Daniel Stekhoven

Last Name

Stekhoven

First Name

Daniel

ORCID

0000-0003-3163-3161

Organisational unit

02892 - NEXUS Personalized Health / NEXUS Personalized Health

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Publications 1 - 6 of 6

CIViCutils: Matching and downstream processing of clinical annotations from CIViC
Rosano González, Lourdes; Thankam Sreedharan, Vipin; Hanns, Antoine; et al. (2023)
F1000Research
Background: With the advent of next-generation sequencing, profiling the genetic landscape of tumors entered clinical diagnostics, bringing the resolution of precision oncology to unprecedented levels. However, the wealth of information generated in a sequencing experiment can be difficult to manage, especially if hundreds of mutations need to be interpreted in a clinical context. Dedicated methods and databases are required that assist in interpreting the importance of a mutation for disease progression, prognosis, and with respect to therapy. Here, the CIViC knowledgebase is a valuable curated resource, however, utilizing CIViC in an efficient way for querying a large number of mutations needs sophisticated downstream methods. Methods: To this end, we have developed CIViCutils, a Python package to query, annotate, prioritize, and summarize information from the CIViC database. Our package provides functionality for performing high-throughput searches in CIViC, automatically matching clinical evidence to input variants, evaluating the accuracy of the extracted variant matches, fully exploiting the available disease-specific information according to cancer types of interest, and in-silico predicting drug-target interactions tailored to individual patients. Results: CIViCutils allows the simultaneous query of hundreds of mutations and is able to harmonize input across different nomenclatures. Moreover, it supports gene expression data, single nucleotide mutations, as well as copy number alterations as input. We utilized CIViCutils in a study on the bladder cancer cohort from The Cancer Genome Atlas (TCGA-BLCA), where it helped to extract clinically relevant mutations for personalized therapy recommendation. Conclusions: CIViCutils is an easy-to-use Python package that can be integrated into workflows for profiling the genetic landscape of tumor samples. It streamlines interpreting large numbers of variants with retrieving and processing curated CIViC information.
Drug screening and genome editing in human pancreatic cancer organoids identifies drug-gene interactions and candidates for off-label therapy
Hirt, Christian; Booij, Tijmen H.; Grob, Linda; et al. (2022)
Cell Genomics
Pancreatic cancer (PDAC) is a highly aggressive malignancy for which the identification of novel therapies is urgently needed. Here, we establish a human PDAC organoid biobank from 31 genetically distinct lines, covering a representative range of tumor subtypes, and demonstrate that these reflect the molecular and phenotypic heterogeneity of primary PDAC tissue. We use CRISPR-Cas9 genome editing and drug screening to characterize drug-gene interactions with ARID1A and BRCA2. We find that missense, but not frameshift, mutations in the PDAC driver gene ARID1A are associated with increased sensitivity to the kinase inhibitors dasatinib (p < 0.0001) and VE-821 (p < 0.0001). We further conduct an automated drug-repurposing screen with 1,172 FDA-approved compounds, identifying 26 compounds that effectively kill PDAC organoids, including 19 chemotherapy drugs currently approved for other cancer types. We validate the activity of these compounds in vitro and in vivo. The in vivo validated hits include emetine and ouabain, compounds that are approved for non-cancer indications and that perturb the ability of PDAC organoids to respond to hypoxia. Our study provides proof-of-concept for advancing precision oncology and for identifying candidates for drug repurposing via genome editing and drug screening in tumor organoid biobanks.
scROSHI: robust supervised hierarchical identification of single cells
Prummer, Michael; Bertolini, Anne; Bosshard, Lars; et al. (2023)
NAR Genomics and Bioinformatics
Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.
gExcite — A start-to-end framework for single-cell gene expression, hashing, and antibody analysis
Grob, Linda; Bertolini, Anne; Carrara, Matteo; et al. (2023)
Bioinformatics
Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single-cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focussed on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene and antibody expression analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples.
Reassessment of the Collaborative Normal-Tension Glaucoma Study: Statistical Evidence and Implications for Current Management
Hanspeter E. Killer; Achmed Pircher; Daniel J. Stekhoven; et al. (2026)
Ophthalmology and Therapy
Introduction The Collaborative Normal-Tension Glaucoma Study (CNTGS) is frequently cited as evidence that a 30% reduction in intraocular pressure (IOP) slows progression in normal-tension glaucoma (NTG). This study re-examines the statistical methodology of CNTGS to assess how its conclusions are supported by the data. Methods This study reviews the CNTGS design with emphasis on survival analysis methodology, including the definition of time zero, censoring rules, and intention-to-treat (ITT) versus per-protocol comparisons. Particular attention is given to the post hoc redefinition of baseline and the handling of cataract-related visual decline, assessing their impact on the reported treatment effect. Results CNTGS shifted the analytical baseline for the treatment group to the point of IOP stabilization, thereby excluding early progression events and introducing immortal time bias. Additionally, cataract-related visual decline, more frequent in the treatment group, was censored rather than treated as a competing risk or time-dependent covariate. These methodological choices reduced the number of counted progression events in the treatment arm. Although the adjusted per-protocol analysis yielded a statistically significant treatment effect, this effect disappeared under the original ITT analysis, which included all randomized eyes from time zero and all progression events. Conclusion The potential treatment benefit reported in CNTGS depended largely on post hoc analytical modifications, whereas the original ITT analysis did not support a statistically significant effect of IOP reduction. These findings highlight the importance of transparent survival analysis methods and strict adherence to ITT principles in future NTG trials. Well-designed prospective studies that avoid immortal time bias and model treatment-related events appropriately are needed to clarify the true role of IOP reduction on NTG management.
Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults
Marcinkevičs, Ričards; Silva, Pamuditha N.; Hankele, Anna-Katharina; et al. (2023)
Frontiers in Immunology
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces B and T cell responses, contributing to virus neutralization. In a cohort of 2,911 young adults, we identified 65 individuals who had an asymptomatic or mildly symptomatic SARS-CoV-2 infection and characterized their humoral and T cell responses to the Spike (S), Nucleocapsid (N) and Membrane (M) proteins. We found that previous infection induced CD4 T cells that vigorously responded to pools of peptides derived from the S and N proteins. By using statistical and machine learning models, we observed that the T cell response highly correlated with a compound titer of antibodies against the Receptor Binding Domain (RBD), S and N. However, while serum antibodies decayed over time, the cellular phenotype of these individuals remained stable over four months. Our computational analysis demonstrates that in young adults, asymptomatic and paucisymptomatic SARS-CoV-2 infections can induce robust and long-lasting CD4 T cell responses that exhibit slower decays than antibody titers. These observations imply that next-generation COVID-19 vaccines should be designed to induce stronger cellular responses to sustain the generation of potent neutralizing antibodies.

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Daniel Stekhoven

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