Biology subjects

Kulozik, A.

Publications and source records attributed to Kulozik, A..

3 recordsLinked to original sources

bioRxiv2025

Phospho 394Y LCK flow-cytometry readout predicts dasatinib sensitivity in paediatric T-cell acute lymphoblastic leukaemia

Children with T-cell acute lymphoblastic leukaemia (T-ALL) who relapse or fail induction have poor outcomes. A subset of cases shows glucocorticoid resistance reversible by dasatinib through inhibition of LCK-dependent signalling. To identify a practical biomarker of dasatinib response, we compared in-vitro drug sensitivity with basal phosphorylation of pre-TCR pathway proteins in 28 paediatric T-ALL patient-derived xenografts (PDX). Phospho-flow cytometry quantified LCK (pY394), ZAP70 (pY319) and CD3{zeta} (pY142), normalised to internal controls. Dasatinib IC50 values correlated significantly with pLCK and pZAP70, with pLCK providing the best single-marker performance across clinically relevant thresholds. Logistic, ROC and precision-recall analyses confirmed that pLCK alone achieved excellent classification (AUC [≥] 0.9), while multivariate models added minimal predictive value. Model selection using LASSO and Bayesian Information Criterion further supported pLCK as the dominant predictor. These findings establish pLCK as a robust, scalable biomarker of dasatinib sensitivity suitable for diagnostic integration. A multicentre international validation programme is underway to harmonise pLCK assay protocols, expand testing across biobanks, and assess clinical feasibility in newly diagnosed and relapsed patients within the ALLTogether and HEM-iSMART trials respectively.

cancer biology↗

bioRxiv2024

Origins of de novo chromosome rearrangements unveiled by coupled imaging and genomics

Chromosomal instability results in widespread structural and numerical chromosomal abnormalities (CAs) during cancer evolution1-3. While CAs have been linked to mitotic errors resulting in the emergence of nuclear atypias4-7, the underlying processes and basal rates of spontaneous CA formation in human cells remain under-explored. Here we introduce machine learning-assisted genomics-and-imaging convergence (MAGIC), an autonomously operated platform that integrates automated live-cell imaging of micronucleated cells, machine learning in real-time, and single-cell genomics to investigate de novo CA formation at scale. Applying MAGIC to near-diploid, non-transformed cell lines, we track CA events over successive cell cycles, highlighting the common role of dicentric chromosomes as an initiating event. We determine the baseline CA rate, which approximately doubles in TP53-deficient cells, and show that chromosome losses arise more rapidly than gains. The targeted induction of DNA double-strand breaks along chromosomes triggers distinct CA processes, revealing stable isochromosomes, amplification and coordinated segregation of isoacentric segments in multiples of two, and complex CA outcomes, depending on the break location. Our data contrast de novo CA spectra from somatic mutational landscapes after selection occurred. The large-scale experimentation enabled by MAGIC provides insights into de novo CA formation, paving the way to unravel fundamental determinants of chromosome instability.

genomics↗

bioRxiv2019

Single cell tri-channel-processing reveals structural variation landscapes and complex rearrangement processes

Structural variation (SV), where rearrangements delete, duplicate, invert or translocate DNA segments, is a major source of somatic cell variation. It can arise in rapid bursts, mediate genetic heterogenity, and dysregulate cancer-related pathways. The challenge to systematically discover SVs in single cells remains unsolved, with copy-neutral and complex variants typically escaping detection. We developed single cell tri-channel-processing (scTRIP), a computational framework that jointly integrates read depth, template strand and haplotype phase to comprehensively discover SVs in single cells. We surveyed SV landscapes of 565 single cell genomes, including transformed epithelial cells and patient-derived leukemic samples, and discovered abundant SV classes including inversions, translocations and large-scale genomic rearrangements mediating oncogenic dysregulation. We dissected the molecular karyotype of the leukemic samples and examined their clonal structure. Different from prior methods, scTRIP also enabled direct detection and discrimination of SV mutational processes in individual cells, including breakage-fusion-bridge cycles. scTRIP will facilitate studies of clonal evolution, genetic mosaicism and somatic SV formation, and could improve disease classification for precision medicine.

genomics↗