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Galesloot, T. E.

Publications and source records attributed to Galesloot, T. E..

2 recordsLinked to original sources

Intravesical BCG in patients with non-muscle invasive bladder cancer induces trained immunity and decreases respiratory infections

Bacillus Calmette-Guerin (BCG) is recommended as intravesical immunotherapy to reduce the risk of tumor recurrence in patients with non-muscle invasive bladder cancer (NMIBC). Currently, it is unknown whether intravesical BCG application induces trained immunity. Here, we found that intravesical BCG does induce trained immunity based on an increased production of TNF and IL-1{beta} after heterologous ex-vivo stimulation of circulating monocytes 6- 12 weeks after intravesical BCG treatment; and a 37% decreased risk (OR 0.63 (95% CI 0.40- 1.01)) for respiratory infections in BCG-treated versus non-BCG-treated NMIBC patients. An epigenomics approach combining ChIP-sequencing and RNA-sequencing with in-vitro trained immunity experiments identified enhanced inflammasome activity in BCG-treated individuals. Finally, germline variation in genes that affect trained immunity was associated with recurrence and progression after BCG therapy in NMIBC, suggesting a link between trained immunity and oncological outcome.

immunology↗

Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

MotivationWith the increasing availability of genome-wide genetic data, methods to combine genetic variables with other sources of data in statistical models are required. This paper introduces quantitative genetic scoring (QGS), a dimensionality reduction method to create quantitative genetic variables representing arbitrary genetic regions. MethodsQGS is defined as the sum of absolute differences in the genetic sequence between a subject and a reference population. QGS properties such as distribution and sensitivity to region size were examined, and QGS was tested in six different existing genomic data sets of various sizes and various phenotypes. ResultsQGS can reduce genetic information by >98% yet explain phenotypic variance at low, medium, and high level of granularity. Associations based on QGS are independent of both size and linkage disequilibrium structure of the underlying region. In combination with stability selection, QGS finds significant results where a traditional genome-wide association approaches struggle. In conclusion, QGS preserves phenotypically significant genetic variance while reducing dimensionality, allowing researchers to include quantitative genetic information in any type of statistical analysis. Availabilityhttps://github.com/machine2learn/QGS Contactgido.schoenmacker@radboudumc.nl Supplemental informationSupplemental data are available online.

bioinformatics↗