Evidence of survival bias in the association between and age at ischemic stroke onset.

Introduction: Large genome-wide association studies (GWASs) using case-control study designs have now identified tens of loci associated with ischemic stroke (IS). As a complement to these studies, we performed GWAS in a case-only design to identify loci influencing the age at onset (AAO) of ischemic stroke.

Methods: Analyses were conducted in a discovery cohort of 10,857 ischemic stroke cases using a linear regression framework.

WEE1 inhibitor adavosertib in combination with carboplatin in advanced TP53 mutated ovarian cancer: A biomarker-enriched phase II study.

Objective: In the first part of this phase II study (NCT01164995), the combination of carboplatin and adavosertib (AZD1775) was shown to be safe and effective in patients with TP53 mutated platinum-resistant ovarian cancer (PROC). Here, we present the results of an additional safety and efficacy cohort and explore predictive biomarkers for resistance and response to this combination treatment.

Methods: This is a phase II, open-label, non-randomized study.

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.

Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling.

Background: Nanopore-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 with new architectures.

Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning.

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models.

Integration of multiple lineage measurements from the same cell reconstructs parallel tumor evolution.

Organoid evolution models complemented with integrated single-cell sequencing technology provide a powerful platform to characterize intra-tumor heterogeneity (ITH) and tumor evolution. Here, we conduct a parallel evolution experiment to mimic the tumor evolution process by evolving a colon cancer organoid model over 100 generations, spanning 6 months in time. We use single-cell whole-genome sequencing (WGS) in combination with viral lineage tracing at 12 time points to simultaneously monitor clone size, CNV states, SNV states, and viral lineage barcodes for 1,641 single cells.

Multi-country metabolic signature discovery for chicken health classification.

Introduction: To decrease antibiotic resistance, their use as growth promoters in the agricultural sector has been largely abandoned. This may lead to decreased health due to infectious disease or microbiome changes leading to gut inflammation.

Objectives: We aimed to generate a m/z signature classifying chicken health in blood, and obtain biological insights from the resulting m/z signature.

In Defense of the Netherlands Code of Conduct for Research Integrity: Response to Radder.

We assess Radder's criticisms of the and show that they either miss their mark or depend on controversial background assumptions about the purpose of the Code. Although Radder raises important questions about the broader roles and purposes of research in society, his conclusion that the Code should be revised in the ways he proposes is unjustified.

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.

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

Motivation: 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 genome-wide association studies (GWAS) summary statistics.

How to trust a scientist.

Epistemic trust among scientists is inevitable. There are two questions about this: (1) What is the content of this trust, what do scientists trust each other for? (2) Is such trust epistemically justified? I argue that if we assume a traditional answer to (1), namely that scientists trust each other to be reliable informants, then the answer to question (2) is negative, certainly for the biomedical and social sciences. This motivates a different construal of trust among scientists and therefore a different answer to (1): scientists trust each other to only testify to claims that are backed by evidence gathered in accordance with prevailing methodological standards.

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. Machine learning approaches have been proposed to classify the cell of origin based on somatic mutation profiles from solid biopsies. However, solid biopsies can cause complications and certain tumors are not accessible.

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.

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes.

Allele-specific expression of GATA2 due to epigenetic dysregulation in CEBPA double-mutant AML.

Transcriptional deregulation is a central event in the development of acute myeloid leukemia (AML). To identify potential disturbances in gene regulation, we conducted an unbiased screen of allele-specific expression (ASE) in 209 AML cases. The gene encoding GATA binding protein 2 (GATA2) displayed ASE more often than any other myeloid- or cancer-related gene.

svMIL: predicting the pathogenic effect of TAD boundary-disrupting somatic structural variants through multiple instance learning.

Motivation: Despite the fact that structural variants (SVs) play an important role in cancer, methods to predict their effect, especially for SVs in non-coding regions, are lacking, leaving them often overlooked in the clinic. Non-coding SVs may disrupt the boundaries of Topologically Associated Domains (TADs), thereby affecting interactions between genes and regulatory elements such as enhancers. However, it is not known when such alterations are pathogenic.

RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials.

Motivation: When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole.

MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data.

Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's 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.

Gene Networks Constructed Through Simulated Treatment Learning can Predict Proteasome Inhibitor Benefit in Multiple Myeloma.

Purpose: Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors.

Experimental Design: We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures.