Biology subjects

Semple, C. A.

Publications and source records attributed to Semple, C. A..

15 recordsLinked to original sources

bioRxiv2026

Explainable machine learning relates histological to genomic pathology

Background & AimsHaematoxylin and eosin (H&E) staining remains the diagnostic gold standard for solid cancers, including hepatocellular carcinoma, and is increasingly complemented by genomic profiling for precision medicine. Inferring genomic alterations directly from H&E images could streamline testing, but heterogeneity and biases in human training data limit interpretation of genotype-phenotype associations. Here, we aimed to relate histologic to genomic pathology to provide biological explainability for mutation prediction models and assess the impact of germline variation on model performance. MethodsWe analysed 597 murine liver tumours with matched whole-genome sequencing and histopathology (163,835 image tiles; 22.9 million nuclei). Our controlled in vivo design accounted for germline variation, biological sex, and causal mutagen (N-diethylnitrosamine), removing confounding factors present in human cohorts. We trained and evaluated deep learning and supervised machine learning models to predict germline variation and cancer driver alterations from H&E. ResultsModelling accurately predicted germline and somatic alterations from histology, at both locus-specific and genome-wide scales. Quantitative image analysis revealed an unexpected association between Egfr driver mutations and hepatic steatosis, linking genotype to an interpretable morphological phenotype. While model performance declined when applied to tumours from unrepresented genetic backgrounds, this limitation was biologically informative, revealing strain-dependent differences in tumour evolution, notably the prevalence of whole-genome duplication. ConclusionsMachine learning integration of histological and genomic pathology enables accurate, interpretable inference of genetic alterations from H&E, potentially reducing reliance on costly ancillary molecular assays. Our predictions are supported by human-interpretable biological features, addressing concerns around "black-box" technologies. However, caution is required when applying such methods to samples with a genetic background that, even if closely related, is beyond the genetic horizon of training data.

pathology↗

bioRxiv2025

Frequent Whole-Genome Duplication Events Drive the Genomic Evolution of Triple-Negative Breast Cancer During Neoadjuvant Chemotherapy

BackgroundTriple-negative breast cancer (TNBC) is associated with poor survival rate and high genomic instability, generating complex tumour genomes. However, the processes that generate this complexity are poorly studied in longitudinal samples. Here, we study the temporal dynamics of TNBC somatic mutations, revealing major transitions in tumour genome evolution, from diagnostic biopsies, through treatment, to cancer remission or recurrence. MethodsDeep whole exome sequencing and CUTseq, a reduced representation whole genome sequencing approach, were performed in parallel, to comprehensively identify short nucleotide variants (SNVs), copy number alterations (CNAs) and aneuploidies. Tumour samples (N=74) from 22 patients were profiled before and after neoadjuvant chemotherapy (NACT), and encompassed spatially diverse samples from multiple primary breast tumours, to allow tracking of the gain and loss of candidate driver variants over time. ResultsGenome-wide SNV mutational burden remained stable across disease progression and RCB classes. However, recurrent SNVs were identified in several known TNBC driver genes in response to treatment, with TP53, MICA, CYP2D6, BRCA1, and BRCA2 being frequently altered. The candidate driver variants in these genes frequently exhibited dynamic changes throughout the course of a patients treatment, with the original SNVs in pre-treatment samples often lost, while novel variants in the same genes emerged at subsequent time points. In contrast to the stable genome-wide SNV burdens, dramatic changes in chromosome structure were seen in all tumours, with abundant CNAs and chromosome arm aneuploidies seen in pre-treatment samples, followed by frequent loss of these alterations post-treatment, and their re-emergence at recurrence. Whole genome duplication (WGD) events appear to drive these dynamics, with a higher frequency of pre-treatment WGD seen in patients with the best response to NACT. ConclusionsComprehensive longitudinal profiling of the TNBC genome demonstrates the complex interplay of SNVs and structural alterations during tumour progression, leading to diverse evolutionary trajectories impacting patient outcomes. Complex mutational patterns encompassing entire chromosomes emerge during progression, with WGD events making major contributions to intra-tumour heterogeneity, and emerging as a potential candidate biomarker of response at both tumour establishment and recurrence.

genomics↗

bioRxiv2025

Systematic identification of context-dependent gene essentiality in Glioblastoma: The GBM-CoDE platform

