Biology subjects

Herold, T.

Publications and source records attributed to Herold, T..

8 recordsLinked to original sources

bioRxiv2025

A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist

Preclinical cancer research requires robust model systems, especially for poor prognosis entities like acute myeloid leukemia (AML), a highly aggressive blood cancer. Here, primary tumor cells from 137 AML patients of all age groups were transplanted into immune compromised mice to generate patient-derived xenografts (PDX). From these, 23 models enable robust, virtually endless serial re-transplantation and are amenable to lentiviral genetic engineering (*PDX AML models). These models primarily originate from patients with highly aggressive, relapsed disease. Comprehensive genomic, transcriptomic, and epigenomic analyses confirmed that they replicate primary AML biology more faithfully than conventional cell lines. Notably, *PDX AML models include AML subgroups that are underrepresented or absent in existing model systems, such as cytogenetically normal or IDH1/2-mutant AML. They withstand freeze-thaw cycles, making them suitable for broad distribution and reproducibility across research institutions. Luciferase-based in vivo imaging enables real-time monitoring of tumor progression and treatment responses in preclinical trials. Surprisingly, long-term treatment, including repeated cytarabine therapy over a period of one year, showed a gradual reduction in leukemia cell proliferation, which decreased continuously after each treatment block. Collectively, our *PDX models represent a robust, versatile, and relevant platform that holds great promise to accelerate translational research for the benefit of cancer patients. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/677299v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1ead31eorg.highwire.dtl.DTLVardef@1ca702corg.highwire.dtl.DTLVardef@18a7137org.highwire.dtl.DTLVardef@edbe6_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIWe present new robust AML PDX models covering subgroups for which no cell lines exist for use in various ex vivo and in vivo applications. C_LIO_LI*PDX models enable serial transplantation, genetic engineering and better representation of primary AML biology than cell lines. C_LIO_LIOne-year in vivo trials mimicking clinical chemotherapy showed surprising gradual decline in leukemia growth after each treatment block. C_LI

cancer biology↗

bioRxiv2024

Pyjacker identifies enhancer hijacking events in acute myeloid leukemia including MNX1 activation via deletion 7q

Acute myeloid leukemia with complex karyotype (ckAML) is characterized by high genomic complexity, including frequent TP53 mutations and chromothripsis. We hypothesized that the numerous genomic rearrangements could reposition active enhancers near proto-oncogenes, leading to their aberrant expression. We developed pyjacker, a computational tool for the detection of enhancer hijacking events, and applied it to a cohort of 39 ckAML samples. Pyjacker identified motor neuron and pancreas homeobox 1 (MNX1), a gene aberrantly expressed in 1.4% of AML patients, often as a result of del(7)(q22q36) associated with hijacking of a CDK6 enhancer. MNX1-activated cases show significant co-occurrence with BCOR mutations and a gene signature shared with t(7;12)(q36;p13) pediatric AML. We demonstrated that MNX1 is a dependency gene, as its knockdown in a xenograft model reduces leukemia cell fitness. In conclusion, enhancer hijacking is a frequent mechanism for oncogene activation in AML. Statement of significanceThis study examines the consequences of structural alterations and demonstrates that proto-oncogene activation by enhancer hijacking is an overlooked pathomechanism in AML. MNX1 overexpression demonstrates that deletions on chromosome 7q can not only lead to haploinsufficiency, but also to activation of oncogenes by enhancer hijacking, providing a novel leukemogenic mechanism.

cancer biology↗

bioRxiv2022

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation.

neuroscience↗

bioRxiv2020

In vivo inducible reverse genetics in patients' tumors to identify individual therapeutic targets

High-throughput sequencing describes multiple alterations in each tumor (1), but their functional relevance is often unclear. Clinic-close, individualized molecular model systems are required for functional validation and to identify therapeutic targets of high significance for each patient (2). Here, we established a Cre-ERT2-loxP based inducible RNAi-mediated gene silencing system in patient-derived xenograft (PDX) models of acute leukemias in vivo. Mimicking anti-cancer therapy in patients, gene inhibition was initiated in mice harboring orthotopic tumors. Fluorochrome guided, competitive in vivo trials identified a major tumor-maintaining potency of the MLL-AF4 fusion protein and validated MCL1 as vulnerability in some, but not all patients tumors. We could prove DUX4 to play an essential role in patients leukemias carrying the recently described DUX4-IGH translocation. By individualizing functional genomics in established tumors in vivo, our technique decisively complements the value chain of precision oncology. Being broadly applicable to tumors of all kinds, it will considerably reinforce personalizing anti-cancer treatment in the future.

