Biology subjects

Lenk, L.

Publications and source records attributed to Lenk, L..

4 recordsLinked to original sources

bioRxiv2026

clinTALL: machine learning-driven multimodal subtypeclassification and treatment outcome prediction in pediatric T-ALL

BackgroundChildhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-cell precursor ALL, T-ALL lacks effective risk stratification strategies. A recent study has integrated whole genome and whole transcriptome data to define over 15 distinct molecular subtypes with prognostic significance. However, clinical translation of this knowledge remains challenging due to the complexity of interpreting high-dimensional multi-omics-based data. MethodsHere, we present clinTALL, a deep learning based multi-task pipeline for pediatric T-ALL subtype classification and treatment outcome estimation. The model integrates multimodal input data and uses a neural network architecture to generate a shared latent embedding for jointly learned multi-task prediction. The competing risk-based model was used to predict event-specific outcomes. The model was trained on a publicly available multimodal dataset comprising clinical, genomic and transcriptomic features of 1309 pediatric T-ALL samples. ResultsWe observed that the transcriptomic-only model achieved superior single modality results, with 92.2% accuracy for subtype prediction and a 65.9% concordance index (C-index) for event-free survival (EFS) in a cross-validation setup. Integrating all data modalities maintained high subtype classification accuracy (91.7%) and improved the overall concordance index for EFS estimation to 67.5%. The competing risk-based model enables accurate predictions of induction failure (C-index = 96.0%) and second malignant neoplasm (C-index = 62.1%). We validated molecular subtype predictions on an internal dataset of 120 pediatric T-ALL samples and obtained an accuracy of 81.8%. To facilitate the broad application of multi-omics based subtype prediction and treatment outcome inference, we provide clinTall as a Docker based application, allowing for user friendly access to the tool. The full source code of clinTALL is available on GitHub (https://github.com/UKWgenommedizin/clinTALL). ConclusionTogether, our machine learning-based framework allows for automated, accurate sub-type classification and treatment outcome inference using multimodal input data, advancing precision risk stratification for pediatric T-ALL.

bioinformatics↗

bioRxiv2025

A novel Fc-optimized antibody drug conjugate targeting CD7 for the therapy of T-cell acute lymphoblastic leukemia

While treatment for patients with T-cell acute lymphoblastic leukemia (T-ALL) has improved in the last decades, therapeutic options for patients refractory to standard therapy or with relapsing disease are limited. In particular, no immunotherapy option has been approved in T-ALL yet. Here, a novel dual antibody engineering approach for targeting CD7 was evaluated. The chimeric CD7 antibody chimTH69 was modified by Fc engineering to improve antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). In addition, it was conjugated to monomethyl auristatin E (MMAE), a microtubule-disrupting agent. The resulting Fc-optimized antibody-drug conjugate (ADC), designated chimTH69-DE-vcMMAE, showed a unique set of effector functions in vitro. It triggered ADCC by mononuclear cells at picomolar concentrations, mediated ADCP by macrophages and directly inhibited the growth of a panel of T-ALL cell lines by delivering the cytotoxic compound to induce G2 cell cycle arrest and apoptosis. In addition, due to its specific linker design, chimTH69-DE-vcMMAE demonstrated bystander killing activity against CD7-negative leukemia cells. In mice, CD7-directed therapy with chimTH69-DE-vcMMAE inhibited the growth of subcutaneous CCRF-CEM T-ALL xenografts. Moreover, chimTH69-DE-vcMMAE exerted strong antileukemic effects in a phase II-like patient-derived xenograft preclinical trial in pediatric and adult patients when applied in an experimental overt leukemia setting. ChimTH69-DE-vcMMAE induced minimal residual disease-negativity in one PDX model. These findings indicate that targeting CD7 with the novel Fc-optimized ADC is a potent strategy to trigger anti-leukemia responses and may open a novel therapeutic avenue for T-ALL treatment. Key PointA novel antibody drug conjugate targeting CD7 showed efficient anti-leukemia activity in preclinical models of T-ALL.

cancer biology↗

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

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↗