Biology subjects

Lesluyes, T.

Publications and source records attributed to Lesluyes, T..

12 recordsLinked to original sources

bioRxiv2026

Chromosomal instability shapes spatial and temporal phenotypic diversity in a malignant peripheral nerve sheath tumour

Understanding the spatial and temporal dynamics of tumour evolution is crucial for determining the drivers of cancer progression. Linking genotype to phenotype across a tumour remains challenging, however. Here, we use single-cell and spatial multi-omics to comprehensively profile a primary malignant peripheral nerve sheath tumour (MPNST) and its multifocal recurrence. Combining the native barcoding system from extensive heterogeneity in copy number alterations with mutation data, we resolve the evolutionary tree of this tumour, revealing a branching structure suggestive of ongoing chromosomal instability. We show that gene dosage effects contribute to phenotypic diversity and scale predominantly linearly with copy number. Using spatial genomics assisted by laser capture microdissection and spatial transcriptomics, we performed in situ lineage tracing in this human tumour, elucidating the relationship between local expansions and interactions between tumour cells and the microenvironment. These findings demonstrate the potential of combined bulk, single-cell and spatial techniques to dissect cancer evolution in time and space and link genotype to phenotype in detail.

genomics↗

bioRxiv2025

CLONAL SELECTION SUPPORTED BY SINGLE CELL DNA SEQUENCING REVEALS HORMONAL ADAPTATION AND RESISTANCE IN LOCALLY ADVANCED BREAST CANCER DURING NEOADJUVANT AROMATASE INHIBITION

BackgroundThe aromatase inhibitors (AI) letrozole and exemestane, are often used in sequence in targeting ER+ breast cancers. However resistance to AI poses a major barrier to sustained clinical benefit, while the biological mechanisms underlying the phenomenon remain largely unknown. In this study, we build on our clinical NeoLetExe trial, with the aim to investigate the molecular basis of resistance to AI, by analysing subclonal evolutionary dynamics during sequential treatment. MethodsWe use whole-exome sequencing (WES) data from 11 ER+ breast cancer patients and 3 timepoints of the Neoletexe trial to reconstruct cancer cell fraction-based subclonal composition. Single-cell DNA sequencing from matched tumour samples is used for validating identified clones and variants. Subclonal variants were annotated to genes by integrating evidence from public data and ExpectoSc. Pathway enrichment analysis using Human Base was conducted. ResultsHigher cancer cell fraction clone trajectories were significantly associated with reduced treatment response (p = 0.023). Clones reconstructed by WES were validated at 81% using single-cell DNA sequencing. Clones resistant to both letrozole and exemestane demonstrated PIK3CA/AKT/mTOR signaling activation, KRAS pathway dysregulation, hedgehog signaling, and androgen receptor pathways, alongside extensive immune activation and metabolic reprogramming. Drug-specific resistance patterns showed exemestane-resistant clones enriched for epigenetic control and miRNA-mediated silencing, while letrozole-resistant clones displayed metabolic dysregulation but notably lacked immune pathway activation. In contrast, treatment-sensitive clones maintained coordinated cell cycle control, preserved DNA damage responses, and retained immune signaling capacity. Analysis of FDA-approved breast cancer targets identified actionable alterations in PIK3CA (4 patients) and AKT1 (1 patient) that persisted through AI treatment, with RNA expression analysis revealing 48 additional therapeutic targets spanning PI3K/AKT/mTOR, CDK4/6, DNA repair (BRCA1/2, ATM), and immune checkpoint pathways. ConclusionWES-based cancer cell fraction analysis successfully captured subclonal evolutionary trajectories during AI treatment, revealing drug-specific mechanisms and identifying key molecular players in endocrine therapy resistance. This work establishes a framework for precision oncology approaches by providing actionable therapeutic targets and advancing our understanding of resistance mechanisms to improve clinical outcomes in sequential AI therapy.

cancer biology↗

bioRxiv2025

An optimised computational approach for the identification of somatic structural variants in cancer

Structural variants play a critical role in tumorigenesis. At present, these events are most commonly identified using short-read whole-genome sequencing data, and a number of computational tools are available for this purpose. Consensus approaches have been used to improve precision, but may reduce sensitivity. The optimal number and combination of callers remains unclear, in part due to the lack of gold standard real-world datasets for validation. Here, we benchmark the performance of Delly, GRIDSS, LUMPY, Manta and SvABA, using a validation set of consensus calls from the Pan-Cancer Analysis of Whole Genomes Consortium. Manta showed the best standalone performance, identifying 88% of the validation set calls, and was included in all of the best-performing caller combinations. A consensus approach comprising Delly, GRIDSS, Manta and SvABA was selected as the optimum approach from those tested. We provide a NextFlow implementation of our optimised consensus approach as a resource for the cancer genomics community.

