Biology subjects

Cario, G.

Publications and source records attributed to Cario, G..

3 recordsLinked to original sources

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↗