Lennart A. Kester

The paper uses lineage tracing and single-cell RNA sequencing to show that rhabdoid tumors converge on a limited set of transcriptional states despite clonal diversity.

The paper introduces ALLCatchR2, a machine learning classifier that identifies 20 T-ALL subtypes across multiple cohorts and age groups with high accuracy.

This study identifies and validates methylation biomarkers for YST subtype-specific liquid biopsy in germ cell tumors using DNA methylation data and qPCR validation.

This study identifies an immunosuppressive bone marrow microenvironment in RAS-mutant B-ALL, enriched for regulatory T cells, and suggests CTLA-4 blockade could enhance T-cell-engager efficacy.

This study presents the first comprehensive mutational and transcriptional analysis of B-cell precursor lymphoblastic lymphoma (BCP-LBL), revealing molecular similarities to BCP-ALL but with distinct mutation patterns in epigenetic modifiers.

This paper develops a deep learning model for intraoperative pediatric CNS tumor classification using rapid sequencing, achieving accurate results within 60-90 minutes.

This paper develops a method to detect tumor-specific gene fusions in pediatric cancer by integrating RNA-seq and WGS, identifying 165 high-confidence fusions and their structural variants.