Biology subjects

Chroni, A.

Publications and source records attributed to Chroni, A..

3 recordsLinked to original sources

bioRxiv2024

ERAP1 activity modulates the immunopeptidome but also affects the proteome, metabolism and stress responses in cancer cells

Endoplasmic reticulum (ER) aminopeptidase 1 (ERAP1) metabolizes peptides inside the ER and shapes the peptide repertoire available for binding to Major Histocompatibility Complex Class I molecules (MHC-I). Moreover, it may have additional effects on cellular homeostasis, which have not been explored. To address these questions, we used both genetic silencing of ERAP1 expression as well as treatment with a selective allosteric ERAP1 inhibitor to probe changes in the immunopeptidome and proteome of the A375 melanoma cancer cell line. We observed significant immunopeptidome shifts with both methods of functional ERAP1 disruption, which were distinct for each method. Both methods of inhibition led to an enhancement, albeit slight, in tumor cell killing by stimulated human PBMCs and in significant proteomic alterations in pathways related to metabolism and cellular stress. Similar proteomic changes were also observed in the leukemia cell line THP-1. Biochemical analyses suggested that ERAP1 inhibition affected sensitivity to ER stress, reactive oxygen species production and mitochondrial metabolism. Although the proteomics shifts were significant, their potential in shaping immunopeptidome shifts was limited since only 15.8% of differentially presented peptides belonged to proteins with altered expression and only 5.0% of proteins with altered expression were represented in the immunopeptidome shifts. Taken together, our findings suggest that modulation of ERAP1 activity can generate unique immunopeptidomes, mainly due to altered peptide processing in the ER, but also induce changes in the cellular proteome and metabolic state which may have further effects on tumor cells.

biochemistry↗

bioRxiv2024

The splicing modulator CLK1 is a candidate oncogenic dependency in pediatric high-grade gliomas

BackgroundPediatric brain tumors are the leading cause of disease-related mortality in children, yet many aggressive tumors lack effective therapies. RNA splicing is a hallmark of cancer, but it has not yet been systematically studied in pediatric brain tumors. MethodsWe analyzed 729 pediatric brain tumors spanning histologies and molecular subtypes to quantify differential tumor splicing. We developed the Splicing Burden Index (SBI) to enable cross-sample comparisons and performed hierarchical clustering of highly variable splice events to define splicing-informed tumor groups. These were integrated with clinical outcomes, pathway activity, and proteogenomic data. Recurrent splice events were prioritized for predicted functional impact, and in vitro perturbation studies were performed targeting the splicing kinase CDC-like kinase 1 (CLK1). ResultsSBI revealed substantial inter- and intra-histology heterogeneity. Clusters were enriched for histologies and molecular subtypes, several of which were independently associated with survival beyond histology and clinical covariates. Spliceosome pathway activity varied across clusters and was associated with worse survival, yet was not correlated with SBI, indicating distinct dimensions of splicing dysregulation. Functional prioritization identified a recurrent in CLK1 exon 4, required for canonical kinase activity. CLK1 exon 4 inclusion followed an oncofetal pattern and showed context-dependent associations with outcome distinct from total CLK1 expression. Pharmacologic inhibition and exon 4-specific perturbation of CLK1 reduced tumor cell viability and disrupted cancer-relevant splicing and transcriptional programs. ConclusionsThis study systematically characterizes splicing in pediatric brain tumors, identifies splicing-informed subgroups, and prioritizes CLK1 exon 4 as an oncofetal tumor-specific event, motivating further preclinical exploration. Key Points[bullet] Splicing analysis of 729 pediatric CNS tumors identifies splicing-defined clusters. [bullet]CLK1 exon 4 inclusion is widespread and developmentally regulated. [bullet]Exon-level CLK1 regulation shows context-dependent links to prognosis in aggressive CNS tumors.

genomics↗

bioRxiv2024

The Open Pediatric Cancer Project

BackgroundIn 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we present an extension of the OpenPBTA called the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). FindingsWe utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCIs pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research-grade integrated diagnoses for these tumors. ConclusionsOpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

genomics↗