cancer biology↗

bioRxiv2020

RNA splicing alterations induce a cellular stress response associated with poor prognosis in AML

RNA splicing is a fundamental biological process that generates protein diversity from a finite set of genes. Recurrent somatic mutations of splicing factor genes are relatively uncommon in Acute Myeloid Leukemia (AML, < 20%). We examined whether RNA splicing differences exist in AML even in the absence of splicing factor mutations. Analyzing RNA-seq data from two independent cohorts of AML patients, we identified recurrent differential alternative splicing between patients with poor and good prognosis. These alternative splicing events occurred even in patients without any discernible splicing factor mutations. The alternative splicing events recurrently occurred in genes involved in specific molecular functions, primarily related to protein translation. Developing informatics tools to predict the functional impact of alternative splicing on the translated protein, we discovered that ~45% of the splicing events directly affected highly conserved protein domains. Several splicing factors were themselves misspliced in patients, and the splicing of their target transcripts were also altered. By studying differential gene expression in the same patients, we identified that alternative splicing of protein translation genes in ELNAdv patients resulted in the induction of an integrated stress response and up- regulation of inflammation-related genes. Lastly, using machine learning techniques, we identified a set of four genes whose alternative splicing can refine the accuracy of existing risk prognosis schemes and validated it in a completely independent cohort. Our discoveries therefore identify aberrant alternative splicing as a molecular feature of adverse AML with clinical relevance.

cancer biology↗

bioRxiv2019

SAMHD1 is a key regulator of the lineage-specific response of acute lymphoblastic leukaemias to nelarabine

The nucleoside analogue nelarabine, the prodrug of arabinosylguanine (AraG), has been known for decades to be effective against acute lymphoblastic leukaemias of T-cell (T-ALL), but not of B-cell (B-ALL) origin. The mechanisms underlying this lineage-specific drug sensitivity have remained elusive. Data from pharmacogenomics studies and from a panel of ALL cell lines revealed an inverse correlation of SAMHD1 expression and nelarabine sensitivity. SAMHD1 can hydrolyse and thus inactivate triphosphorylated nucleoside analogues. Transcriptomic and protein expression profiling of cell lines and patient-derived leukaemic blasts revealed lower SAMHD1 abundance in T-ALL than in B-ALL. Mechanistically, SAMHD1 promoter methylation strongly correlated with suppressed SAMHD1 expression, while T-ALL cells did not display increased global DNA methylation. Targeted SAMHD1 degradation using virus-like particles containing Vpx sensitised B-ALL cells to AraG, while ectopic SAMHD1 expression in SAMHD1-null T-ALL cells induced AraG resistance. SAMHD1 had a larger impact on cytarabine activity than on nelarabine/ AraG activity in acute myeloid leukaemia (AML) cells, but more strongly affected nelarabine/ AraG activity in ALL cells. This indicates a critical role of the cancer entity. In conclusion, lineage-specific differences in SAMHD1 promoter methylation and, in turn, SAMHD1 expression levels determine ALL cell response to nelarabine. SAMHD1 is a potential biomarker for the identification of ALL patients likely to benefit from nelarabine therapy and a therapeutic target to overcome nelarabine resistance.

pharmacology and toxicology↗

bioRxiv2019

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.

bioinformatics↗

bioRxiv2019

KLF4 acts as a tumor suppressor in human B-cells and patients’ B-cell leukemias growing in mice

Clinic-close methods are not available that prioritize and validate potential therapeutic targets in individual tumors from the vast bulk of descriptive expression data. We developed a novel technique to express transgenes in established patient-derived xenograft (PDX) models in vivo to fill this gap. With this technique at hand, we analyzed the role of transcription factor Kruppel-like factor 4 (KLF4) in B-cell acute lymphoblastic leukemia (B-ALL) PDX models at different disease stages. In competitive pre-clinical in vivo trials, we found that re-expression of wild type KLF4 reduced leukemia load in PDX models of B-ALL, with strongest effects after conventional chemotherapy at minimal residual disease (MRD). A non-functional KLF4 mutant had no effect in this model. Re-expressing KLF4 sensitized tumor cells in the PDX model towards systemic chemotherapy in vivo. Of major translational relevance, Azacitidine upregulated KLF4 levels in the PDX model and a KLF4 knockout reduced Azacitidine-induced cell death, suggesting that Azacitidine can regulate KLF4 re-expression. These results support applying Azacitidine in patients with B-ALL to regulated KLF4 as a therapeutic option. Taken together, our novel technique allows studying the function of dysregulated genes in a highly clinic-related, translational context and testing clinically applicable drugs in a relevant pre-clinical model.

cancer biology↗