Glioblastoma (GBM) is a heterogeneous and aggressive brain tumour that is invariably fatal despite maximal treatment. Genetic or transcriptomic biomarkers could be used to stratify patients for treatments, however, pairing biomarkers with appropriate therapeutic targets is challenging. Consequently, therapeutics have not yet been optimised for specific GBM patient subsets. Here we integrate genome-wide CRISPR/Cas9 knockout screening and genetic-annotation data for 60 distinct patient-derived, IDHwildtype, adult GBM cell lines, quantifying the essentiality of 15,145 genes. We describe a novel method using Targeted Learning, to estimate the effect size of GBM-relevant biomarkers on context-dependent gene essentiality (GBM-CoDE). We derive multiple target-biomarker pair hypotheses, which we release in an accessible platform to accelerate translation to biomarker-stratified clinical trials. Two of these (WWTR1 with EGFR mutation/amplification, and VRK1 with VRK2 expression suppression) have been validated in GBM, implying that our additional novel findings may be valid. Our method is readily translatable to other cancers of unmet need.

bioinformatics↗

bioRxiv2025

Genetic background sets the trajectory of cancer evolution

Human cancers are heterogeneous. Their genomes evolve from genetically diverse germlines in complex and dynamic environments, including exposure to potential carcinogens. This heterogeneity of humans, our environmental exposures, and subsequent tumours makes it challenging to understand the extent to which cancer evolution is predictable. Addressing this limitation, we re-ran early tumour evolution hundreds of times in diverse, inbred mouse strains, capturing genetic variation comparable to and beyond that found in human populations. The sex, environment, and carcinogenic exposures were all controlled and tumours comprehensively profiled with whole genome and transcriptome sequencing. Within a strain, there was a high degree of consistency in the mutational landscape, a limited range of driver mutations, and all strains converged on the acquisition of a MAPK activating mutation with similar transcriptional disruption of that pathway. Despite these similarities in the phenotypic state of tumours, different strains took markedly divergent paths to reach that state. This included pronounced biases in the precise driver mutations, the strain specific occurrence of whole genome duplication, and differences in subclonal selection that reflected both cancer susceptibility and tumour growth rate. These results show that interactions between the germline genome and the environment are highly deterministic for the trajectory of tumour genome evolution, and even modest genetic divergence can substantially alter selection pressures during cancer development, influencing both cancer risk and the biology of the tumour that develops.

genomics↗

bioRxiv2024

Protein structural context of cancer mutations reveals molecular mechanisms and identifies novel candidate driver genes

Advances in structure determination and computational modelling are enabling us to study the protein structural context of human genetic variants at an unprecedented scale. Here, we investigate millions of human cancer-associated missense mutations in terms of their structural locations and predicted perturbative effects. We find that, while cancer-driving mutations have properties similar to other known disease-causing mutations, this is obscured by the abundance of passenger mutations in cancer sequencing datasets. Nevertheless, by considering the collective properties of mutations at the level of individual proteins, we identify distinct mutational signatures associated with tumour suppressors and oncogenes. Tumour suppressors are enriched in structurally damaging mutations, consistent with loss-of-function mechanisms. In contrast, oncogene mutations tend to be structurally mild, reflecting selection for gain-of-function driver mutations and against loss-of-function mutations. Although oncogenes are difficult to distinguish from genes with no role in cancer using only structural damage, we find that an alternate metric based on the clustering of mutations in three-dimensional space is highly predictive of oncogenes, particularly when mutation recurrence is considered. These observations allow us to identify novel candidate driver genes and speculate about their molecular roles, which we expect to have general utility in the analysis of cancer sequencing data.

bioinformatics↗

bioRxiv2024

Stage-resolved chromatin conformation dynamics during human meiosis

BackgroundDuring meiosis, the mammalian genome is organised within chromatin loops, which facilitate synapsis, crossing over and chromosome segregation, setting the stage for recombination events and the generation of genetic diversity. Chromatin looping is thought to play a major role in the establishment of cross overs during prophase I of meiosis, in diploid early primary spermatocytes. However, chromatin conformation dynamics during human meiosis are difficult to study experimentally, due to the transience of each cell division and the difficulty of obtaining stage-resolved cell populations. Here, we employed a machine learning framework trained on single cell ATAC-seq and RNA-seq data to predict CTCF-anchored looping during spermatogenesis, including cell types at different stages of meiosis. ResultsWe find dramatic changes in genome-wide looping patterns throughout meiosis: compared to pre-and-post meiotic germline cell types, loops in meiotic early primary spermatocytes are more abundant, more variable between individual cells, and more evenly spread throughout the genome. In preparation for the first meiotic division, loops also include longer stretches of DNA, encompassing more than half of the total genome. These loop structures then influence the rate of recombination initiation and resolution as cross overs. In contrast, in later mature sperm stages, we find evidence of genome compaction, with loops being confined to the telomeric ends of the chromosomes. ConclusionOverall, we find that chromatin loops do not orchestrate the gene expression dynamics seen during spermatogenesis, but loops do play important roles in recombination, influencing the positions of DNA breakage and cross over events.

