Biology subjects

Cuppen, E.

Publications and source records attributed to Cuppen, E..

At least 19 recordsLinked to original sources

bioRxiv2026

The pan-cancer whole-genome landscape of Adolescent and Young Adult solid cancers

Cancer incidence in adolescents and young adults (AYAs; 18-39 years) is rising, but how their tumors differ genomically from those of older adults (OAs) is unclear. We analyzed paired tumor-normal whole-genomes from 698 AYAs and 5,220 OAs, with subcohorts matched by cancer group to disentangle age from tumor type. AYA tumors carried fewer somatic variants yet largely shared the same genetic drivers; many apparent age differences reflected age-dependent cancer subtypes, such as IDH-mutant glioma and HPV-positive cervical cancer. Colibactin, APOBEC and homologous recombination deficiency-associated signatures were more prevalent in AYA colorectal, gynecological and breast cancers, respectively, but were not more mutagenic when present, suggesting earlier-life activity rather than AYA-specific mutagenesis. Tumor type-concordant pathogenic germline variants were enriched in AYAs (10.9% overall versus 6.6% in matched OAs; p=0.006), often with a somatic second hit. Age thus shaped AYA cancer genomes through mutational processes, subtype composition and germline predisposition, supporting comprehensive tumor-normal profiling beyond driver-focused testing.

cancer biology↗

bioRxiv2025

Systematic identification of genomic non-response biomarkers to cancer therapies

The costs of cancer therapies are rising rapidly across the globe, with novel therapies like targeted treatment and immunotherapies as major contributors, but their effectiveness can be low or uncertain due to limited post market surveillance. Reliable biomarkers to identify patients highly unlikely to respond to cancer therapies represent an increasingly important clinical and societal need, as they could prevent unnecessary treatments, reduce side effects, and alleviate pressure on healthcare systems. Here, we developed a robust statistical framework and applied it to whole-genome and transcriptome sequencing data of cancer patients (n = 2,596) with advanced disease. Our approach systematically identified known and potentially novel genomic and transcriptomic biomarkers of non-response, such as immune evasion driver events in skin melanoma patients treated with anti-PD-1 checkpoint inhibitors, and KRASG12 mutations in metastatic colorectal cancer patients treated with different chemotherapy regimens. Despite the identification of these promising non-response signals, an analytical power analysis revealed that for most treatments and/or cancer types the cohort sizes are underpowered. Our results underscore the promises and the urgent need for expanding response-annotated real-world comprehensive genomics datasets to enable robust biomarker identification and validation.

genomics↗

bioRxiv2023

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

bioRxiv2023

EXO1-mediated DNA repair by single-strand annealing is essential for BRCA1-deficient cells

Deficiency for the repair of DNA double-strand breaks (DSBs) via homologous recombination (HR) leads to chromosomal instability and diseases such as cancer. Yet, defective HR also results in vulnerabilities that can be exploited for targeted therapy. Here, we identify such a vulnerability and show that BRCA1-deficient cells are dependent on the long-range end-resection factor EXO1 for survival. EXO1 loss results in DNA replication-induced lesions decorated by poly(ADP-ribose)-chains. In cells that lack both BRCA1 and EXO1, this is accompanied by unresolved DSBs due to impaired single-strand annealing (SSA), a DSB repair process that requires the activity of both proteins. In contrast, BRCA2-deficient cells have increased SSA, also in the absence of EXO1, and hence are not dependent on EXO1 for survival. In agreement with our mechanistic data, BRCA1-mutated tumours have elevated EXO1 expression and contain more genomic signatures of SSA compared to BRCA1-proficient tumours. Collectively, our data indicate that EXO1 is a promising novel target for treatment of BRCA1-deficient tumours.

