Biology subjects

de Ridder, J.

Publications and source records attributed to de Ridder, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Decoding the Sequence Requirements for Translation Initiation

Accurate selection of start codons by ribosomes is a fundamental determinant of proteome composition. Although the Kozak sequence--an 8-nucleotide sequence flanking the start codon--has long been viewed as the primary determinant of initiation in eukaryotes, it fails to explain the large diversity of start codon usage across transcripts. Here we combine massively parallel reporter assays, bioinformatics, machine learning, single-molecule imaging and cryo-electron microscopy to define the extended translation initiation sequence (eTIS), an [~]80-nucleotide sequence surrounding the start codon that governs initiation efficiency. A deep-learning model trained on eTIS features accurately predicts translation initiation across transcripts. Unexpectedly, we find that the Kozak sequence is not optimal for initiation as is widely presumed, and we identify the origin of this discrepancy. eTIS nucleotides that promote efficient initiation are enriched in the human transcriptome and are evolutionarily conserved, underscoring their functional importance. Biophysical and structural analyses reveal that specific eTIS residues--including the key +6 position and residues in the mRNA entry and exit channel--engage ribosomal proteins, rRNA and initiation factors to promote start codon recognition by stabilizing the ribosome at the start codon and facilitating the structural transitions required for initiation. Finally, optimization of the eTIS markedly enhances translational fidelity and protein output from therapeutic mRNAs, highlighting its practical utility. Together, these findings redefine the sequence logic of translation initiation and establish a framework for precise control of protein expression.

molecular biology↗

bioRxiv2025

Development of a metabolic signature of post-weaning diarrhoea in pigs

In the agricultural sector, antibiotics have been used to improve swine growth performance. This application is banned nowadays, due to increased risk of antibiotic resistance. In piglets this results in a higher prevalence of post-weaning diarrhoea, deteriorating both animal health and performance. Our goal was to find a metabolite signature separating piglets with low faecal consistency score (FCS) from piglets with normal faecal consistency and determine which pathways were enriched in this signature. By using direct infusion mass spectrometry on blood spots, we built machine learning (ML) models that aimed to differentiate between low and normal FCS. To test the general predictive capability of these models, we applied a Leave-One-Country-Out (LOCO) strategy for cross validation. Our second approach after LOCO was finding the optimal number of features to include in a feature-reduced model. To determine the order in which features were to be eliminated, we ranked them based on a combination of t-test and fold-change significance scores. Enrichment analysis using mummichog was used to gain insights into the final signature set of m/z values found using this ranking and ML models. Models trained both using all countries and leaving out specific countries from training showed a limited ability to predict FCS category. Furthermore, the LOCO results were mixed, with some countries showing a predictive signal present in the data, but others with predictive capability that was no better than random. Signature analysis using t-test and fold-change results did not result in any KEGG pathways that were enriched in this signature as compared to random. Using these methods, we could predict the FCS category to a limited degree. Although no common signature for low faecal consistency could be determined using this method, given that some countries showed more reliable LOCO results, further analysis into specifically the samples from those countries could be a valuable next step. No metabolite signature enriched in changes to KEGG pathways was found in the data using the combined t-test and fold-change analysis ranking method. We have shared the data with the community through the MetaboLights repository.

bioinformatics↗

bioRxiv2024

Bacterial cell-free DNA profiling reveals co-elevation of multiple bacteria in newborn foals with suspected sepsis

BackgroundSepsis is the leading cause of death in newborn foals. This study investigates whether cell-free DNA (cfDNA) sequencing can enhance bacterial pathogen detection in foals with suspected sepsis and addresses existing knowledge gaps and diagnostic challenges. MethodsWe developed a foal cfDNA sequencing for bacteria identification (cfFBI) workflow, integrating wetlab and computational protocols to detect increased bacterial cfDNA abundance in blood. Specifically, cfFBI focusses on enriching bacterial cfDNA molecules and preventing false positive bacterial identifications. cfFBI was applied to blood samples of 25 hospitalized foals categorized according to the neonatal Systemic Inflammatory Response Syndrome (nSIRS) criteria and 7 healthy foals. ResultscfDNA levels of potential sepsis-causing bacterial genera were elevated in all 11 nSIRS-positive foals compared to healthy foals (n=7), and in 8/11 (72.7%) when compared to both nSIRS-negative (n=4; nSIRS=0) and healthy foals, with multiple genera elevated in 5/11 (45.5%). The total cfDNA concentration, bacterial cfDNA fraction and bacterial diversity were not different between the foal groups. However, nSIRS-positive foals showed significantly different end-motifs in host chromosomal cfDNA, and a decrease in host mitochondrial cfDNA fraction. ConclusionsThis study is the first to demonstrate that cfDNA sequencing in blood samples from newborn foals enables detection of pathogenic bacteria and can help identify novel host-related sepsis biomarkers. The elevated presence of multiple sepsis-causing genera in nSIRS-positive foals and the difference in end-motif, suggests that multibacterial elevation may be more common than previously thought. These findings indicate that cfDNA sequencing holds promise as a future diagnostic tool for identifying sepsis in newborn foals.

