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Kuipers, J.

Publications and source records attributed to Kuipers, J..

32 records · Page 2Linked to original sources
bioRxiv2021

A statistical test for clonal exclusivity in tumour evolution

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Pairs of mutations that are overrepresented in a clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical test, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our test for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 6 clonally exclusive and 2 clonally co-occurring gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

cancer biology↗
bioRxiv2021

scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

bioinformatics↗
bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗
bioRxiv2020

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups. Author summaryThere is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.

evolutionary biology↗
bioRxiv2020

Pancreatic Beta Cell Autophagy is Impaired in Type 1 Diabetes

Aims/hypothesisPancreatic beta cells are highly metabolic secretory cells that are subjected to exogenous damaging factors such as proinflammatory cytokines or excess glucose that can cause accumulation of damage-inducing reactive oxygen species (ROS) during the pathogenesis of diabetes. We and others have shown that beta cell autophagy can reduce ROS to protect against apoptosis both in vitro and in vivo. While impaired islet autophagy has been demonstrated in human type 2 diabetes, it is unknown if islet autophagy is perturbed in the pathogenesis of type 1 diabetes. We hypothesized that beta cell autophagy is dysfunctional in type 1 diabetes, and that there is a progressive loss during early diabetes development. MethodsMouse pancreata were collected from chloroquine injected and non-injected NOR, nondiabetic NOD, and diabetic NOD mice. Age and BMI-matched pancreas tissue sections from human organ donors (n=34) were obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD). To assess autophagic flux, we injected the mice with chloroquine to inhibit the final stages of autophagy. We analyzed tissues for markers of autophagy via immunofluorescence analysis. Tissue sections were stained with antibodies against proinsulin or insulin (beta cell markers), LC3A/B (autophagosome marker), Lamp1 (lysosome marker), and p62 (autophagy adaptor protein and marker for autophagic flux). Images were collected on a scanning laser confocal microscope then analyzed with CellProfiler and ImageJ. Secondary lysosomes and telolysosomes (formerly called lipofuscin bodies, residual bodies or tertiary lysosomes) were analyzed in electron micrographs of pancreatic tissue sections from human organ donors (nPOD; n=12) deposited in www.nanotomy.org/OA/nPOD. Energy Dispersive X-ray (EDX) analysis was also performed on these tissues to analyze distribution of elements such as nitrogen, phosphorus, and osmium in secondary lysosomes and telolysosomes of nondiabetic and autoantibody positive donor tissues (n=5). ResultsWe observed increased autophagosome numbers in islets of diabetic NOD mice (p=0.0077) and increased p62 in islets of both nondiabetic and diabetic NOD mice (p<0.0001 in both cases) when compared to NOR mice. There was also a significant reduction in autophagosome:lysosome colocalization in islets of diabetic NOD mice compared to both nondiabetic NOD mice (p=0.0004) and NOR mice (p=0.0003). Chloroquine infusions elicited accumulation of autophagosomes in the islets of NOR (p=0.0029) and nondiabetic NOD mice (p<0.0001), but not in the islets of diabetic NOD mice. Chloroquine also stimulated an accumulation of the autophagy adaptor protein p62 in the islets of NOR mice (p<0.001), however this was not observed in NOD mice (regardless of diabetes status). In the human pancreata, we observed significantly reduced autophagosome:lysosome colocalization (p=0.0002) in the residual beta cells of donors with type 1 diabetes compared to nondiabetic controls. We also observed reduced colocalization of proinsulin with lysosomes in the residual beta cells of donors with type 1 diabetes compared to both nondiabetic (p<0.0001) and autoantibody positive donors (p<0.0001). Electron microscopy based analysis of human pancreas sections also revealed accumulation of telolysosomes in beta cells of autoantibody positive donors (p=0.0084), the majority of which had an nitrogen dense ring outside a phospholipid core. Conclusions/interpretationCollectively, we provide evidence of impairment in the final degradation stages of islet macroautophagy and crinophagy in human type 1 diabetes. We also document an accumulation of telolysosomes with nitrogen accumulation at their periphery in the beta cells of autoantibody positive donors. This demonstrates clear differences in the lysosome contents of autoantibody positive donors that may be associated with lysosome dysfunction prior to clinical hyperglycemia. We observe similar impairments in macroautophagy in the diabetic NOD mouse, a model of type 1 diabetes, suggesting that this mouse model can be appropriately used to study the pathogenesis of autophagy/crinophagy loss and how it relates to disease initiation and progression. Considering these data in the context of what is known regarding the cell-protective effects of islet autophagy, we suggest targeting beta cell autophagy pathways as an approach to reduce apoptosis in individuals at risk for type 1 diabetes development. Research in contextWhat is already known about this subject? O_LIAutophagy confers a cytoprotective role in the beta cell. C_LIO_LIAutophagy is reduced in type 2 diabetes. C_LIO_LIAutophagy in the context of type 1 diabetes is unexplored. C_LI What is the key question? O_LIIs autophagy reduced during the pathogenesis of human type 1 diabetes? C_LI What are the new findings? O_LIWe provide evidence of reduced autophagy and crinophagy in human type 1 diabetes. C_LIO_LIThese data are supported by observations of reduced autophagy in a mouse model of autoimmune diabetes. C_LI How might this impact on clinical practice in the foreseeable future? O_LIThis study provides evidence that autophagy is impaired in human type 1 diabetes. Prior studies have shown that loss of autophagy in the islet is associated with increased beta cell apoptosis, therefore we propose that therapeutic targeting of autophagy pathways may reduce beta cell death in type 1 diabetes development. C_LI