molecular biology↗

bioRxiv2022

The genome-wide mutational consequences of DNA hypomethylation

DNA methylation is important for establishing and maintaining cell identity and for genomic stability. This is achieved by regulating the accessibility of regulatory and transcriptional elements and the compaction of subtelomeric, centromeric, and other inactive genomic regions. Carcinogenesis is accompanied by a global loss in DNA methylation, which facilitates the transformation of cells. Cancer hypomethylation may also cause genomic instability, for example through interference with the protective function of telomeres and centromeres. However, understanding the role(s) of hypomethylation in tumor evolution is incomplete because the precise mutational consequences of global hypomethylation have thus far not been systematically assessed. Here we made genome-wide inventories of all possible genetic variation that accumulates in single cells upon the long-term global hypomethylation by CRISPR/CAS9-mediated conditional knockdown of DNMT1. Depletion of DNMT1 results in a genomewide reduction in DNA methylation levels. Hypomethylated cells show reduced proliferation rates, reactivation of the inactive X-chromosome and abnormal nuclear morphologies. Prolonged hypomethylation is accompanied by increased chromosomal instability. However, there is no increase in mutational burden, enrichment for certain mutational signatures or structural changes to the genome. We conclude that the primary consequence of global hypomethylation is chromosomal instability and does not necessarily lead to other small-scale or structural mutational effects.

genomics↗

bioRxiv2022

Pan-cancer whole genome comparison of primary and metastatic solid tumors

Metastatic cancer remains almost inevitably a lethal disease. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here, we characterize the genomic differences between early-stage untreated primary tumors and late-stage treated metastatic tumors using a harmonized pan-cancer (re-)analysis of 7,152 whole-genome-sequenced tumors. In general, our analysis shows that metastatic tumors have a low intra-tumor heterogeneity, high genomic instability and increased frequency of structural variants with comparatively a modest increase in the number of small genetic variants. However, these differences are cancer type specific and are heavily impacted by the exposure to cancer therapies. Five cancer types, namely breast, prostate, thyroid, kidney clear carcinoma and pancreatic neuroendocrine, are a clear exception to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. These changes were supported by increased genomic instability and involved substantial differences in tumor mutation burden, clock-based molecular signatures and the landscape of driver alterations as well as a pervasive increase in structural variant burden. The majority of cancer types had either moderate genomic differences (e.g., cervical and colorectal cancers) or highly consistent genomic portraits (e.g., ovarian cancer and skin melanoma) when comparing early- and late-stage disease. Exposure to treatment further scars the tumor genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcases the potential of whole-genome analysis to understand tumor evolution and provides a valuable resource to further investigate the biological basis of cancer and resistance to cancer therapies.

genomics↗

bioRxiv2022

Genomic and transcriptomic landscape of advanced renal cell cancer to individualize treatment strategy

BackgroundDifferences in the clinical course and treatment responses in individual patients with advanced renal cell carcinoma (RCC) can largely be explained by the different genomics of this disease. To improve the personalized treatment strategy and survival outcomes for patients with advanced RCC, the genomic make-up in patients with advanced RCC was investigated to identify putative actionable mutations and signatures. MethodsIn this prospective multicenter study (NCT01855477), whole-genome sequencing (WGS) data of locally advanced and metastatic tissue biopsies and matched whole-blood samples were collected from 91 patients with histopathologically confirmed RCC. WGS data were analyzed for small somatic variants, copy-number alterations and structural variants. For a subgroup of patients, RNA sequencing (RNA-Seq) data could be analyzed. RNA-Seq data were clustered on immunogenic and angiogenic gene expression patterns according to a previously developed angio-immunogenic gene signature. ResultsFor papillary and clear cell RCC, putative actionable drug targets were detected by WGS in 100% of the patients. RNA-Seq data of clear cell and papillary RCC were clustered using a previously developed angio-immunogenic gene signature. Analyses of driver mutations and RNA-Seq data revealed clear differences among different RCC subtypes, showing the added value of WGS and RNA-Seq over clinicopathological data. ConclusionsBy improving both histological subtyping and the selection of treatment according to actionable targets and immune signatures, WGS and RNA-Seq may improve therapeutic decision making for most patients with advanced RCC, including patients with non-clear cell RCC for whom no standard treatment is available to data. Prospective clinical trials are needed to evaluate the impact of genomic and transcriptomic diagnostics on survival outcome for advanced RCC patients.

