Biology subjects

Gerstung, M.

Publications and source records attributed to Gerstung, M..

32 records · Page 2Linked to original sources

bioRxiv2020

Patterns of within-host genetic diversity in SARS-CoV-2

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1,181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within and between host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

genomics↗

bioRxiv2020

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics

The spatial organization of cell types in tissues fundamentally shapes cellular interactions and function, but the high-throughput spatial mapping of complex tissues remains a challenge. We present [c]ell2location, a principled and versatile Bayesian model that integrates single-cell and spatial transcriptomics to map cell types in situ in a comprehensive manner. We show that [c]ell2location outperforms existing tools in accuracy and comprehensiveness and we demonstrate its utility by mapping two complex tissues. In the mouse brain, we use a new paired single nucleus and spatial RNA-sequencing dataset to map dozens of cell types and identify tissue regions in an automated manner. We discover novel regional astrocyte subtypes including fine subpopulations in the thalamus and hypothalamus. In the human lymph node, we resolve spatially interlaced immune cell states and identify co-located groups of cells underlying tissue organisation. We spatially map a rare pre-germinal centre B-cell population and predict putative cellular interactions relevant to the interferon response. Collectively our results demonstrate how [c]ell2location can serve as a versatile first-line analysis tool to map tissue architectures in a high-throughput manner.

genomics↗

bioRxiv2020

Systematic analysis of mutational spectra associated with DNA repair deficiency in C. elegans

Genome integrity is particularly important in germ cells to faithfully preserve genetic information across generations. As yet little is known about the contribution of various DNA repair pathways to prevent mutagenesis. Using the C. elegans model we analyse mutational spectra that arise in wild-type and 61 DNA repair and DNA damage response mutants cultivated over multiple generations. Overall, 44% of lines show >2-fold increased mutagenesis with a broad spectrum of mutational outcomes including changes in single or multiple types of base substitutions induced by defects in base excision or nucleotide excision repair, or elevated levels of 50-400 bp deletions in translesion polymerase mutants rev-3(pol {zeta}) and polh-1(pol {eta}). Mutational signatures associated with defective homologous recombination fall into two classes: 1) mutants lacking brc-1/BRCA1 or rad-51/RAD51 paralogs show elevated base substitutions, indels and structural variants, while 2) deficiency for MUS-81/MUS81 and SLX-1/SLX1 nucleases, and HIM-6/BLM, HELQ-1/HELQ and RTEL-1/RTEL1 helicases primarily cause structural variants. Genome-wide investigation of mutagenesis patterns identified elevated rates of tandem duplications often associated with inverted repeats in helq-1 mutants, and a unique pattern of translocation events involving homeologous sequences in rip-1 paralog mutants. atm-1/ATM DNA damage checkpoint mutants harboured complex structural variants enriched in subtelomeric regions, and chromosome end-to-end fusions. Finally, while inactivation of the p53-like gene cep-1 did not affect mutagenesis, combined brc-1 cep-1 deficiency displayed increased, locally clustered mutagenesis. In summary, we provide a global view of how DNA repair pathways prevent germ cell mutagenesis.

genomics↗

bioRxiv2020

Genomic copy number predicts oesophageal cancer years before transformation

Cancer arises through a process of somatic evolution and recent studies have shown that aneuploidies and driver gene mutations precede cancer diagnosis by several years to decades1-4 Here, we address the question whether such genomic signals can be used for early detection and pre-emptive cancer treatment. To this end we study Barretts oesophagus, a genomic copy number driven neoplastic precursor lesion to oesophageal adenocarcinoma5. We use shallow whole genome sequencing of 777 biopsies sampled from 88 patients in surveillance for Barretts oesophagus over a period of up to 15 years. These data show that genomic signals exist that distinguish progressive from stable disease with an AUC of 0.87 and a sensitivity of 50% even ten years prior to histopathological disease transformation. These finding are validated on two independent cohorts of 75 and 248 patients. Compared against current patient management guidelines genomic risk classification enables earlier treatment for high risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.

