Biology subjects

Cario, G.

Publications and source records attributed to Cario, G..

6 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↗

bioRxiv2023

Mutational and transcriptional landscape of pediatric B-cell precursor lymphoblastic lymphoma

Pediatric B-cell precursor (BCP) lymphoblastic malignancies are neoplasms with manifestation either in bone marrow/blood (BCP acute lymphoblastic leukemia, BCP-ALL) or less common in extramedullary tissue (BCP lymphoblastic lymphoma, BCP-LBL). Although both presentations are similar in morphology and immunophenotype molecular studies are virtually restricted to BCP-ALL so far. The lack of molecular studies on BCP-LBL is probably due to its rarity and the restriction to tiny, mostly formalin-fixed paraffin embedded (FFPE) tissues. Here we present the first comprehensive mutational and transcriptional analysis of what we consider the largest BCP-LBL cohort described to date (n=97). Whole exome sequencing indicates a mutational spectrum of BCP-LBL strikingly similar to that found in BCP-ALL. However, epigenetic modifiers were more frequently mutated in BCP-LBL, whereas BCP-ALL was more frequently affected by mutation in genes involved in B-cell development. Integrating copy number alterations, somatic mutations and gene expression by RNA-sequencing revealed virtually all molecular subtypes originally defined in BCP-ALL to be present in BCP-LBL too, with only 7% of lymphomas that were not assigned to a subtype. Therefore, the results here described may pave the way for molecular risk adapted treatment protocols for BCP-LBL patients. KeypointsComprehensive molecular characterization of B-cell precursor lymphoblastic lymphoma allows molecular subtyping analogous to leukemias Compared to leukemias, lymphomas show more alterations in epigenetic modifiers and less in B-cell development genes

cancer biology↗

bioRxiv2023

The gene expression classifier ALLCatchR identifies B-precursor ALL subtypes and underlying developmental trajectories across age

Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes, which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High confidence predictions were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared patterns of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.

cancer biology↗

bioRxiv2020

MondoA Drives Malignancy in cALL through Enhanced Adaptation to Metabolic Stress

Cancer cells are in most instances characterized by rapid proliferation and uncontrolled cell division. Hence, they must adapt to proliferation-induced metabolic stress through intrinsic or acquired anti-metabolic stress responses to maintain homeostasis and survival. One mechanism to achieve this is to reprogram gene expression in a metabolism-dependent manner. MondoA (also known as MLXIP), a member of the MYC interactome, has been described as an example of such a metabolic sensor. However, the role of MondoA in malignancy is not fully understood and the underlying mechanism in metabolic responses remains elusive. By assessing patient data sets we found that MondoA overexpression is associated with a worse survival in pediatric common acute lymphoblastic leukemia (cALL). Using CRISPR/Cas9 and RNA interference approaches, we observed that MondoA depletion reduces transformational capacity of cALL cells in vitro and dramatically inhibits malignant potential in an in vivo mouse model. Interestingly, reduced expression of MondoA in patient data sets correlated with enrichment in metabolic pathways. The loss of MondoA correlated with increased tricarboxylic acid (TCA) cycle activity. Mechanistically, MondoA senses metabolic stress in cALL cells by restricting oxidative phosphorylation through reduced PDH activity. Glutamine starvation conditions greatly enhance this effect and highlight the inability to mitigate metabolic stress upon loss of MondoA in cALL. Our findings give a novel insight into the function of MondoA in pediatric cALL and support the notion that MondoA inhibition in this entity offers a therapeutic opportunity and should be further explored. Key PointsMondoA maintains aggressiveness and leukemic burden in common ALL, modulating metabolic stress response.

cancer biology↗