cancer biology↗

bioRxiv2022

Genetic immune escape landscape in primary and metastatic cancer

Immune surveillance escape is a hallmark of tumorigenesis1. Multiple studies have characterized the immune escape landscape across several untreated early-stage primary cancer types2-4. However, whether late-stage treated metastatic tumors present differences in genetic immune escape (GIE) prevalence and dynamics remains unclear. Here, we performed a pan-cancer characterization of GIE prevalence across six immune escape pathways in 6,457 uniformly processed Whole Genome Sequencing (WGS) tumor samples including 58 cancer types from 1,943 primary untreated patients and 4,514 metastatic patients. To effectively address the complexity of the Human Leukocyte Antigen (HLA-I) locus and to characterize its tumor status, we developed LILAC, an open-source integrative framework. We demonstrate that one in four tumors harbor GIE alterations, with high mechanistic and frequency variability across cancer types. GIE prevalence is highly consistent between primary and metastatic tumors for most cancer types with few exceptions such as prostate and thyroid carcinomas that have increased immune evasion frequencies in metastatic tumors. Positive selection analysis revealed that GIE alterations are frequently selected for in tumor evolution and that focal LOH of HLA-I, unlike non-focal LOH of HLA-I, tends to lose the HLA allele that presents the largest neoepitope repertoire. We also unraveled tumor genomic features contributing to immune escape incidence, including DNA repair deficiency, APOBEC activity, tobacco associated mutation load and viral DNA integration. Finally, there is a strong tendency for mid and high tumor mutation burden (TMB) tumors to preferentially select LOH of HLA-I for GIE whereas hypermutated samples favor global immune evasion strategies. Our results indicate that genetic immune escape is generally a pre-metastatic event during tumor evolution and that tumors adapt different strategies depending on their neoepitope burden.

genomics↗

bioRxiv2021

Recurrent activating mutations in AHR act as drivers of urinary tract cancer

Bladder cancer has a high recurrence rate and low survival of advanced stage patients. Few genetic drivers of bladder cancer have thus far been identified. We performed in-depth structural variant analysis on whole-genome sequencing data of 206 metastasized urinary tract cancers. In ~10% of the patients, we identified recurrent in-frame deletions of exons 8 and 9 in the aryl hydrocarbon receptor gene (AHR{Delta}e8-9), which codes for a ligand-activated transcription factor. Pan-cancer analyses show that AHR{Delta}e8-9 is highly specific to urinary tract cancer and mutually exclusive with other bladder cancer drivers. In AHR{Delta}e8-9 the ligand-binding domain is disrupted and we show that this results in ligand-independent AHR-pathway activation. In bladder organoids, AHR{Delta}e8-9 induces a transformed phenotype that is characterized by upregulation of AHR target genes, downregulation of differentiation markers and upregulation of genes associated with stemness and urothelial cancer. Furthermore, AHR{Delta}e8-9 expression results in anchorage independent growth of bladder organoids, indicating tumorigenic potential. DNA-binding deficient AHR{Delta}e8-9 fails to induce transformation, suggesting a role for AHR target genes in the acquisition of the oncogenic phenotype. In conclusion, we show that AHR{Delta}e8-9 is a novel driver of urinary tract cancer and that the AHR pathway could be an interesting therapeutic target.

cancer biology↗

bioRxiv2021

MutationalPatterns: The one stop shop for the analysis of mutational processes

BackgroundThe collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. ResultsHere, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. ConclusionsThis novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

genomics↗

bioRxiv2021

Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

Tumor tissue of origin (TOO) is an important factor for guiding treatment decisions. However, TOO cannot be determined for ~3% of metastatic cancer patients and are categorized as cancers of unknown primary (CUP). As whole genome sequencing (WGS) of tumors is now transitioning from the research domain to diagnostic practice in order to address the increasing demand for biomarker detection, its use for detection of TOO in routine diagnostics also starts becoming within reach. While proof of concept for the use of genome-wide features has been demonstrated before, more complex WGS mutation features, including structural variant (SV) driver and passenger events, have never been integrated into TOO-classifiers even though they bear highly characteristic links with tumor TOO. Using a uniformly processed dataset containing 6820 whole-genome sequenced primary and metastatic tumors, we have developed Cancer of Unknown Primary Location Resolver (CUPLR), a random forest based TOO classifier that employs 502 features based on simple and complex somatic driver and passenger mutations. Our model is able to distinguish 33 cancer (sub)types with an overall accuracy of 91% and 89% based on cross-validation (n=6139) and hold out set (n=681) predictions respectively. We found that SV derived features increase the accuracy and utility of TOO classification for specific cancer types. To ensure that predictions are human-interpretable and suited for use in routine diagnostics, CUPLR reports the top contributing features and their values compared to cohort averages. The comprehensive output of CUPLR is complementary to existing histopathological procedures and may thus improve diagnostics for patients with CUP.

genomics↗

bioRxiv2020

Unscrambling cancer genomes via integrated analysis of structural variation and copy number

Complex somatic genomic rearrangement and copy number alterations (CNA) are hallmarks of nearly all cancers. Whilst whole genome sequencing (WGS) in principle allows comprehensive profiling of these events, biological and clinical interpretation remains challenging. We have developed LINX, a novel algorithm which allows interpretation of short-read paired-end WGS derived structural variant and CNA data by clustering raw structural variant calls into distinct events, predicting their impact on the local structure of the derivative chromosome, and annotating their functional impact on affected genes. Novel visualisations facilitate further investigation of complex genomic rearrangements. We show that LINX provides insights into a diverse range of structural variation events including single and double break-junction events, mobile element insertions, complex shattering and high amplification events. We demonstrate that LINX can reliably detect a wide range of pathogenic rearrangements including gene fusions, immunoglobulin enhancer rearrangements, intragenic deletions and duplications. Uniquely, LINX also predicts chained fusions which we demonstrate account for 13% of clinically relevant oncogenic fusions. LINX also reports a class of inactivation events we term homozygous disruptions which may be a driver mutation in up to 8.8% of tumors including frequently affecting PTEN, TP53 and RB1, and are likely missed by many standard WGS analysis pipelines.

cancer biology↗

bioRxiv2020

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality gold standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines for comprehensive somatic SV detection. Here, we approached this challenge by genome-wide somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different technologies: Illumina HiSeq, Oxford Nanopore, Pacific Biosciences and 10x Genomics. Based on the evidence from multiple technologies combined with extensive experimental validation, including Bionano optical mapping data and targeted detection of candidate breakpoint junctions, we compiled a comprehensive set of true somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance of each technology as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects and data analysis tool evaluation. The reference truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

genomics↗

bioRxiv2020

SARS-CoV-2 Productively Infects Human Gut Enterocytes

COVID-19, caused by SARS-CoV-2, is an influenza-like disease with a respiratory route of transmission, yet clinical evidence suggests that the intestine may present another viral target organ. Indeed, the SARS-CoV-2 receptor angiotensin converting enzyme 2 (ACE2) is highly expressed on differentiated enterocytes. In human small intestinal organoids, enterocytes were readily infected by SARS-CoV and SARS-CoV-2 as demonstrated by confocal- and electron-microscopy. Consequently, significant titers of infectious viral particles were measured. mRNA expression analysis revealed strong induction of a generic viral response program. We conclude that intestinal epithelium supports SARS-CoV-2 replication. One Sentence SummarySARS-CoV-2 infection of enterocytes in human small intestinal organoids

