Biology subjects

Brüggemann, M.

Publications and source records attributed to Brüggemann, M..

6 recordsLinked to original sources

bioRxiv2026

ALLCatchR, a machine-learning classifier identifies now 20 T-ALL subtypes across cohorts and age groups

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2,314 transcriptomes (15 cohorts, age: 0.8 to 90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (ETP-like) definition and resolved the LMO2{gamma}{delta} -like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine learning classifier based on ALLCatchR, our B-ALL classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n=203). Testing the classifier on a second hold-out data set (n=265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene expression cluster markedly enriched (P<0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts representing 8.9% of adults and 39.5% of patients aged >50 years. We advanced ALLCatchR, as a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts. Key PointsO_LIEstablished using 2,314 T-ALL transcriptomes, ALLCatchR2 assigns 20 RNA-Seq subtypes and their developmental underpinnings across ages. C_LIO_LIClonal hematopoiesis-related T-ALL (DNMT3A, IDH2 R140Q) defines an immature gene-expression cluster in [~]40% of patients aged >50 years. C_LI

bioinformatics↗

bioRxiv2025

Intra-subtype heterogeneity shapes treatment response in KMT2A-rearranged ALL across all age groups

BackgroundKMT2A-rearranged B-cell acute lymphoblastic leukemia (KMT2Ar B-ALL) exhibits significant heterogeneity in age of onset, developmental origins, and clinical outcomes. The interplay of individual factors influencing early treatment response within this high-risk molecular subtype remains poorly elucidated. We aimed to comprehensively assess how leukemic developmental state, fusion partner, and patient age jointly influence early treatment response and drug sensitivity. MethodsTo identify determinants of early treatment response to induction chemotherapy, we analysed 465 KMT2Ar B-ALL cases spanning a wide age range (1 month to 89 years) by integrating transcriptomic and genomic profiling with functional drug response and measurable residual disease (MRD) kinetics. Transcriptomic profiling was used to derive a developmental maturity score based on proximity to physiological B-cell differentiation from a normal B-lymphopoiesis reference. Using an ordinal regression model we identify gene expression programs associated with early MRD response. ResultsWe observed a strong inverse correlation between MRD clearance with advancing age (p=2.1E-04), proximity to early B-cell-precursor developmental state (low maturity score, p=1.3E-03) and AFF1 as fusion partner (p=7.0E-04). A combined model confirmed the predominate impact of both maturity and KMT2A fusion partner on MRD response, supporting the concept that the cells developmental state defines therapy response. Gene expression analysis identified cellular traits that relate to MRD response (e.g. chromatin organization, immune modulation and proliferation). This gene expression classifier grouped cases by MRD response but also by ex-vivo induction drug sensitivity. Notably, good responders to ex-vivo induction drugs were characterized by a higher maturity score (p=1.8E-03), whereas for less mature KMT2Ar B-ALL cases response profiles suggested higher Venetoclax sensitivity. ConclusionsOur study provides an integrative framework linking developmental phenotype, fusion partner, and MRD kinetics across the full age spectrum of KMT2Ar B-ALL. The maturity score, derived from bulk transcriptome data, offers a biologically relevant predictor of early treatment response and drug sensitivity. These insights may support future risk-adapted strategies and therapeutic targeting, particularly in immature KMT2Ar B-ALL.

cancer biology↗

bioRxiv2020

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity. Author summaryThe individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

evolutionary biology↗

bioRxiv2019

The RNA-binding ubiquitin ligase MKRN1 functions in ribosome-associated quality control of poly(A)-translation

