In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity

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Abstract

Early T precursor acute lymphoblastic leukemia (ETP-ALL) exhibits poor clinical outcomes and high relapse rates following conventional chemotherapeutic protocols. Extensive developmental flexibility of the multipotent ETP-ALL blasts with considerable intra-population heterogeneity in terms of immunophenotype and prognostic parameters might be a target for novel therapeutic interventions. Using a public gene expression dataset (GSE28703) from NCBI GEO DataSets with 12 ETP-ALL and 40 non-ETP-ALL samples, such heterogeneity was found to be reflected in their transcriptome as well. Hub genes were identified from the STRING-derived functional interaction network of genes showing differential expression between ETP-ALL and non-ETP-ALL as well as variable expression across ETP-ALL. Nine genes (KIT, HGF, NT5E, PROM1, CD33, ANPEP, CDH2, IL1B, and CXCL2) among the hubs were further validated as possible diagnostic ETP-ALL markers using another gene expression dataset (GSE78132) with 17 ETP-ALL and 27 non-ETP-ALL samples. Linear dimensionality reduction analysis with the expression levels of the hub genes in ETP-ALL revealed their divergent inclinations towards different hematopoietic lineages, proposing them as novel indicators of lineage specification in the incompletely differentiated ETP-ALL blasts. This further led to the formulation of a personalized lineage score calculation algorithm, which uncovered a considerable B-lineage-bias in a substantial fraction of ETP-ALL subjects from the GSE28703 and GSE78132 cohorts. In addition, STRING-derived physical interactome of the potential biomarkers displayed complete segregation of the B-lineage-skewed markers from other lineage-associated factors, highlighting their distinct functionality and possible druggability in ETP-ALL. A panel of these biomarkers might be useful in pinpointing the dominant lineage specification programmes in the ETP-ALL blasts on a personalized level, urging the development of novel lineage-directed precision therapies as well as repurposing of existing therapies against leukemia of different hematopoietic lineages; which might overcome the drawbacks of conventional chemotherapy.

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Mukherjee, S., Kar, A., Paul, P., Dey, S., Biswas, A., & Barik, S. (2022). In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity. Frontiers in Cell and Developmental Biology, 10. https://doi.org/10.3389/fcell.2022.899752

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