cell biology↗

bioRxiv2020

Pan-cancer landscape of homologous recombination deficiency

Homologous recombination deficiency (HRD) results in impaired double strand break repair and is a frequent driver of tumorigenesis. Here, we developed a genome-wide mutational scar-based pan-cancer Classifier of HOmologous Recombination Deficiency (CHORD) that can discriminate BRCA1- and BRCA2-subtypes. Analysis of a metastatic (n=3,504) and primary (n=1,854) pan-cancer cohort revealed HRD was most frequent in ovarian and breast cancer, followed by pancreatic and prostate cancer. Biallelic inactivation of BRCA1, BRCA2, RAD51C or PALB2 was the most common genetic cause of HRD, with RAD51C and PALB2 inactivation resulting in BRCA2-type HRD. While the specific genetic cause of HRD was cancer type specific, biallelic inactivation was predominantly associated with loss-of-heterozygosity (LOH), with increased contribution of deep deletions in prostate cancer. Our results demonstrate the value of pan-cancer genomics-based HRD testing and its potential diagnostic value for patient stratification towards treatment with e.g. poly ADP-ribose polymerase inhibitors (PARPi).

cancer biology↗

bioRxiv2019

GRIDSS, PURPLE, LINX: Unscrambling the tumor genome via integrated analysis of structural variation and copy number

We have developed a novel, integrated and comprehensive purity, ploidy, structural variant and copy number somatic analysis toolkit for whole genome sequencing data of paired tumor/normal samples. We show that the combination of using GRIDSS for somatic structural variant calling and PURPLE for somatic copy number alteration calling allows highly sensitive, precise and consistent copy number and structural variant determination, as well as providing novel insights for short structural variants and regions of complex local topology. LINX, an interpretation tool, leverages the integrated structural variant and copy number calling to cluster individual structural variants into higher order events and chains them together to predict local derivative chromosome structure. LINX classifies and extensively annotates genomic rearrangements including simple and reciprocal breaks, LINE, viral and pseudogene insertions, and complex events such as chromothripsis. LINX also comprehensively calls genic fusions including chained fusions. Finally, our toolkit provides novel visualisation methods providing insight into complex genomic rearrangements.

bioinformatics↗

bioRxiv2019

Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

BackgroundGenomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients with multiple congenital abnormalities and/or intellectual disability carrying de novo SVs are frequently unknown.\n\nResultsWe applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with de novo SVs and an inconclusive diagnosis after regular genetic testing. In seven of these cases (18%) whole genome sequencing analysis detected disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict effects on genes directly affected by SVs and on genes indirectly affected due to changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In eight cases evidence was found for involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to a collection of 382 patients with previously detected and classified de novo SVs and identified candidate driver genes in 210 cases (54%), including 32 cases whose SVs were previously not classified as pathogenic. Pathogenic positional effects were predicted in 25% of the cases with balanced SVs and in 8% of the cases with copy number variants.\n\nConclusionsThese results show that driver gene prioritization based on integrative analysis of WGS data with phenotype association and chromatin organization datasets can improve the molecular diagnosis of patients with de novo SVs.

genomics↗

bioRxiv2019

Mutational impact of chronic alcohol use on stem cells in cirrhotic liver

Excessive alcohol consumption increases the risk of developing liver cancer, but the mechanism through which alcohol drives carcinogenesis is as yet unknown. Here, we determined the mutational consequences of chronic alcohol use on the genome of human liver stem cells prior to cancer development. No change in base substitution rate or spectrum could be detected. Analysis of the trunk mutations in an alcohol-related liver tumor by multi-site whole-genome sequencing confirms the absence of specific alcohol-induced mutational signatures driving the development of liver cancer. However, we did identify an enrichment of nonsynonymous base substitutions in cancer genes in stem cells of the cirrhotic livers, such as recurrent nonsense mutations in PTPRK that disturb Epidermal Growth Factor (EGF)-signaling. Our results thus suggest that chronic alcohol use does not contribute to carcinogenesis through altered mutagenicity, but instead induces microenvironment changes which provide a fertile ground for selection of cells with oncogenic mutations.

cancer biology↗