Table5_In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity.XLSX
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.
History
References
- https://doi.org//10.1182/blood.2019004547
- https://doi.org//10.1093/nar/gkw419
- https://doi.org//10.1093/nar/gky1076
- https://doi.org//10.4049/jimmunol.1701186
- https://doi.org//10.4049/jimmunol.1700498
- https://doi.org//10.1093/nar/gks1193
- https://doi.org//10.1038/nature06840
- https://doi.org//10.1111/j.2517-6161.1995.tb02031.x
- https://doi.org//10.3389/fped.2016.00049
- https://doi.org//10.1016/j.ccell.2018.01.006
- https://doi.org//10.3390/ijms18081652
- https://doi.org//10.1177/2040620720929475
- https://doi.org//10.1186/1471-2105-14-128
- https://doi.org//10.3390/cells10051074
- https://doi.org//10.1186/1752-0509-8-S4-S11
- https://doi.org//10.1111/ejh.12238
- https://doi.org//10.1016/s1470-2045%2808%2970314-0
- https://doi.org//10.1021/acs.jproteome.8b00702
- https://doi.org//10.1002/ijc.32369
- https://doi.org//10.1186/s13045-016-0324-8
- https://doi.org//10.1111/ijlh.12942
- https://doi.org//10.1186/s12859-018-2486-6
- https://doi.org//10.3324/haematol.2019.225078
- https://doi.org//10.4049/jimmunol.1103316
- https://doi.org//10.1186/s12859-015-0611-3
- https://doi.org//10.1038/s41577-020-00426-6
- https://doi.org//10.1182/blood-2015-02-580043
- https://doi.org//10.1111/j.1365-2141.2011.08955.x
- https://doi.org//10.1182/blood-2015-08-661702
- https://doi.org//10.1093/nar/gkq1156
- https://doi.org//10.1093/nar/gkn698
- https://doi.org//10.1111/bjh.15960
- https://doi.org//10.1016/j.cancergen.2019.11.002
- https://doi.org//10.1111/j.1365-2141.2011.08957.x
- https://doi.org//10.1093/nar/gkw377
- https://doi.org//10.1016/j.leukres.2019.01.003
- https://doi.org//10.1016/j.immuni.2020.03.019
- https://doi.org//10.1038/ni.2255
- https://doi.org//10.1182/blood-2014-06-580480
- https://doi.org//10.1097/HS9.0000000000000379
- https://doi.org//10.1093/nar/gkv468
- https://doi.org//10.3390/cells10081917
- https://doi.org//10.1182/blood-2012-11-465138
- https://doi.org//10.1200/PO.18.00127
- https://doi.org//10.1111/bjh.12882
- https://doi.org//10.1016/j.ebiom.2021.103352
- https://doi.org//10.1101/gr.1239303
- https://doi.org//10.3389/fonc.2021.750789
- https://doi.org//10.1038/nmeth.2811
- https://doi.org//10.1093/nar/gky1131
- https://doi.org//10.1186/s40364-021-00347-z
- https://doi.org//10.1084/jem.20122727
- https://doi.org//10.1016/j.blre.2017.08.006
- https://doi.org//10.1097/hs9.0000000000000310
- https://doi.org//10.1038/nature06839
- https://doi.org//10.1371/journal.pone.0232520
- https://doi.org//10.1182/blood.v124.21.1.1
- https://doi.org//10.1182/blood.v114.22.9.9
- https://doi.org//10.1002/cpz1.90
- https://doi.org//10.2147/ott.s214905
- https://doi.org//10.1038/nature10725
- https://doi.org//10.3389/fonc.2020.01423
- https://doi.org//10.1016/j.cels.2019.09.008
- https://doi.org//10.3324/haematol.2013.090233
CC BY 4.0