Journal Logo

SINGLE-CELL TRANSCRIPTIONAL HETEROGENEITY SUGGESTS NOVEL FINGERPRINT OF RELAPSE IN ACUTE MYELOID LEUKEMIA

PF156

Wang, Z.1; Wang, R.1; Yang, C.1; liu, Y.1; Zhang, C.1; Gao, L.1; Zhang, X.1

doi: 10.1097/01.HS9.0000558840.27713.01
Poster Session I: Acute lymphoblastic leukemia - Biology & translational research
Free

1Department of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, China

Back to Top | Article Outline

Background:

Some rare subgroups of leukemia cells harboring relapse-inducing genes were selected after chemotherapy. Tounravel intra-tumoral heterogeneity and selective drug-resistance, single-cell RNA sequencing (scRNA-seq) was already performed on many solid tumors and blood cancer to achieve the high-resolutiontranscriptome profiling on individual cells from a larger heterogeneous population. However,the comprehensive investigation on cancer heterogeneityduring cancer development at single-cell resolution is still rare.

Back to Top | Article Outline

Aims:

To identify diverse subsets and molecular characteristics of acute myeloid leukemia (AML) relapse

Back to Top | Article Outline

Methods:

Since single-cell suspension was obtainedfrom bone marrow of acute myeloid leukemia samples, we used the 10x GenomicsChromium platform to capture transcriptomes of singlecells on barcoded mRNA capture beadsfor massively parallel scRNA-seq. Data processing followed by the Cell Ranger software pipelineto demultiplex cellularbarcodes, and map reads to the genome and transcriptome hg38 using the STAR aligner. Uniquemolecular identifier (UMI) count matrix and quality control were performed using Seurat. The t-SNE map was calculated using Rtsne package

Back to Top | Article Outline

Results:

We analyzed transcriptome data from near 50K single leukemia bone marrow cells across 3 patients during newly diagnosed, complete remission and relapse stages. To define the landscape of cellular heterogeneity and its association with outcome in an unbiased manner, we performed unsupervised machine learning algorithm on near 50K single cells from leukemia bone marrow and identify one robust 14-cluster solution (from 0 to 13, Figure 1A) and the hallmark genes within each clusters (Figure 1B). The pattern exhibits distinct distribution on different stages (Figures 1C), indicating intra-tumoral heterogeneity during leukemia progression. Within cluster 0, the subgroups expressing such as LILRB2, TNFAIP2 or PTAFR were chemotherapy sensitive (Figure 2A). While the subgroups expressing such as APOC1, CDKN2A, KLF1 or GATA1 were chemotherapy resistant (Figure 2B). These chemotherapy resistant subgroups may play some key roles in leukemia relapse.

Figure

Figure

Back to Top | Article Outline

Summary/Conclusion:

We identify novel relapse subgroups in highly heterogeneous leukemia, which could not be found using traditional bulk RNA-seq.

Copyright © 2019 The Authors. Published by Wolters Kluwer Health Inc., on behalf of the European Hematology Association.