genomics↗

bioRxiv2024

Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer

Deciphering the structural variation across tumour genomes is crucial to determine the events driving tumour progression and better understand tumour adaptation and evolution. High grade serous ovarian cancer (HGSOC) is an exemplar tumour type showing extreme, but poorly characterised structural diversity. We comprehensively describe the mutational landscape driving HGSOC, exploiting a large (N=324), deeply whole genome sequenced dataset. We reveal two divergent evolutionary trajectories, affecting patient survival and involving differing genomic environments. One involves homologous recombination repair deficiency (HRD) while the other is dominated by whole genome duplication (WGD) with frequent chromothripsis, breakage-fusion-bridges and extra-chromosomal DNA. These trajectories contribute to structural variation hotspots, containing novel candidate driver genes with significantly altered expression. While structural variation predominantly drives tumorigenesis, we also find high mtDNA mutation loads associated with shorter patient survival, and acting in combination with alterations in the nuclear genome to impact prognosis and suggesting new strategies for patient stratification.

cancer biology↗

bioRxiv2023

Mutational scanning reveals oncogenic CTNNB1 mutations have diverse effects on signalling and clinical traits

CTNNB1, the gene encoding {beta}-catenin, is a frequent target for oncogenic mutations activating the canonical Wnt signalling pathway, typically via missense mutations within a degron hotspot motif in exon 3. Here, we combine saturation genome editing with a fluorescent reporter assay to quantify signalling phenotypes for all 342 missense mutations in the mutation hotspot, including 74 recurrent mutations reported in over 6000 tumours. Our data define the genetic requirements for {beta}-catenin degron function and reveal diverse levels of signal activation among known driver mutations. Tumorigenesis in different human tissues involves selection for CTNNB1 mutations spanning distinct ranges of effect size. In hepatocellular carcinoma, mutations that activate {beta}-catenin relatively weakly are associated with worse prognosis compared to stronger activating mutations, despite greater immune cell infiltration in the tumour microenvironment. Our work therefore provides a resource to understand mutational diversity within a pan-cancer mutation hotspot, with potential implications for targeted therapy.

cancer biology↗

bioRxiv2022

Regionally enriched rare deleterious exonic variants in the UK and Ireland

Geographic clustering of haplotypes appears to have emerged in the UK as a result of differing patterns of immigration and drift in regions that have been relatively isolated from each other. However, until recently it has been unclear how such patterns of regional genetic differentiation might impact the protein-coding fraction of the genome. Here, we exploit UK Biobank (UKB) and Viking Genes whole exome sequencing data to study regional genetic differentiation across the UK and Ireland in protein coding genes, encompassing 20 regions of origin and 44,696 unrelated individuals. We rediscover the strong influence of genetic drift in shaping variation in the Northern Isles of Scotland and among those with full or partial Ashkenazi Jewish (AJ) ancestry. For full AJ, almost half the known rare exonic variants (45%) are at least two-fold more or less frequent than in a Europe-wide reference sample, while the degree of variant frequency differences in Shetland and Orkney are comparable to part AJ (19%, 17%, 16%, respectively). We also demonstrate substantial genetic differentiation among several mainland regions of origin, particularly north and south Wales, SE Scotland and Ireland. With stringent filtering criteria we found 67 variants likely to have adverse biomedical consequences, enriched by at least five-fold in frequency in one or more British or Irish regions relative to a European reference group, and we calculate that this may lead to tens or hundreds of affected individuals. We conclude that regional genetic variation across the UK and Ireland should be considered in the design of genetic studies, and may inform effective genetic screening and counselling.