pathology↗

bioRxiv2024

sv-channels: filtering genomic deletions using one-dimensional convolutional neural networks

Structural variant (SV) detection in human genomes using short-read sequencing data is hindered by false positives, arising from sequencing and mapping artifacts that mimic genuine SV signals. Despite advances, state-of-the-art SV callers like GRIDSS and Manta exhibit trade-offs between precision and recall, with GRIDSS offering the highest precision and Manta excelling in recall. To address these limitations, we introduce sv-channels, a novel deep learning model designed to improve the precision of SV detection by leveraging read information at call sites. Our method effectively reduces false positives in Mantas deletion callsets, achieving precision that surpasses GRIDSS while maintaining a recall rate comparable to Manta. This represents a significant improvement in SV detection, leveraging Mantas high recall through deep learning and paving the way for more accurate genomic analyses. The sv-channels codebase is openly accessible on GitHub at https://github.com/GooglingTheCancerGenome/sv-channels enabling further research and application in the field.

bioinformatics↗

bioRxiv2024

The regulatory grammar of human promoters uncovered by MPRA-trained deep learning

One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. At the heart of gene regulation are promoters, yet their regulatory logic is still incompletely understood. Here, we report PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences. PARM requires [~]1,000 times less computational power than state-of-the-art technology, and reliably predicts autonomous promoter activity throughout the genome from DNA sequence alone, in multiple cell types. PARM can even design purely synthetic strong promoters. We leveraged PARM to systematically identify binding sites of transcription factors (TFs) that are likely to contribute to the activity of each natural human promoter. We uncovered and experimentally confirmed striking positional preferences of TFs that differ between activating and repressive regulatory functions, as well as a complex grammar of motif-motif interactions. For example, many, but not all, TFs act as repressors when their binding motif is located near or just downstream of the transcription start site. Our approach lays the foundation towards a deep understanding of the regulation of human promoters by TFs. HighlightsO_LICausality-trained deep learning model PARM captures regulatory grammar of human promoters C_LIO_LIPARM is highly economical, both experimentally and computationally C_LIO_LITranscription factors have different preferred positions for their regulatory activity C_LIO_LIMany (but not all) transcription factors act as repressors when binding downstream of transcription start sites C_LI

genetics↗

bioRxiv2024

Direct detection of 8-oxo-dG using nanopore sequencing

Genomic DNA is constantly subjected to oxidative damage, which is thought to be one of the major drivers of cancer and age-dependent decline. The most prominent consequence is the modification of guanine into 8-hydroxyguanine (8-oxo-dG), which has important mutagenic potential and plays a role in methylation-mediated gene regulation. Methods to simultaneously detect and quantify 8-oxo-dG within its genomic context have been lacking; mainly because these methods rely on indirect detection or are based on hydrolysis of the DNA. Nanopore sequencing has been deployed for the direct detection of base-modifications like cytosine methylation during sequencing. However, currently there is no model to detect 8-oxo-dG by nanopore sequencing due to the lack of training data. Here, we developed a strategy based on synthetic oligos to create long DNA molecules with context variability for effective deep learning and nanopore sequencing. Moreover, we showcase a training approach suitable to deal with the extreme scarceness of 8-oxo-dG compared to canonical G to enable specific 8-oxo-dG detection. Applied to an inducible tissue culture system for oxidative DNA damage, our approach reveals variable 8-oxo-dG distribution across the genome, a dissimilar context pattern to C>A mutations, and concurrent 5-mC depletion within a 2-kilobase window surrounding 8-oxo-dG sites. These findings not only underscore the potential of nanopore sequencing in epigenetic research, but also shed light on 8-oxo-dGs role in genomic regulation. By simultaneously measuring 5-mC and 8-oxo-dG at single molecule resolution, our study provides insights into the functional interplay between these DNA modifications. Moreover, our approach using synthetic oligos to generate a ground truth from machine learning modification calling could be applied to any other DNA modification. Overall, our work contributes to advancing the field of epigenetics and highlights nanopore sequencing as a powerful tool for studying DNA modifications.

