Abstract 2607: Multi-omic profiling reveals subtype-specific signaling mechanisms in pediatric B-cell acute lymphoblastic leukemias

Abstract Genetic subtyping has improved the accuracy of diagnosis, and in turn outcomes, for children with B-cell acute lymphoblastic leukemia (B-ALL). Currently, clinical subtyping is based primarily on mutations in, or fusions involving, known driver genes. This approach is restricted in its power, particularly for subtypes such as BCR::ABL1-like B-ALL, which is largely characterized by its transcriptional similarity to BCR::ABL1+ cases, but has numerous possible driver fusions. Moreover, even transcriptomic profiling does not fully capture the molecular processes driving leukemogenesis, which are carried out by proteins, yet proteomics has been relatively unexplored in pediatric B-ALL to date. To bridge this gap, we compare genomic, transcriptomic, proteomic, and phosphoproteomic profiles between pediatric patients with BCR::ABL1-like and ETV6::RUNX1 B-ALL to characterize subtype-specific molecular mechanisms. For each subtype, we selected at least five blood or bone marrow aspirates collected from patients at diagnosis. We combined genomic findings from clinical assays (karyotyping, FISH, microarray, and whole exome sequencing) with total RNA sequencing (Illumina). Additionally, we generated global proteomic and phosphoproteomic profiles using label-free quantitation. Peptides were enriched for phosphorylation using the SMOAC method, and all samples were run on an Orbitrap Ascend mass spectrometer (Thermo) using data dependent acquisition. Differential gene expression analysis comparing the two subtypes showed elevated expression of CRLF2 in BCR::ABL1-like cases, consistent with clinical data and prior subtype characterization. Pathway enrichment of the 684 differentially expressed genes (p < 0.05) revealed upregulation of cell differentiation in ETV6::RUNX1 samples, but otherwise provided limited mechanistic insights. Differentially expressed proteins (n = 644, p < 0.05) showed upregulation of translation-related processes in ETV6::RUNX1 samples, and calcium-dependent signaling in BCR::ABL1-like samples. Kinase activity prediction based on phosphoproteomic data showed significantly higher activity of calcium/calmodulin-dependent protein kinases (CAMKs), including CAMK2B and CAMK4, in BCR::ABL1-like samples, which is not observed in corresponding gene expression between subtypes. There is a growing body of work suggesting a role for CAMKs in leukemias, though they have not previously been implicated in BCR::ABL1-like pediatric B-ALLs. Using a multi-omic profiling approach, we demonstrate that proteomic and phosphoproteomic data reveal previously undescribed subtype-specific activation of signaling cascades in pediatric B-ALL, which are not captured by genomic or transcriptomic assays. These insights add to our understanding of subtype-specific mechanisms, and can facilitate the identification of novel treatment targets. Citation Format: Irina Pushel, Zachary Clark, Michaella J. Rekowski, Lisa A. Lansdon, Byunggil Yoo, Keith J. August, Alan S. Gamis, Nicole M. Wood, Michael P. Washburn, Midhat S. Farooqi. Multi-omic profiling reveals subtype-specific signaling mechanisms in pediatric B-cell acute lymphoblastic leukemias [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2607.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-2607
Primary Topic
Acute Lymphoblastic Leukemia research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 2607: Multi-omic profiling reveals subtype-specific signaling mechanisms in pediatric B-cell acute lymphoblastic leukemias

Irina Pushel, Michael P. Washburn, Keith J. August, Midhat S. Farooqi et al.
Cancer Research
Acute Lymphoblastic Leukemia research
article

Abstract 2607: Multi-omic profiling reveals subtype-specific signaling mechanisms in pediatric B-cell acute lymphoblastic leukemias

Irina Pushel, Michael P. Washburn, Keith J. August, Midhat S. Farooqi, Byunggil Yoo, Alan S. Gamis, Michaella J. Rekowski, Lisa A. Lansdon, Nicole M. Wood, Zachary Clark
article en

Abstract

Abstract Genetic subtyping has improved the accuracy of diagnosis, and in turn outcomes, for children with B-cell acute lymphoblastic leukemia (B-ALL). Currently, clinical subtyping is based primarily on mutations in, or fusions involving, known driver genes. This approach is restricted in its power, particularly for subtypes such as BCR::ABL1-like B-ALL, which is largely characterized by its transcriptional similarity to BCR::ABL1+ cases, but has numerous possible driver fusions. Moreover, even transcriptomic profiling does not fully capture the molecular processes driving leukemogenesis, which are carried out by proteins, yet proteomics has been relatively unexplored in pediatric B-ALL to date. To bridge this gap, we compare genomic, transcriptomic, proteomic, and phosphoproteomic profiles between pediatric patients with BCR::ABL1-like and ETV6::RUNX1 B-ALL to characterize subtype-specific molecular mechanisms. For each subtype, we selected at least five blood or bone marrow aspirates collected from patients at diagnosis. We combined genomic findings from clinical assays (karyotyping, FISH, microarray, and whole exome sequencing) with total RNA sequencing (Illumina). Additionally, we generated global proteomic and phosphoproteomic profiles using label-free quantitation. Peptides were enriched for phosphorylation using the SMOAC method, and all samples were run on an Orbitrap Ascend mass spectrometer (Thermo) using data dependent acquisition. Differential gene expression analysis comparing the two subtypes showed elevated expression of CRLF2 in BCR::ABL1-like cases, consistent with clinical data and prior subtype characterization. Pathway enrichment of the 684 differentially expressed genes (p < 0.05) revealed upregulation of cell differentiation in ETV6::RUNX1 samples, but otherwise provided limited mechanistic insights. Differentially expressed proteins (n = 644, p < 0.05) showed upregulation of translation-related processes in ETV6::RUNX1 samples, and calcium-dependent signaling in BCR::ABL1-like samples. Kinase activity prediction based on phosphoproteomic data showed significantly higher activity of calcium/calmodulin-dependent protein kinases (CAMKs), including CAMK2B and CAMK4, in BCR::ABL1-like samples, which is not observed in corresponding gene expression between subtypes. There is a growing body of work suggesting a role for CAMKs in leukemias, though they have not previously been implicated in BCR::ABL1-like pediatric B-ALLs. Using a multi-omic profiling approach, we demonstrate that proteomic and phosphoproteomic data reveal previously undescribed subtype-specific activation of signaling cascades in pediatric B-ALL, which are not captured by genomic or transcriptomic assays. These insights add to our understanding of subtype-specific mechanisms, and can facilitate the identification of novel treatment targets. Citation Format: Irina Pushel, Zachary Clark, Michaella J. Rekowski, Lisa A. Lansdon, Byunggil Yoo, Keith J. August, Alan S. Gamis, Nicole M. Wood, Michael P. Washburn, Midhat S. Farooqi. Multi-omic profiling reveals subtype-specific signaling mechanisms in pediatric B-cell acute lymphoblastic leukemias [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2607.

Cancer ResearchVol. 85(8_Supplement_1)
Children's Mercy Hospital (US), University of Kansas Medical Center (US)
Openalex Percentile: Top 88%
Acute Lymphoblastic Leukemia research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.