genomics↗

bioRxiv2019

Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

Mutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Pol{eta}-driven translesion synthesis found in a broad range of cancer types. Key findingsO_LISimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants. C_LIO_LIAnalysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes. C_LIO_LIDistinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage. C_LIO_LIAPOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively. C_LIO_LISomatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types. C_LI

cancer biology↗

bioRxiv2019

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

The diagnosis of cancer is typically based on histopathological assessment of tissue sections, and supplemented by genetic and other molecular tests1-6. Modern computer vision algorithms have high diagnostic accuracy and potential to augment histopathology workflows7-9. Here we use deep transfer learning to quantify histopathological patterns across 17,396 hematoxylin and eosin (H&E) stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations pan-cancer. This includes whole genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions as well as driver gene mutations. There are wide-spread associations between bulk gene expression levels and histopathology, which reflect tumour composition and enables localising transcriptomically defined tumour infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings demonstrate the large potential of computer vision to characterise the molecular basis of tumour histopathology and lay out a rationale for integrating molecular and histopathological data to augment diagnostic and prognostic workflows.

bioinformatics↗

bioRxiv2019

Mutational signatures are jointly shaped by DNA damage and repair

Mutations arise when DNA lesions escape DNA repair. To delineate the contributions of DNA damage and DNA repair deficiency to mutagenesis we sequenced 2,717 genomes of wild-type and 53 DNA repair defective C. elegans strains propagated through several generations or exposed to 11 genotoxins at multiple doses. Combining genotoxin exposure and DNA repair deficiency alters mutation rates or leads to unexpected mutation spectra in nearly 40% of all experimental conditions involving 9/11 of genotoxins tested and 32/53 genotypes. For 8/11 genotoxins, signatures change in response to more than one DNA repair deficiency, indicating that multiple genes and pathways are involved in repairing DNA lesions induced by one genotoxin. For many genotoxins, the majority of observed single nucleotide variants results from error-prone translesion synthesis, rather than primary mutagenicity of altered nucleotides. Nucleotide excision repair mends the vast majority of genotoxic lesions, preventing up to 99% of mutations. Analogous mutagenic DNA damage-repair interactions can also be found in cancers, but, except for rare cases, effects are weak owing to the unknown histories of genotoxic exposures and DNA repair status. Overall, our data underscore that mutation spectra are joint products of DNA damage and DNA repair and imply that mutational signatures computationally derived from cancer genomes are more variable than currently anticipated.

genomics↗

bioRxiv2018

Process-specific somatic mutation distributions vary with three-dimensional genome structure

Somatic mutations arise during the life history of a cell. Mutations occurring in cancer driver genes may ultimately lead to the development of clinically detectable disease. Nascent cancer lineages continue to acquire somatic mutations throughout the neoplastic process and during cancer evolution (Martincorena and Campbell, 2015). Extrinsic and endogenous mutagenic factors contribute to the accumulation of these somatic mutations (Zhang and Pellman, 2015). Understanding the underlying factors generating somatic mutations is crucial for developing potential preventive, therapeutic and clinical decisions. Earlier studies have revealed that DNA replication timing (Stamatoyannopoulos et al., 2009) and chromatin modifications (Schuster-Bockler and Lehner, 2012) are associated with variations in mutational density. What is unclear from these early studies, however, is whether all extrinsic and exogenous factors that drive somatic mutational processes share a similar relationship with chromatin state and structure. In order to understand the interplay between spatial genome organization and specific individual mutational processes, we report here a study of 3000 tumor-normal pair whole genome datasets from more than 40 different human cancer types. Our analyses revealed that different mutational processes lead to distinct somatic mutation distributions between chromatin folding domains. APOBEC- or MSI-related mutations are enriched in transcriptionally-active domains while mutations occurring due to tobacco-smoke, ultraviolet (UV) light exposure or a signature of unknown aetiology (signature 17) enrich predominantly in transcriptionally-inactive domains. Active mutational processes dictate the mutation distributions in cancer genomes, and we show that mutational distributions shift during cancer evolution upon mutational processes switch. Moreover, a dramatic instance of extreme chromatin structure in humans, that of the unique folding pattern of the inactive X-chromosome leads to distinct somatic mutation distribution on X chromosome in females compared to males in various cancer types. Overall, the interplay between three-dimensional genome organization and active mutational processes has a substantial influence on the large-scale mutation rate variations observed in human cancer.

genomics↗

bioRxiv2018

Genomic landscape and chronological reconstruction of driver events in multiple myeloma