bioinformatics↗

bioRxiv2024

Mutational synergy with CREBBP loss in lymphomagenesis identified through forward insertional mutagenesis in a new DLBCL mouse model

Loss-of-function mutations in the gene encoding the acetyltransferase CREBBP have been reported in numerous cancers but are particularly frequent in lymphoid malignancies. However, the functional significance of CREBBP loss in transformation and disease progression, most likely through cooperation with secondary genetic hits, has not yet been fully unravelled. Similarly, the contribution of the initial cell population sustaining CREBBP loss in the course of disease remains elusive. Here, we developed a new lymphoma mouse model integrating Crebbp loss at various stages of B cell development with a transposon-based insertional mutagenesis system. We demonstrated that Crebbp loss from the HSPC compartment resulted in an aggressive DLBCL-like disease, recapitulating well-characterised histological and molecular features of the human disease, as well as the recently described enhanced CD24 expression. More importantly, we identified candidate genes functionally equivalent to patient mutated genes. Those genes, mainly related to B cell development and cellular signalling, may represent novel therapeutic targets. Overall, this new model provides a powerful resource in which to conduct future mechanistic and therapeutic studies.

cancer biology↗

bioRxiv2024

Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling

Shallow genome-wide cell-free DNA (cfDNA) sequencing holds great promise for non-invasive cancer monitoring by providing reliable copy number alteration (CNA) and fragmentomic profiles. Single nucleotide variations (SNVs) are, however, much harder to identify with low sequencing depth due to sequencing errors. Here we present Nanopore Rolling Circle Amplification (RCA)-enhanced Consensus Sequencing (NanoRCS), which leverages RCA and consensus calling based on genome-wide long-read nanopore sequencing to enable simultaneous multimodal tumor fraction estimation through SNVs, CNAs, and fragmentomics. Efficacy of NanoRCS is tested on 18 cancer patient samples and 3 healthy controls, demonstrating its ability to reliably detect tumor fractions as low as 0.24%. In vitro experiments confirm that SNV measurements are essential for detecting tumor fractions below 2%. NanoRCS provides the opportunity for cost-effective and rapid processing, which aligns well with clinical needs, particularly in settings where quick and accurate cancer monitoring is essential for personalized treatment strategies.

genomics↗

bioRxiv2023

Intestinal LKB1 loss drives a pre-malignant program along the serrated cancer pathway

Background & AimsHeterozygous inactivating mutations of Serine Threonine Kinase 11 (STK11)/Liver Kinase B1 (LKB1) are causative to the Peutz-Jeghers syndrome (PJS), a hereditary disease characterized by gastrointestinal hamartomatous polyposis and increased cancer susceptibility. While LKB1 loss-induced polyp formation has been ascribed to non-epithelial tissues, how LKB1 deficiency increases cancer risk of patients by altering the phenotypical landscape and hierarchical organization of epithelial tissues remains poorly understood. MethodsUsing CRISPR/Cas9, we generated heterozygous and homozygous Lkb1-deficient mouse small intestinal and human colon organoids. These organoids were characterized by an integrated approach that combines imaging, bulk and single-cell RNA sequencing and growth factor dependency assays. Our findings were validated in human PJS-derived tissues using immunohistochemistry and linked to colorectal cancer profiles using the TCGA cancer database. ResultsOur results reveal that heterozygous Lkb1 loss is sufficient to push intestinal cells into a premalignant transcriptional program associated with serrated colorectal cancer, which is further amplified by loss-of-heterozygosity. This altered epithelial growth state associates with persistent features of regeneration and enhanced EGFR ligand and receptor expression, conferring niche-independent growth properties to Lkb1-deficient organoids. Moreover, our newly generated LKB1-mutant signature is enriched in sporadic serrated colorectal cancer, and synergistic cooperation of Lkb1-deficiency with mutant Kras was experimentally confirmed by assessing organoid growth properties and transcriptomes. ConclusionsHeterozygous loss of LKB1 pushes intestinal cells into a chronic regenerative state which is amplified upon loss-of-heterozygosity. Lkb1-deficiency thereby generates fertile ground for serrated colorectal cancer formation in the intestine, potentially explaining the increased cancer risk observed in PJS.

cancer 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

Comprehensive and standardized benchmarking of deep learning architectures for basecalling nanopore sequencing data