genomics↗

bioRxiv2025

Forecasting oncogene amplification and tumour suppressor deletion

Oncogene amplification and tumour suppressor deletion can drive tumour initiation, progression and treatment resistance. Detection at diagnosis often signals poor prognosis, but it can also enable opportunities for treatment with highly effective targeted therapies. Predicting the likelihood that a patient will acquire these driver alterations in the future using a genomic test represents an opportunity to realise the benefits of interventions earlier, potentially with preventative intent. Here, we present a forecasting framework that takes as input a DNA copy number profile and predicts whether the tumour will acquire an oncogene amplification or tumour suppressor deletion in the future. This framework leverages mutation rate estimates from the input tumour, alongside gene-specific selection coefficients derived from a large cohort of 7,880 tumours. We demonstrate feasibility using 7,042 single-time-point samples and longitudinally collected tumour pairs from 44 prostate and 100 lung cancers, identifying tumours that went on to acquire amplifications at a later time point with an average AUC of 0.87. We show potential clinical utility by forecasting poor prognosis in low-grade gliomas via CDK4/PDGFRA amplification or CDKN2A deletion, and osimertinib resistance in lung cancers via MET amplification. This study serves as a proof-of-concept for a new class of biomarker, wherein selective pressures and mutation-generating processes can be harnessed to anticipate future genomic alterations.

genomics↗

bioRxiv2024

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Intra-tumor heterogeneity is characterized by a diverse population of tumor clones and subclones which are important drivers of tumor evolution and therapeutic response. However, accurate subclonal reconstruction at scale remains challenging. We developed a machine learning tool, CliPP, and surveyed 9,972 tumors from 32 cancer types. We found that high subclonal mutation load (sML), the fraction of subclonal single nucleotide variants (SNVs) to all SNVs in the coding region, was prognostic of survival (progression free survival or overall survival) in 18 cancer types. In 14 cancers with low to moderate tumor mutation burden (TMB), high sML was associated with better prognosis. In immunotherapy trials for 42 metastatic prostate cancer (mCRPC), high sML was predictive of favorable response to ipilimumab and associated with increased CD8+ T-cell infiltration and decreased macrophage population. A validation using 613 whole-genomes of esophageal adenocarcinoma confirms the favorable effect of high sML and the observed tumor-associated macrophage. Our study identifies sML as a key feature of cancer, suggesting a biphasic relationship between evolutionary dynamics and differential immune environments. Finally, sML may serve as an orthogonal approach to identify likely responders of immune checkpoint blockade in low to moderate TMB tumors.

genomics↗

bioRxiv2023

Reconstructing oral cavity tumor evolution through brush biopsy

Oral premalignant lesions (OPLs) with genomic alterations have a heightened risk of evolving into oral squamous cell carcinoma (OSCC). Currently, genomic data are obtained through invasive tissue biopsy. Brush biopsy has been utilized for diagnosing dysplasia but its effectiveness in reflecting the complete genomic landscape of OPLs remains uncertain. This study investigates the potential of brush biopsy samples in accurately reconstructing the genomic profile of OPLs. We analyzed single nucleotide variants (SNVs), copy number aberrations (CNAs), and subclonal structures in paired tissue and brush biopsy samples from a patient with both OPL and OSCC lesions. The results showed that brush biopsy can effectively reflect about 90% of SNVs and similar CNA profiles as those found in tissue biopsies. It was specific, as normal oral epithelium didnt share these genomic alterations. Interestingly, brush biopsy revealed shared SNVs and CNAs between the distinct OPL and OSCC lesions, indicating a common ancestral origin. Subclonal reconstruction confirmed this shared ancestry, followed by divergent evolution of the lesions. These findings highlight the potential of brush biopsies in accurately representing the genomic profile of OPL and OSCC, proving useful in understanding tumor evolution.

genomics↗

bioRxiv2023

The history of chromosomal instability in genome doubled tumors

Tumors frequently display high chromosomal instability (CIN) and contain multiple copies of genomic regions. Here, we describe GRITIC, a generic method for timing genomic gains leading to complex copy number states, using single-sample bulk whole-genome sequencing data. By applying GRITIC to 5,656 tumors, we found that non-parsimonious evolution is frequent in the formation of complex copy number states in genome-duplicated tumors. We measured CIN before and after genome duplication in human tumors and found that late genome doubling was followed by an increase in the rate of copy number gain. Copy number gains often accumulate as punctuated bursts, commonly after genome duplication. We infer that genome duplications typically affect the selection landscape of copy number losses, while only minimally impacting copy number gains. In summary, GRITIC is a novel copy number gain timing framework that permits the analysis of copy number evolution in chromosomally unstable tumors. Statement of significanceComplex genomic gains are associated with whole-genome duplications, which are frequent across tumors, span a large fraction of their genomes, and are linked to poorer outcomes. GRITIC infers when these gains occur during tumor development, which will help to identify the genetic events that drive tumor evolution.

