Back

Single Cell RNA Sequencing Driven Characterization of Pediatric Mixed Phenotype Acute Leukemia

Mumme, H. L.; Raikar, S. S.; Bhasin, S. S.; Thomas, B. E.; DeRyckere, D.; Wechsler, D. S.; Porter, C. C.; Castellino, S. M.; Graham, D. K.; Bhasin, M. K.

2022-07-08 genomics
10.1101/2022.07.07.499210 bioRxiv
Show abstract

BackgroundMixed phenotype acute leukemia (MPAL) is a rare subgroup of leukemia characterized by blast cells that display both myeloid and lymphoid lineage features, making this cancer difficult to diagnose and treat. A deeper characterization of MPAL at the molecular level is essential to better understand similarities/differences to the more common and better-studied leukemias, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Therefore, we performed single-cell RNA sequencing (scRNAseq) on MPAL bone marrow (BM) samples in an attempt to develop a more granular map of the MPAL microenvironment landscape. MethodsWe analyzed [~]16,000 cells from five pediatric MPAL BM samples collected at diagnosis to generate a single-cell transcriptomic landscape of B/Myeloid (B/My) and T/Myeloid (T/My) MPAL blasts and associated microenvironment cells. Cell clusters were identified using principal component analysis and uniform manifold approximation and projection (UMAP). Unsupervised analysis was performed to determine the overall relationship among B/My MPAL, T/My MPAL, and other acute leukemias - B-ALL, T-ALL, and AML. Supervised differentially expressed gene (DEG) analysis was performed to identify B/My and T/My MPAL blast-specific signatures. MPAL sample transcriptome profiles were compared with normal BM stem and immune cells to identify MPAL-specific dysregulation. Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes. Comparative analysis was performed on diagnostic samples based on their future minimal residual disease (MRD) and relapse status. ResultsB/My MPAL and T/My MPAL blasts displayed distinct subtype-specific blast signatures. UMAP analysis revealed that B/My MPAL samples had greater overlap with B-ALL samples, while T/My MPAL samples clustered separately from other acute leukemia subtypes. Genes overexpressed in both MPAL subtypes blasts compared to other leukemias and healthy controls included PLIN2, CD81, and UBE2S. B/My MPAL blast-specific genes included IRS2, SMIM3, and HBEGF, whereas T/My MPAL blast-overexpressed genes included IER5, BOD1L1, and HPGD. Sirtuin signaling, p38 MPAK signaling, and PI3K signaling pathways were upregulated in B/My MPAL blasts while oxidative phosphorylation and Rho family GTPases signaling pathways were upregulated in T/My MPAL blasts. Transcriptomic, pathways, and cell communication level differences were observed in the MPAL samples based on future MRD and clinical outcome status. ConclusionsWe have for the first time described the single-cell landscape of pediatric MPAL and demonstrate that B/My and T/My MPAL have unique scRNAseq profiles distinct from each other as well as from ALL and AML.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
Experimental Hematology
11 papers in training set
Top 0.1%
12.6%
2
PLOS ONE
4510 papers in training set
Top 18%
10.3%
3
Frontiers in Genetics
197 papers in training set
Top 0.5%
7.3%
4
Cancers
200 papers in training set
Top 1%
4.3%
5
Scientific Reports
3102 papers in training set
Top 35%
3.7%
6
BMC Medical Genomics
36 papers in training set
Top 0.2%
3.1%
7
Archives of Clinical and Biomedical Research
28 papers in training set
Top 0.3%
2.7%
8
Transplantation
13 papers in training set
Top 0.2%
2.7%
9
Frontiers in Immunology
586 papers in training set
Top 3%
2.1%
10
Leukemia
39 papers in training set
Top 0.5%
1.7%
50% of probability mass above
11
Briefings in Bioinformatics
326 papers in training set
Top 4%
1.7%
12
BMC Bioinformatics
383 papers in training set
Top 5%
1.4%
13
Genomics
60 papers in training set
Top 1%
1.4%
14
Journal of Cellular and Molecular Medicine
18 papers in training set
Top 0.5%
1.2%
15
Blood Cancer Journal
11 papers in training set
Top 0.2%
1.2%
16
Molecular Biology Reports
19 papers in training set
Top 0.3%
1.2%
17
Journal of Hematology & Oncology
10 papers in training set
Top 0.1%
1.0%
18
Cells
232 papers in training set
Top 4%
1.0%
19
Genes
126 papers in training set
Top 2%
1.0%
20
European Radiology
14 papers in training set
Top 0.5%
1.0%
21
Cancer Medicine
24 papers in training set
Top 1%
0.9%
22
Journal of Personalized Medicine
28 papers in training set
Top 0.9%
0.9%
23
BMC Genomics
328 papers in training set
Top 4%
0.9%
24
Heliyon
146 papers in training set
Top 5%
0.8%
25
Molecular Oncology
50 papers in training set
Top 0.9%
0.8%
26
Journal of Thrombosis and Haemostasis
28 papers in training set
Top 0.7%
0.8%
27
Blood
67 papers in training set
Top 1%
0.8%
28
Journal of Leukocyte Biology
40 papers in training set
Top 0.4%
0.8%
29
Modern Pathology
21 papers in training set
Top 0.5%
0.7%
30
Laboratory Investigation
13 papers in training set
Top 0.3%
0.7%