THE SINGLE-CELL ATLAS OF DRIVER MUTATIONS IN AML
Author(s): ,
Kiyomi Morita
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Feng Wang
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Katharina Jahn
Affiliations:
Swiss Federal Institute of Technology in Zurich,Zürich,Switzerland
,
Yuanqing Yan
Affiliations:
The University of Texas Health Science Center at Houston,Houston,United States
,
Robert Durruthy-Durruthy
Affiliations:
Mission Bio, Inc.,South San Francisco,United States
,
Anup Parikh
Affiliations:
Mission Bio, Inc.,South San Francisco,United States
,
Jairo Matthews
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Latasha Little
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Curtis Gumbs
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Jianhua Zhang
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Xingzhi Song
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Erika Thompson
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Keyur Patel
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Carlos Bueso-Ramos
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Courtney DiNardo
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Farhad Ravandi
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Elias Jabbour
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Michael Andreeff
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Jorge Cortes
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Marina Konopleva
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Guillermo Garcia-Manero
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Hagop Kantarjian
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Dennis Eastburn
Affiliations:
Mission Bio, Inc.,South San Francisco,United States
,
P Andrew Futreal
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
,
Niko Beerenwinkel
Affiliations:
Swiss Federal Institute of Technology in Zurich,Zürich,Switzerland
Koichi Takahashi
Affiliations:
The University of Texas MD Anderson Cancer Center,Houston,United States
EHA Library. Morita K. Jun 15, 2019; 267416; S833
Kiyomi Morita
Kiyomi Morita
Contributions
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Abstract

Abstract: S833

Type: Oral Presentation

Presentation during EHA24: On Saturday, June 15, 2019 from 11:30 - 11:45

Location: Forum Hall

Background
Assessment of genetic intratumor heterogeneity using next generation sequencing (NGS) can underestimate the complexity of subclonal architecture, since it is confounded by tumor purity, zygosity, and cell-level co-occurrence and exclusivity among multiple mutations.

Aims
To thoroughly dissect the subclonal architecture, we performed single cell DNA sequencing (scDNA-seq) in 98 bone marrow samples from 80 patients with acute myeloid leukemia (AML).

Methods
We used a novel microfluidics-based scDNA-seq platform covering 40 amplicons in 19 AML genes (Tapestri, Mission Bio). As a reference, all samples were concurrently sequenced by the bulk NGS using 295-gene exome capture sequencing. Allele-specific copy number data was obtained from SNP array data (Illuminia Omni 2.5 array). Droplet digital PCR (Bio-Rad QX200 Droplet Digital PCR system) was used to estimate the sensitivity of our scDNA-seq platform.

Results
Median 6,786 cells/sample were sequenced with median allele drop-out rate of 7.2% (population frequency inferred from commonly heterozygous SNP loci). Each amplicon was covered at a median 26x/cell. The scDNA-seq detected all of the bulk NGS-confirmed mutations. RUNX1 and FLT3 mutations were frequently detected as homozygous mutations and concurrent SNP array analysis detected copy number neutral loss-of-heterozygosity of the mutant loci, which likely resulted in homozygous calls. The scDNA-seq also uniquely detected driver mutations that were not detected by the bulk NGS but were confirmed by droplet digital PCR, suggesting that scDNA-seq is more sensitive than the conventional bulk NGS.

scDNA-seq data unambiguously visualized the single-cell level co-occurrence of driver mutations (i.e. DNMT3A/FLT3-ITD/NPM1 and SRSF2/IDH2), confirming the cooperative function of these mutations. scDNA-seq data also revealed the cellular-level mutual exclusivity between functionally redundant mutations such as IDH1/IDH2, FLT3-ITD/TKD and NRAS/KRAS (Figure 1).

Inference of phylogenetic trees using SCITE algorithm (Jahn, et al. Genome Biology 2016) uncovered distinct patterns of clonal evolution in AML. The majority of the cases showed a linear evolution pattern where the founder mutations linearly acquired sub-clonal mutations in a step-wise manner. We also detected convergent evolution in some cases where functionally similar driver mutations were acquired in parallel. DNMT3A, IDH1, IDH2 and U2AF1 mutations were frequently detected as trunk mutations, whereas FLT3, NRAS, and NPM1 mutations were usually detected as branch mutations. Analysis of longitudinal samples from 15 patients revealed the remodeling of clonal architecture in AML. For example, scDNA-seq data for a previously-untreated therapy-related AML case revealed that a FLT3p.D835Y clone, which was originally presented as a small sub-clone, survived the induction therapy consisting of azacitidine and sorafenib, and significantly expanded at relapse, while all other FLT3 and KRAS mutations were eradicated. This clonal remodeling is consistent with differential sensitivity of various FLT3 mutations to sorafenib (Smith et al. Leukemia 2015), of which FLT3 p.D835Y mutation has been shown to be more resistant to sorafenib compared to p.D835E and ITD mutations, demonstrating the differential behavior of sub-clones and clonal selection under molecularly targeted therapy (Figure 2).

Conclusion
We performed a high-throughput scDNA-seq and described a comprehensive landscape of driver mutations and detailed clonal evolution history in AML at the single-cell resolution.

Session topic: 3. Acute myeloid leukemia - Biology & Translational Research

Keyword(s): AML, Mutation analysis

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