Dataset Information

Integrated multi-omic analysis reveals novel subtype-specific regulatory interactions in pediatric B-cell acute lymphoblastic leukemia

ABSTRACT: In this study, we performed proteomic and phosphoproteomic profiling of B-cell acute lymphoblastic leukemia (B-ALL) samples collected at diagnosis and remission from pediatric leukemia patients with one of two subtypes of B-ALL: BCR::ABL1-like (Ph-like) and ETV6::RUNX1. We analyzed each dataset, as well as existing RNAseq data at diagnosis, to identify subtype-specific features, including increased calcium-dependent signaling in Ph-like samples. We then performed an integrated analysis of all three datasets, which enabled us to identify multiple layers of regulation, including subtype-specific phosphorylation of known cancer-associated proteins. Taken together, these data add to our understanding of the molecular profile of Ph-like and ETV6::RUNX1 B-ALL, demonstrating the utility of multi-omic comparison in pediatric leukemia subtype characterization.

INSTRUMENT(S): Orbitrap Ascend

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Midhat S Farooqi

PROVIDER: MSV000097955 | MassIVE | Wed May 21 11:48:00 BST 2025

SECONDARY ACCESSION(S): PXD064162

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining three published microarray datasets (PMIDs: 12086872, 12730115, 17002788) on 535 Caucasian samples and generating a new dataset on 100 Chinese children ALL samples, we were able to 1) identify a 62-gene classifier with 97.6% accuracy from the Caucasian samples and validated it on the completely independent set of 100 Chinese samples, 2) to uncover potential regulatory networks of ALL subtypes. The classifier we identified was so far the only one that could be applied directly to a single sample and sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL. A total of 100 Chinese pediatric acute lymphoblastic leukemia bone marrow (BM) samples were analyzed, together with five non-ALL BM samples as negative control (the five samples were combined to generate one control, see details in Li et al. Blood, 2009). The diagnosis of ALL was based on morphology, immunology, cytogenetic and molecular (MICM) classification. Cytogenetic ALL subtypes were identified experimentally by G-banding karyotype and multiplex nested RT-PCR. Among the 100 ALL patients, eleven relapsed within five years. All the samples, including those relapsed afterward, were from patients treated on BCH-2003 protocol and were extracted at their initial diagnosis. The five non-ALL BM samples were taken from the removed bones of patients who had plastic surgery for their bone deformity in Beijing Children’s Hospital. And the informed consent was obtained from parents, guardians, or patients (as appropriate). More details are available in Li et al. Gene expression-based classification and regulatory networks of pediatric acute lymphoblastic leukemia, Blood, 2009. We have generated these ALL and control samples to validate that a high accuracy subtype classifier that we identified could be applied directly to a single sample and sustained validation in a large independent patient group.

Dataset Information

Integrated multi-omic analysis reveals novel subtype-specific regulatory interactions in pediatric B-cell acute lymphoblastic leukemia

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets