-53), and another with two copies deleted and only a single methylated gene copy remaining (A6-30 - validated further using nanopore long-read sequencing). These both lacked BRCA1 gene expression and were sensitive to PARPi treatment. The A6-30 line was transplanted into immunocompromised mice to generate a xenograft model that retained homozygous meBRCA1 and demonstrated some response to PARPi in vivo. Thus, using CRISPR gene editing we have created several novel isogenic HGSOC cell line models, including one with homozygous meBRCA1, that will support future studies of meBRCA1 stability and PARPi resistance.

cancer biology↗

bioRxiv2022

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Trust and transparency are critical for deploying deep learning (DL) models into the clinic. DL application poses generalisation obstacles since training/development datasets often have different data distributions to clinical/production datasets that can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models used to predict cancer of unknown primary with three independent RNA-seq datasets covering 10,968 samples across 57 primary cancer types. Our results highlight simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation (e.g., p-value = 0.0013 for calibration). Moreover, we demonstrate Bayesian DL substantially improves accuracy under data distributional shifts when utilising uncertainty thresholding by designing a prototypical metric that evaluates the expected (accuracy) loss when deploying models from development to production, which we call the Area between Development and Production curve (ADP). In summary, Bayesian DL is a hopeful avenue of research for generalising uncertainty, which improves performance, transparency, and therefore safety of DL models for deployment in real-world.

bioinformatics↗

bioRxiv2021

Genomic analysis of patient-derived xenograft models reveals intra-tumor heterogeneity in endometrial cancer and can predict tumor growth inhibition with talazoparib

BackgroundEndometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. MethodsHere, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the different molecular subtypes of EC. ResultsPDX models were successfully generated from all four molecular subtypes of EC and uterine carcinosarcomas, and they recapitulated morphology and the molecular landscape of primary tumors without major genomic drift. We also observed a wide range of inter-tumor and intra-tumor heterogeneity, well captured by different PDX lineages, which could lead to different treatment responses. An in vivo response to talazoparib was detected in two p53mut models consistent with stable disease, however both lacked the HR deficiency genomic signature. ConclusionsEC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the p53mut molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.

genomics↗

bioRxiv2020

Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma

While loss of BRCA1 promoter methylation has been shown to cause PARP inhibitor (PARPi) resistance in high-grade serous ovarian carcinoma (HGSC), the impacts of RAD51C methylation (meRAD51C) remain unresolved. In this study, three PARPi-responsive HGSC patient-derived xenografts (PDX) with RAD51C gene silencing and homologous recombination deficiency were found to have either homogeneous or heterogeneous patterns of meRAD51C. PDX could lose meRAD51C following PARPi treatment (rucaparib/niraparib), where a single unmethylated RAD51C copy was sufficient to drive PARPi-resistance. Genomic profiling revealed this resistance was acquired independently in two distinct PDX lineages. Furthermore, we describe a patient sample where 1/3 RAD51C gene copies lost methylation following neoadjuvant chemotherapy. We show meRAD51C is a positive predictive biomarker for PARPi response and should be screened for routinely in patients. However, methylation loss in a single gene copy is sufficient to cause PARPi resistance and should be carefully assessed in previously treated patients considering PARPi therapy.

cancer biology↗

bioRxiv2020

Anatomic position determines oncogenic specificity in melanoma

Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programs in the cell of origin. Here, we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet, or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma but CRKL amplifications in acral melanoma. We modeled these changes in transgenic zebrafish models and found that CRKL-driven tumors predominantly formed in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin/limb melanocytes, compared to body melanocytes, revealed a positional identity gene program typified by posterior HOX13 genes. This positional gene program synergized with CRKL to drive tumors at acral sites. Abrogation of this CRKL-driven program eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.

cancer biology↗

bioRxiv2020

APC Mutation marks an aggressive subtype of BRAF mutant colorectal cancers that are associated with early-onset and dismal prognosis

Background & AimsWNT activation is a hallmark of colorectal cancer. BRAF mutation is present in 15% of colorectal cancers, and the role of mutations in WNT signaling regulators in this context is unclear. Here we evaluate the mutational landscape of WNT signaling regulators in BRAF mutant cancers. MethodsWe performed exome-sequencing on 24 BRAF mutant colorectal cancers and analysed these data in combination with 175 publicly available BRAF mutant colorectal cancer exomes. We assessed the somatic mutational landscape of WNT signaling regulators, and performed hotspot and driver mutation analyses to identify potential drivers of WNT signaling. The effects of Apc and Braf mutation were modelled, in vivo, using the Apcmin/+ and BrafV637/Villin-CreERT2/+ mouse, respectively. ResultsRNF43 was the most frequently mutated WNT signaling regulator (41%). Mutations in the beta-catenin destruction complex occurred in 48% of cancers. Hotspot analyses identified potential cancer driver genes in the WNT signaling cascade, including MEN1, GNG12 and WNT16. Truncating APC mutation was identified in 20.8% of cancers. Truncating APC mutation was associated with early age at diagnosis (P< 2x10-5), advanced stage (P<0.01), and poor survival (P=0.026). Apcmin/+/BrafV637 animals had more numerous and larger SI and colonic lesions (P<0.0001 and P<0.05, respectively), and a markedly reduced survival (Median survival: 3.2 months, P=8.8x10-21) compared to animals with Apc or Braf mutation alone. ConclusionsThe WNT signaling axis is frequently mutated in BRAF mutant colorectal cancers. WNT16 and MEN1 may be novel drivers of aberrant WNT signaling in colorectal cancer. Co-mutation of BRAF and APC generates an extremely aggressive neoplastic phenotype that is associated with poor patient outcome. SynopsisWe have comprehensively evaluated the somatic mutation landscape of WNT signaling regulators in serrated colorectal cancers. We identified a mosaic of mutations that may be responsible for elevating WNT signaling in this context. Approximately 20% of serrated colorectal cancers harbor truncating APC mutation, and these cancers confer extremely poor prognoses.

cancer biology↗

bioRxiv2019

Chromatin interactome mapping at 139 independent breast cancer risk signals

Genome-wide association studies have identified 196 high confidence independent signals associated with breast cancer susceptibility. Variants within these signals frequently fall in distal regulatory DNA elements that control gene expression. We designed a Capture Hi-C array to enrich for chromatin interactions between the credible causal variants and target genes in six human mammary epithelial and breast cancer cell lines. We show that interacting regions are enriched for open chromatin, histone marks for active enhancers and transcription factors relevant to breast biology. We exploit this comprehensive resource to identify candidate target genes at 139 independent breast cancer risk signals, and explore the functional mechanism underlying altered risk at the 12q24 risk region. Our results demonstrate the power of combining genetics, computational genomics and molecular studies to rationalize the identification of key variants and candidate target genes at breast cancer GWAS signals.

molecular biology↗

bioRxiv2018

Genome Scale Epigenetic Profiling Reveals Five Distinct Subtypes of Colorectal Cancer

BACKGROUNDColorectal cancer is an epigenetically heterogeneous disease, however the extent and spectrum of the CpG Island Methylator Phenotype (CIMP) is not clear.\n\nRESULTSAn unselected cohort of 216 colorectal cancers clustered into five clinically and molecularly distinct subgroups using Illumina 450K DNA methylation arrays. CIMP-High cancers were most frequent in the proximal colons of female patients. These dichotomised into CIMP-Hl and CIMP-H2 based on methylation profile which was supported by over representation of BRAF (74%, P<0.0001) or KRAS (55%, P<0.0001) mutation, respectively. Congruent with increasing methylation, there was a stepwise increase in patient age from 62 years in the CI MP-Negative subgroup to 75 years in the CIMP-Hl subgroup (P<0.0001). There was a striking association between PRC2-marked loci and those subjected to significant gene body methylation in CIMP-type cancers (P<1.6xl078). We identified oncogenes susceptible to gene body methylation and Wnt pathway antagonists resistant to gene body methylation. CIMP cluster specific mutations were observed for genes involved in chromatin remodelling, such as in the SWI/SNF and NuRD complexes, suggesting synthetic lethality.\n\nCONCLUSIONThere are five clinically and molecularly distinct subgroups of colorectal cancer based on genome wide epigenetic profiling. These analyses highlighted an unidentified role for gene body methylation in progression of serrated neoplasia. Subgroup-specific mutation of distinct epigenetic regulator genes revealed potentially druggable vulnerabilities for these cancers, which may provide novel precision medicine approaches.

genomics↗

bioRxiv2018

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.\n\nMethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.\n\nResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.\n\nConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.

bioinformatics↗