Multiple myeloma (MM) has a heterogeneous genome, evolving through both pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM genomes serially collected from 30 patients together with public datasets, we establish a hierarchy of driver lesions. Point mutations, structural variants and copy number aberrations define at least 7 genomic subgroups of MM, each with distinct sets of co-operating driver mutations. Complex structural events are major drivers of MM, including chromothripsis, chromoplexy and a replication-based mechanism of templated insertions: these typically occur early. Hyperdiploidy also occurs early, with individual chromosomes often gained in more than one chronological epoch of MM evolution, showing a preferred order of acquisition. Positively selected point mutations frequently occur in later phases of disease development, as do structural variants involving MYC. Thus, initiating driver events of MM, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of subsequent evolution.

cancer biology↗

bioRxiv2018

Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

cancer biology↗

bioRxiv2017

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

cancer biology↗

bioRxiv2017

Neutral tumor evolution?

Tumor growth is an evolutionary process governed by somatic mutation, clonal selection and random genetic drift, constrained by the co-evolution of the microenvironment1,2. Tumor subclones are subpopulations of tumor cells with a common set of mutations resulting from the expansion of a single cell during tumor development, and have been observed in a significant fraction of cancers and across multiple cancer types3. Peter Nowell proposed that tumors evolve through sequential genetic events4, whereby one cell acquires a selective advantage so that its lineage becomes predominant. According to this traditional model, the selective advantage is conferred by a small set of driver mutations, but, as the subclones that bear them expand successively, they accumulate passenger mutations as well, which can be detected in sequencing experiments1. Genomes of indiv ...

cancer biology↗

bioRxiv2017

Mutational signatures of DNA mismatch repair deficiency in C. elegans and human cancers

1.Throughout their lifetime cells are subject to extrinsic and intrinsic mutational processes leaving behind characteristic signatures in the genome. One of these, DNA mismatch repair (MMR) deficiency leads to hypermutation and is found in different cancer types. While it is possible to associate mutational signatures extracted from human cancers with possible mutational processes the exact causation is often unknown. Here we use C. elegans genome sequencing of pms-2 and mlh-1 knockouts to reveal the mutational patterns linked to C. elegans MMR deficiency and their dependency on endogenous replication errors and errors caused by deletion of the polymerase {varepsilon} subunit pole-4. Signature extraction from 215 human colorectal and 289 gastric adenocarcinomas revealed three MMR-associated signatures one of which closely resembles the C. elegans MMR spectrum. A characteristic difference between human and worm MMR deficiency is the lack of elevated levels of NCG>NTG mutations in C. elegans, likely caused by the absence of cytosine (CpG) methylation in worms. The other two human MMR signatures may reflect the interaction between MMR deficiency and other mutagenic processes, but their exact cause remains unknown. In summary, combining information from genetically defined models and cancer samples allows for better aligning mutational signatures to causal mutagenic processes.

genomics↗

bioRxiv2017

Universal Patterns Of Selection In Cancer And Somatic Tissues

Cancer develops as a result of somatic mutation and clonal selection, but quantitative measures of selection in cancer evolution are lacking. We applied methods from evolutionary genomics to 7,664 human cancers across 29 tumor types. Unlike species evolution, positive selection outweighs negative selection during cancer development. On average, <1 coding base substitution/tumor is lost through negative selection, with purifying selection only detected for truncating mutations in essential genes in haploid regions. This allows exome-wide enumeration of all driver mutations, including outside known cancer genes. On average, tumors carry [~]4 coding substitutions under positive selection, ranging from <1/tumor in thyroid and testicular cancers to >10/tumor in endometrial and colorectal cancers. Half of driver substitutions occur in yet-to-be-discovered cancer genes. With increasing mutation burden, numbers of driver mutations increase, but not linearly. We identify novel cancer genes and show that genes vary extensively in what proportion of mutations are drivers versus passengers.\n\nHIGHLIGHTSO_LIUnlike the germline, somatic cells evolve predominantly by positive selection\nC_LIO_LINearly all ([~]99%) coding mutations are tolerated and escape negative selection\nC_LIO_LIFirst exome-wide estimates of the total number of driver coding mutations per tumor\nC_LIO_LI1-10 coding driver mutations per tumor; half occurring outside known cancer genes\nC_LI

genomics↗