Figure 1.
Clonal composition of paired AML samples of PB and BM. AML samples 16888, 17385, 17550, 17570, and 18037 were from the ECOG-ACRIN E1900 clinical trial.13 Lin-negative CD34+ cells were obtained from mononuclear fractions by magnetic sorting using the EasySep Lin-negative selection cocktail followed by CD34+ selection (StemCell Technologies). sctDNAseq was performed as described before using our custom-made expanded myeloid panel.12 Briefly, sequencing data generated from the Tapestri platform were processed using Mission Bio’s Tapestri Pipeline (Tapestri 2.0.2) for adapter trimming (Cutadapt), sequence alignment (reference genome hg19), barcode correction, cell finding, and variant calling (GATK). Annotations for the filtered variants were curated using the Integrative Genomics Viewer. Initial clonal architectures were determined using genotype clustering analysis, including zygosity information with the Tapestri Insight software package. We used the SCITE software to infer phylogenetic trees of the driver mutations from the sctDNAseq data. SCITE phylogenetic inference is based on Bayesian approach which will allow us to quantify uncertainty in the inferred clonal architectures by sampling trees based on the model’s posterior distribution. Steps were performed using the methodology described previously.12 SCITE software was run with a chain length of 900 000 for each repetition. We used an estimated allele dropout rate of 4.5% and a false-positive rate of 1.0%. Further data analysis was performed using a customized R script. Left panels: The phylogenetic trees visualize the predicted evolutionary descent of the AML clones based on sctDNAseq data. The connecting lines represent the link between the consecutive clones. Right panels: The circles illustrate the clone sizes. The percentage of cells carrying indicated mutations (clones) is illustrated.

Clonal composition of paired AML samples of PB and BM. AML samples 16888, 17385, 17550, 17570, and 18037 were from the ECOG-ACRIN E1900 clinical trial.13 Lin-negative CD34+ cells were obtained from mononuclear fractions by magnetic sorting using the EasySep Lin-negative selection cocktail followed by CD34+ selection (StemCell Technologies). sctDNAseq was performed as described before using our custom-made expanded myeloid panel.12 Briefly, sequencing data generated from the Tapestri platform were processed using Mission Bio’s Tapestri Pipeline (Tapestri 2.0.2) for adapter trimming (Cutadapt), sequence alignment (reference genome hg19), barcode correction, cell finding, and variant calling (GATK). Annotations for the filtered variants were curated using the Integrative Genomics Viewer. Initial clonal architectures were determined using genotype clustering analysis, including zygosity information with the Tapestri Insight software package. We used the SCITE software to infer phylogenetic trees of the driver mutations from the sctDNAseq data. SCITE phylogenetic inference is based on Bayesian approach which will allow us to quantify uncertainty in the inferred clonal architectures by sampling trees based on the model’s posterior distribution. Steps were performed using the methodology described previously.12 SCITE software was run with a chain length of 900 000 for each repetition. We used an estimated allele dropout rate of 4.5% and a false-positive rate of 1.0%. Further data analysis was performed using a customized R script. Left panels: The phylogenetic trees visualize the predicted evolutionary descent of the AML clones based on sctDNAseq data. The connecting lines represent the link between the consecutive clones. Right panels: The circles illustrate the clone sizes. The percentage of cells carrying indicated mutations (clones) is illustrated.

Close Modal

or Create an Account

Close Modal
Close Modal