AO_SCPLOWBSTRACTC_SCPLOWNanopore-based DNA sequencing relies on basecalling the electric current signal. Basecalling requires neural networks to achieve competitive accuracies. To improve sequencing accuracy further, new models are continuously proposed. However, benchmarking is currently not standardized, and evaluation metrics and datasets used are defined on a per publication basis, impeding progress in the field. To standardize the process of benchmarking, we unified existing benchmarking datasets and defined a rigorous set of evaluation metrics. We benchmarked the latest seven basecaller models and analyzed their deep learning architectures. Our results show that overall Bonito has the best architecture for basecalling. We find, however, that species bias in training can have a large impact on performance. Our comprehensive evaluation of 90 novel architecture demonstrates that different models excel at reducing different types of errors and using RNNs (LSTM) and a CRF decoder are the main drivers of high performing models.

bioinformatics↗

bioRxiv2022

Echtvar: Compressed variant representation for rapid annotation and filtering of SNPs and indels

Germline and somatic variants within an individual or cohort are interpreted with information from large cohorts. Annotation with this information becomes a computational bottleneck as population sets grow to terabytes of data. Here, we introduce echtvar, which efficiently encodes population variants and annotation fields into a compressed archive that can be used for rapid variant annotation and filtering. Most variants, including position and alleles are encoded into 32-bits-half the size of previous encoding schemes and at least 4 times smaller than a naive encoding. The annotations, stored separately, are also encoded and compressed. We show that echtvar is faster and uses less space than existing tools and that it can effectively reduce the number of candidate variants. We give examples on germ-line and somatic variants to document how echtvar can facilitate exploratory data analysis on genetic variants. Echtvar is available at https://github.com/brentp/echtvar under an MIT license.

bioinformatics↗

bioRxiv2022

PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from GWAS summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a p-value per SNP that can be used for further analysis. We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. This network shows how PolarMorphism can be used to gain insight into relationships between traits and trait domains. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

genetics↗

bioRxiv2021

Cancer type classification in liquid biopsies based on sparse mutational profiles enabled through data augmentation and integration

Identifying the cell of origin of cancer is important to guide treatment decisions. However, in patients with cancer of unknown primary (CUP), standard diagnostic tools often fail to identify the primary tumor. As an alternative, machine learning approaches have been proposed to classify the cell of origin based on somatic mutation profiles in the genome of solid tissue biopsies. However, solid biopsies can cause complications and certain tumors are not accessible. A promising alternative would be liquid biopsies, which contain ctDNA originating from the tumor. Problematically, somatic mutation profiles of tumors obtained from liquid biopsies are inherently extremely sparse and current machine learning models fail to perform in this setting. Here we propose an improved machine learning method to deal with the sparse nature of liquid biopsy data. Firstly, we downsample the SNVs in the samples in order to mimic sparse data conditions. Then extensive data augmentation is performed to artificially increase the number of training samples in order to enhance model robustness under sparse data conditions. Finally, we employ data integration to merge information from i) somatic single nucleotide variant (SNV) density across the genome, ii) somatic SNVs in driver genes and iii) trinucleotide motifs. Our adapted method achieves an average accuracy of 0.88 on the data where only 70% of SNVs are retained, which is comparable to an average accuracy of 0.87 with the original model on the full SNV data. Even when only 2% of the data is retained, the average accuracy is 0.65 compared to 0.41 with the original model. The method and results presented here open the way for application of machine learning in the detection of the cell of origin of cancer from sparse liquid biopsy data. Author SummaryThe identification of the cell of origin of cancer is an important step towards more personalized cancer care, but this remains a challenge for patients with cancer of unknown primary (CUP) where the source of the malignancy cannot be identified even after extensive clinical assessment with standard diagnostic methods. Somatic mutation profile-based cell of origin classification has emerged in recent years as a promising alternative diagnostic tool that could circumvent the issues of standard CUP diagnostic. In this approach the somatic mutations are obtained from whole genome sequencing (WGS) of solid tissue biopsies from the tumor. However, needle biopsies from tumor tissue can be challenging, as accessibility to the tumor can be limited and taking a biopsy can cause further complications. For these reasons, liquid biopsies have been proposed as a safer alternative to solid tissue biopsies. Problematically, the circulating tumor DNA fragments available in e.g. blood typically represent a much scarcer tumor source than conventional solid tissue biopsies and therefore liquid biopsies give rise to sparse somatic mutation profiles. Therefore it is crucial to investigate the applicability of sparse somatic mutation profiles in the identification of cell of origin and explore potential improvements of the data analysis and prediction models to overcome sparsity.

bioinformatics↗

bioRxiv2020

Accurate detection of circulating tumor DNA using nanopore consensus sequencing

Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant ctDNA molecules based on somatic variants, extremely sensitive sequencing methods are required. Here, we describe a new technique, CyclomicsSeq, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule. Consensus calling of the DNA copies increased the base-calling accuracy [~]60x, enabling accurate detection of TP53 mutations at frequencies down to 0.02%. We demonstrate that a TP53-specific CyclomicsSeq assay can be successfully used to monitor tumor burden during treatment for head-and-neck cancer patients. CyclomicsSeq can be applied to any genomic locus and offers an accurate diagnostic liquid biopsy approach that can be implemented in point-of-care clinical workflows.