genomics↗

bioRxiv2023

Evolution of chromosome arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize the evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. TCGA data analysis showed recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a unique panel of 23 primary tumor/patient-derived xenograft basal breast cancers revealed early evolution of chr4p deletion. Mechanistically we show that Chr4p loss is associated with enhanced proliferation. Gene function studies identified an unknown gene, C4orf19, within chr4p, which suppressed proliferation when overexpressed and is a novel member of a PDCD10-GCKIII kinase module, we name as PGCA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames, identified chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner implicating network interactions. Together this sheds light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

genomics↗

bioRxiv2023

Spatiotemporal Genomic Profiling of Intestinal Metaplasia Reveals Clonal Dynamics of Gastric Cancer Progression

Intestinal metaplasia (IM) is a pre-malignant condition of the gastric mucosa associated with increased gastric cancer (GC) risk. We analyzed 1256 gastric samples (1152 IMs) from 692 subjects through a prospective 10-year study. We identified 26 IM driver genes in diverse pathways including chromatin regulation (ARID1A) and intestinal homeostasis (SOX9), largely occurring as small clonal events. Analysis of clonal dynamics between and within subjects, and also longitudinally across time, revealed that IM clones are likely transient but increase in size upon progression to dysplasia, with eventual transmission of somatic events to paired GCs. Single-cell and spatial profiling highlighted changes in tissue ecology and lineage heterogeneity in IM, including an intestinal stem-cell dominant cellular compartment linked to early malignancy. Expanded transcriptome profiling revealed expression-based molecular subtypes of IM, including a body-resident "pseudoantralized" subtype associated with incomplete histology, antral/intestinal cell types, ARID1A mutations, inflammation, and microbial communities normally associated with the healthy oral tract. We demonstrate that combined clinical- genomic models outperform clinical-only models in predicting IMs likely to progress. Our results raise opportunities for GC precision prevention and interception by highlighting strategies for accurately identifying IM patients at high GC risk and a role for microbial dysbiosis in IM progression.

cancer biology↗

bioRxiv2021

Signatures of copy number alterations in human cancer

The gains and losses of DNA that emerge as a consequence of mitotic errors and chromosomal instability are prevalent in cancer. These copy number alterations contribute to cancer initiaition, progression and therapeutic resistance. Here, we present a conceptual framework for examining the patterns of copy number alterations in human cancer using whole-genome sequencing, whole-exome sequencing, and SNP6 microarray data making it widely applicable to diverse datasets. Deploying this framework to 9,873 cancers representing 33 human cancer types from the TCGA project revealed a set of 19 copy number signatures that explain the copy number patterns of 93% of TCGA samples. 15 copy number signatures were attributed to biological processes of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, and chromothripsis. The aetiology of four copy number signatures are unexplained and some cancer types have unique patterns of amplicon signatures associated with extrachromosomal DNA, disease-specific survival, and gains of proto-oncogenes such as MDM2. In contrast to base-scale mutational signatures, no copy number signature associated with known cancer risk factors. The results provide a foundation for exploring patterns of copy number changes in cancer genomes and synthesise the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes giving rise to copy number changes.

cancer biology↗

bioRxiv2020

ATRX alteration contributes to tumor growth and immune escape in pleomorphic sarcomas

Whole genome and transcriptome sequencing of a cohort of 67 leiomyosarcomas revealed ATRX to be one of the most frequently mutated genes in leiomyosarcomas after TP53 and RB1. While its function is well described in the alternative lengthening of telomeres mechanism, we wondered whether its alteration could have complementary effects on sarcoma oncogenesis. ATRX alteration is associated with the down-expression of genes linked to differentiation in leiomyosarcomas, and to immunity in an additional cohort of 60 poorly differentiated sarcomas. In vitro and in vivo models showed that ATRX loss increases tumor growth rate and immune escape by decreasing the immunity load of active mast cells in sarcoma tumors. These data indicate that an alternative to unsuccessful targeting of the adaptive immune system in sarcoma could be to target the innate system. This might lead to a better outcome for sarcoma patients in terms of ATRX status.

cancer biology↗

bioRxiv2020

An Integrative Analysis of the Age-Associated Genomic, Transcriptomic and Epigenetic Landscape across Cancers

Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterised genomic, transcriptomic and epigenetic alterations in relation to patients age across cancer types. We showed that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations were identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences were found in gliomas and endometrial cancer. We identified age-related global transcriptomic changes and demonstrated that these genes are controlled by age-associated DNA methylation changes. This study provides a comprehensive view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

cancer biology↗