genetics↗

bioRxiv2022

Strand-resolved mutagenicity of DNA damage and repair

DNA base damage is a major source of oncogenic mutations1. Such damage can produce strand-phased mutation patterns and multiallelic variation through the process of lesion segregation2. Here, we exploited these properties to reveal how strand-asymmetric processes, such as replication and transcription, shape DNA damage and repair. Despite distinct mechanisms of leading and lagging strand replication3,4, we observe identical fidelity and damage tolerance for both strands. For small DNA adducts, our results support a model in which the same translesion polymerase is recruited on-the-fly to both replication strands, starkly contrasting the strand asymmetric tolerance of bulky adducts5. We find that DNA damage tolerance is also common during transcription, where RNA-polymerases frequently bypass lesions without triggering repair. At multiple genomic scales, we show the pattern of DNA damage induced mutations is largely shaped by the influence of DNA accessibility on repair efficiency, rather than gradients of DNA damage. Finally, we reveal specific genomic conditions that can corrupt the fidelity of nucleotide excision repair and actively drive oncogenic mutagenesis. These results provide insight into how strand-asymmetric mechanisms underlie the formation, tolerance, and repair of DNA damage, thereby shaping cancer genome evolution.

genomics↗

bioRxiv2021

Mutational bias in spermatogonia impacts the anatomy of regulatory sites in the human genome

Mutation in the germline is the ultimate source of genetic variation, but little is known about the influence of germline chromatin structure on mutational processes. Using ATAC-seq, we profile the open chromatin landscape of human spermatogonia, the most proliferative cell-type of the germline, identifying transcription factor binding sites (TFBSs) and PRDM9-binding sites, a subset of which will initiate meiotic recombination. We observe an increase in rare structural variant (SV) breakpoints at PRDM9-bound sites, implicating meiotic recombination in the generation of structural variation. Many germline TFBSs, such as NRF, are also associated with increased rates of SV breakpoints, apparently independent of recombination. Singleton short insertions (>=5 bp) are highly enriched at TFBSs, particularly at sites bound by testis active TFs, and their rates correlate with those of structural variant breakpoints. Short insertions often duplicate the TFBS motif, leading to clustering of motif sites near regulatory regions in this male-driven evolutionary process. Increased mutation loads at germline TFBSs disproportionately affect neural enhancers with activity in spermatogonia, potentially altering neurodevelopmental regulatory architecture. Local chromatin structure in spermatogonia is thus pervasive in shaping both evolution and disease.

genomics↗

bioRxiv2020

Structural variants at the BRCA1/2 loci are a common source of homologous repair deficiency in high grade serous ovarian carcinoma

Around half of high grade serous ovarian carcinomas (HGSOC) show homologous recombination repair deficiency (HRD), often caused by germline or somatic single nucleotide variant (SNV) mutations or small indels disrupting BRCA1/2. We have uniformly processed the largest collection of whole genome sequencing (WGS) data from HGSOC samples to date (N=205), comprehensively characterising the somatic mutational landscape, and expression at the BRCA1/2 loci. We discover that large structural variants (SV) are a frequent but unappreciated source of BRCA1/2 disruption in these tumours. Somatic structural variation at these loci is dominated by multi-megabase deletions that span the entirety of BRCA1 (median = 4.9Mb) or BRCA2 (median = 6.2Mb), independently affecting a substantial proportion of patients (16%) in addition to those affected by damaging germline or somatic short variants, within the BRCA1/2 coding sequences (24%). In common with previous studies, we show that the presence of damaging somatic SNVs or short indels in BRCA1 (OR=10, 95% CI 1.8-103, p=0.002, adj p=0.027 and BRCA2 (OR=17, 95% CI 2.1-816), p=0.002, adj.p=0.021) was found to influence HRD. For the first time we also study the compound effect of SV and SNV or short indel mutations at both loci, demonstrating that SVs often contribute to compound deficiencies involving SNVs or indels, with large somatic deletions contributing to these compound deficiencies in 15/205 (7%) of samples. Notably the strongest risk of HRD (OR=19 (2.4-896), p=6.6x10-3, adj P=8.5x10-3) is generated by combined large deletions at BRCA1 and BRCA2 in the absence of SNVs or indels, affecting 3% of patients. Overall, we show that HRD is a complex phenotype in HGSOC tumours, affected by the patterns of shorter variants such as SNVs and indels, SVs, methylation and expression seen at multiple loci, and we construct a successful (ROC AUC = 0.75) predictive model of HRD using such variables. In addition, HRD impacts patient survival when conferred by mechanisms other than through the well-understood short variants at BRCA1/2, currently exploited in the clinic. These results alter our understanding of the mutational landscape at the BRCA1/2 loci in highly rearranged tumours, and increase the number of patients predicted to benefit from therapies exploiting HRD in tumours such as PARP inhibition.