cancer biology↗

bioRxiv2019

MetaboShiny - interactive processing, analysis and identification of untargeted metabolomics data

Direct infusion untargeted metabolomics, as mass-over-charge values and intensity of ions, allows for rapid insight into a samples metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted metabolomics data.

bioinformatics↗

bioRxiv2019

Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke

Stroke causes approximately 1 in every 20 deaths in the United States. Most strokes are ischemic, caused by a blockage of blood flow to the brain. While neurologists agree on the delineation of ischemic stroke (IS) into the three most common subtypes (cardioembolic stroke (CES), large artery stroke (LAS), and small vessel stroke (SVS)), several different subtyping systems exist. The two most commonly-used clinical subtyping systems are TOAST (Trial of Org 10172 in Acute Stroke Treatment) and CCS (Causative Classification System for Stroke), but agreement between these two systems is only moderate. Here, we have compared two approaches to combining the existing subtyping systems for a phenotype suited for a genome-wide association study (GWAS).\n\nWe used the NINDS Stroke Genetics Network dataset (SiGN, 13,390 cases and 28,026 controls), which includes cases with both CCS and TOAST subtypes. We defined two new phenotypes: 1) the intersect, for which an individual must be assigned the same subtype by CCS and TOAST; and 2) the union, for which an individual must be assigned a subtype by either CCS or TOAST. The union yields the largest sample size while the intersect may yield a phenotype with less potential misclassification.\n\nWe performed GWAS for all subtypes, using the original subtyping systems, the intersect, and the union as phenotypes. In each subtype, heritability was higher for the intersect phenotype compared to the union, CCS (alone), and TOAST (alone) phenotypes. We observed stronger effects at known IS variants with the intersect compared to the other phenotype definitions. In GWAS of the intersect, we identify rs10029218 as an associated variant with small vessel stroke. We conclude that in the absence of a golden standard for phenotyping, taking this alternate approach yields more power to detect genetic associations in ischemic stroke.\n\nAuthor summaryAround one in five people will have a stroke at some point in their life. Most strokes (~80%) are ischemic, caused by a blockage of blood supply to the brain. Ischemic stroke risk is partly influenced by lifestyle, and partly by genetics. There are different ischemic stroke subtypes, and genome-wide association studies (GWAS) indicate that the genetic risk for these subtypes is influenced by different genetic factors. Genetic studies of ischemic stroke are therefore typically performed by analyzing each subtype separately. There are several methods to determine someones subtype based on clinical features. To find more genetic factors that influence ischemic stroke risk, we aimed to find a group of patients that are phenotypically similar by using information from all subtyping methods. We compared a group of patients assigned the same subtype by all subtyping methods (the intersect) to a group of patients assigned that subtype by at least one subtyping method (the union). Even though the intersect sample size is smaller, we find genetic factors in the intersect GWAS have stronger genetic effects, likely explained by the fact that we are more certain of the subtype in the intersect. Using the intersect, we find new risk-associated genetic factors.

genetics↗

bioRxiv2018

Molecular Heterogeneity and Early Metastatic Clone Selection in Testicular Germ Cell Cancer Development

BackgroundTesticular germ cell cancer (TGCC), being the most frequent malignancy in young Caucasian males, is initiated from an embryonic germ cell. This study determines intratumor heterogeneity to unravel tumor progression from initiation till metastasis.\n\nMethodsIn total 42 purified samples of four treatment-resistant nonseminomatous TGCC (NS) were investigated, including the precursor germ cell neoplasia in situ (GCNIS) and metastatic specimens, using whole genome- and targeted sequencing. Their evolution was reconstructed.\n\nResultsIntratumor molecular heterogeneity did not correspond to the supposed primary tumor histological evolution. Metastases after systemic treatment could be derived from cancer stem cells not identified in the primary cancer. GCNIS mostly lacked the molecular marks of the primary NS and comprised dominant clones that failed to progress. A BRCA-like mutational signature was observed without evidence for direct involvement of BRCA1 and BRCA2 genes.\n\nConclusionsOur data strongly support the hypothesis that NS is initiated by whole genome duplication, followed by chromosome copy number alterations in the cancer stem cell population, and accumulation of low numbers of somatic mutations. These observations of heterogeneity at all stages of tumorigenesis should be considered when treating patients with GCNIS-only disease, or with clinically overt NS.

cancer biology↗