Cells have evolved quality control mechanisms to ensure protein homeostasis by detecting and degrading aberrant mRNAs and proteins. A common source of aberrant mRNAs is premature polyadenylation, which can result in non-functional protein products. Translating ribosomes that encounter poly(A) sequences are terminally stalled, followed by ribosome recycling and decay of the truncated nascent polypeptide via the ribosome-associated quality control (RQC). Here, we demonstrate that the conserved RNA-binding E3 ubiquitin ligase Makorin Ring Finger Protein 1 (MKRN1) promotes ribosome stalling at poly(A) sequences during RQC. We show that MKRN1 interacts with the cytoplasmic poly(A)-binding protein (PABP) and is positioned upstream of poly(A) tails in mRNAs. Ubiquitin remnant profiling uncovers PABP and ribosomal protein RPS10, as well as additional translational regulators as main ubiquitylation substrates of MKRN1. We propose that MKRN1 serves as a first line of poly(A) recognition at the mRNA level to prevent production of erroneous proteins, thus maintaining proteome integrity.

molecular biology↗

bioRxiv2018

Makorin1 controls embryonic patterning by alleviating Bruno-mediated repression of oskar translation

Makorins are evolutionary conserved proteins that contain C3H-type zinc finger modules and a RING E3 ubiquitin ligase domain. In Drosophila maternal Makorin 1 (Mkrn1) has been linked to embryonic patterning and germ cell specification. Here, we show that Mkrn1 is required for translational activation of oskar, whose product is critical for axis specification and germ plasm assembly. We demonstrate that Mkrn1 interacts with poly(A) binding protein (pAbp) and binds osk 3 UTR in a region adjacent to A-rich sequences. This binding site also overlaps with Bruno (Bru) responsive elements (BREs), which regulate osk translation. We observe increased association of the translational repressor Bru with osk mRNA upon depletion of Mkrn1, implying that the two proteins compete for osk binding. Consistently, reducing Bru dosage partially rescues viability and Osk protein level in ovaries from Mkrn1 females. We conclude that Mkrn1 controls embryonic patterning and germ cell formation by specifically activating osk translation via displacing Bru from its 3 UTR.

developmental biology↗

bioRxiv2018

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia

Recent studies implicated that long non-coding RNAs (lncRNAs) may play a role in the progression and development of acute lymphoblastic leukemia, however, this role is not yet clear. In order to unravel the role of lncRNAs associated with B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) subtypes, we performed transcriptome sequencing and DNA methylation array across 82 BCP-ALL samples from three molecular subtypes (DUX4, Ph-like, and Near Haploid or High Hyperdiploidy). Unsupervised clustering of BCP-ALL samples on the basis of their lncRNAs on transcriptome and DNA methylation profiles revealed robust clusters separating three molecular subtypes. Using extensive computational analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific lncRNAs with altered expression and methylation patterns from three subtypes of BCP-ALL. By analyzing the co-expression of subtype-specific lncRNAs and protein-coding genes, we inferred key molecular processes in BCP-ALL subtypes. A strong correlation was identified between the DUX4 specific lncRNAs and activation of TGF-{beta} and Hippo signaling pathways. Similarly, Ph-like specific lncRNAs were correlated with genes involved in activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific differentially expressed lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we showed a set of epigenetically altered lncRNAs facilitating the expression of tumor genes located at their cis location. Overall, our study provides a comprehensive set of novel subtype and relapse-specific lncRNAs in BCP-ALL. Our findings suggest a wide range of molecular pathways are associated with lncRNAs in BCP-ALL subtypes and provide a foundation for functional investigations that could lead to new therapeutic approaches.\n\nAuthor SummaryAcute lymphoblastic leukemia is a heterogeneous blood cancer, with multiple molecular subtypes, and with high relapse rate. We are far from the complete understanding of the rationale behind these subtypes and high relapse rate. Long non-coding (lncRNAs) has emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. LncRNAs does not code for proteins and represent around 70% of human transcripts. Recently, there are a number of studies used lncRNAs expression profile in the classification of various cancers subtypes and displayed their correlation with genomic, epigenetic, pathological and clinical features in diverse cancers. Therefore, lncRNAs can account for heterogeneity and has independent prognostic value in various cancer subtypes. However, lncRNAs defining the molecular subtypes of BCP-ALL are not portrayed yet. Here, we describe a set of relapse and subtype-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining how lncRNAs are involved in the pathogenesis of disease and the relevance in the stratification of BCP-ALL subtypes.

cancer biology↗