cell biology↗
bioRxiv2020

Single-cell copy number calling and event history reconstruction

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.

cancer biology↗
bioRxiv2020

Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

One of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1/FLT3-ITD, DNMT3A/NPM1, SRSF2/IDH2, and WT1/FLT3-ITD) and those that were mutually exclusive (e.g., NRAS/KRAS, FLT3-D835/ITD, and IDH1/IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity ([&ge;]4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

cancer biology↗
bioRxiv2018

Intra-tumor heterogeneity and clonal exclusivity in renal cell carcinoma

Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1), frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction, and might explain clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.

cancer biology↗
bioRxiv2018

SCIΦ: Single-cell mutation identification via phylogenetic inference

Understanding the evolution of cancer is important for the development of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations with an unknown number of genetically distinct subclones of varying frequencies. Conventional approaches based on bulk sequencing are limited in addressing this challenge as clones cannot be observed directly. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic dropout, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCI{Phi}, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme we robustly account for the various sources of noise in single-cell sequencing data. Our approach enables us to reliably call mutations in each single cell even in experiments with high dropout rates and missing data. We show that SCI{Phi} outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset. Availability: https://github.com/cbg-ethz/SCIPhI

bioinformatics↗
bioRxiv2018

Improved pathway reconstruction from RNA interference screens by exploiting off-target effects

Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs. Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components.\n\nAvailability: The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git\n\nContact: niko.beerenwinkel@bsse.ethz.ch

bioinformatics↗
bioRxiv2017

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.

bioinformatics↗
bioRxiv2017

Mutational interactions define novel cancer subgroups

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

cancer biology↗
bioRxiv2017

A novel flatworm-specific gene family implicated in reproduction in Macrostomum lignano

Free-living flatworms, such as the planarian Schmidtea mediterranea, are extensively used as model organisms to study stem cells and regeneration. The majority of studies in planarians so far focused on broadly conserved genes. However, investigating what makes these animals different might be equally informative for understanding its biology. Here, we present a re-analysis of neoblast and germline transcriptional signatures in the flatworm M. lignano and combine it with the whole-animal electron microscopy atlas (nanotomy) as a reference platform for ultrastructural studies in M. lignano. We show that germline-enriched genes have a high fraction of flatworm-specific genes and identify Mlig-sperm1 gene as a member of a novel gene family conserved only in free-living flatworms and essential for producing healthy spermatozoa. This work demonstrates that investigation of flatworm-specific genes is crucial for understanding flatworm biology and establishes a basis for future research in this direction in M. lignano.

developmental biology↗
bioRxiv2016

A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

cancer biology↗