cancer biology↗

bioRxiv2019

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow through clonal expansion1, 2. Here we reveal that most mutagenic DNA lesions are not resolved as mutations within a single cell-cycle. Instead, DNA lesions segregate unrepaired into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterise this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can generate multiple alternative alleles in successive cell divisions, thereby increasing both multi-allelic and combinatorial genetic diversity. The phasing of lesions enables the accurate measurement of strand biased repair processes, the quantification of oncogenic selection, and the fine mapping of sister chromatid exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

cancer biology↗

bioRxiv2019

Increased ultra-rare variant load in an isolated Scottish population impacts exonic and regulatory regions

Human population isolates provide a snapshot of the impact of historical demographic processes on population genetics. Such data facilitate studies of the functional impact of rare sequence variants on biomedical phenotypes, as strong genetic drift can result in higher frequencies of variants that are otherwise rare. We present the first whole genome sequencing (WGS) study of the VIKING cohort, a representative collection of samples from the isolated Shetland population in northern Scotland, and explore how its genetic characteristics compare to a mainland Scottish population. Our analyses reveal the strong contributions played by the founder effect and genetic drift in shaping genomic variation in the VIKING cohort. About one tenth of all high-quality variants discovered are unique to the VIKING cohort or are seen at frequencies at least ten fold higher than in more cosmopolitan control populations. Multiple lines of evidence also suggest relaxation of purifying selection during the evolutionary history of the Shetland isolate. We demonstrate enrichment of ultra-rare VIKING variants in exonic regions and for the first time we also show that ultra-rare variants are enriched within regulatory regions, particularly promoters, suggesting that gene expression patterns may diverge relatively rapidly in human isolates.\n\nAuthor SummaryPopulation isolates provide a valuable window on the roles of rare genetic variation in human phenotypes, as a result of their unusual evolutionary histories, that often lead to relatively high frequencies of variants that are exceptionally rare elsewhere. Such populations show increased levels of background relatedness among individuals and are often subject to stronger genetic drift, leading to a higher frequency of deleterious variants. Here, for the first time, we present whole genome sequencing data from the Shetland population in Northern Scotland, encompassing 500 individuals, and compare these genomes to the mainland Scottish population. As expected we find the imprint of Shetland population history in the Shetland genome, with strong evidence for founder effects and genetic drift, but we also discover a relaxation of selective constraint across the genome. These influences have combined to endow the Shetland genome with thousands of ultra-rare genetic variants, not observed previously in other populations. Surprisingly these variants are significantly enriched in functional regions including protein coding regions of genes and regulatory elements. Among regulatory regions, promoters are particularly enriched for ultra-rare variants, suggesting the potential for rapid divergence of gene expression in isolates.

genomics↗

bioRxiv2017

Chromatin loop anchors are associated with genome instability in cancer and recombination hotspots in the germline.

Chromatin loops form a basic unit of interphase nuclear organisation, providing contacts between regulatory regions and target promoters, and forming higher level patterns defining self interacting domains. Recent studies have shown that mutations predicted to alter chromatin loops and domains are frequently observed in tumours and can result in the upregulation of oncogenes, but the combinations of selection and mutational bias underlying these observations remains unknown. Here, we explore the unusual mutational landscape associated with chromatin loop anchor points (LAPs), which are located at the base of chromatin loops and form a kinetic trap for cohesin. We show that LAPs are strongly depleted for single nucleotide variants (SNVs) in tumours, which is consistent with their relatively early replication timing. However, despite low SNV rates, LAPs emerge as sites of evolutionary innovation showing enrichment for structural variants (SVs). They harbour an excess of SV breakpoints in cancers, are prone to double strand breaks in somatic cells, and are bound by DNA repair complex proteins. Recurrently disrupted LAPs are often associated with genes annotated with functions in cell cycle transitions. An unexpectedly large fraction of LAPs (16%) also overlap known meiotic recombination hotspot (HSs), and are enriched for the core PRDM9 binding motif, suggesting that LAPs have been foci for diversity generated during recent human evolution. We suggest that the unusual chromatin structure at LAPs underlies the elevated SV rates observed, marking LAPs as sites of regulatory importance but also genomic fragility.

genomics↗