Single Cell Genotypic and Phenotypic Analysis of Measurable Residual Disease in Acute Myeloid Leukemia

Robinson, Troy M.; Bowman, Robert L.; Persaud, Sonali; Liu, Ying; Gao, Qi; Zhang, Jingping; Sun, Xiaotian; Sciambi, Adam; Llanso, Aaron; Famulare, Christopher; Goldberg, Aaron D; Doğan, Ahmet; Roshal, Mikhail; Levine, Ross L.; Xiao, Wenbin

doi:10.1182/blood-2022-166501

Cited by 6 publications

(9 citation statements)

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“…We used this method with default parameters and without modifications. scSplit is a SNP-based approach developed to demultiplex scRNA-seq data using initial k-means clustering followed by an expectation-maximization approach 19 . To modify this approach to scDNA-seq data, we used this method starting with an allele count matrix of SNPs x single-cell barcodes and then as-written without modifications. While there are currently no named algorithms for demultiplexing scDNA-seq data, a SNP-based approach is described in a publication by Robinson et al 26 . This method uses initial k-means clustering followed by additional multiple identification by generating artificial multiplets and comparing them to true cells 26 .…”

Section: System and Methodsmentioning

confidence: 99%

“…To modify this approach to scDNA-seq data, we used this method starting with an allele count matrix of SNPs x single-cell barcodes and then as-written without modifications. While there are currently no named algorithms for demultiplexing scDNA-seq data, a SNP-based approach is described in a publication by Robinson et al 26 . This method uses initial k-means clustering followed by additional multiple identification by generating artificial multiplets and comparing them to true cells 26 . We refer to this method as the “Robinson method.” doubletD is a SNP-based approach for detecting doublets in scDNA-seq data using an expectation-maximization approach, and is based on the observation the scDNA-seq multiplets tend to have an increase in number of allele copies and/or drop-out 24 .…”

Section: System and Methodsmentioning

confidence: 99%

“…(4) While there are currently no named algorithms for demultiplexing scDNA-seq data, a SNPbased approach is described in a publication by Robinson et al 26 . This method uses initial kmeans clustering followed by additional multiple identification by generating artificial multiplets and comparing them to true cells 26 . We refer to this method as the "Robinson method.…”

Section: Comparison Against Alternative Multiplexing Approachesmentioning

confidence: 99%

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Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Kennedy,

Roy,

Peretz

et al. 2024

Preprint

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MotivationRecently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity.Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need.ResultsHere, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 – 0.991 vs 0.552 – 0.934 using demultiplexing methods from the single-cell literature.Availability ImplementationSNACS is available athttps://github.com/olshena/SNACS.Abstract Figure

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Section: System and Methodsmentioning

confidence: 99%

Section: System and Methodsmentioning

confidence: 99%

Section: Comparison Against Alternative Multiplexing Approachesmentioning

confidence: 99%

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Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Kennedy,

Roy,

Peretz

et al. 2024

Preprint

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“…In this technique, the combination of mitochondrial and nuclear DNA sequencing allowed for more reliable assignment of single cells to clones when allelic dropout of the nuclear mutations occurs, as it often can in single-cell DNA sequencing [64 ▪ ]. Robinson et al [65 ▪ ] applied single-cell joint immunophenotypic-genotypic analysis to cryopreserved samples enriched for blasts by MFC, to study MRD in 29 patients post induction chemotherapy. Their methodology allowed sensitive discernment of residual leukaemia from preleukemic clones and also chronicled genotype-phenotype correlations.…”

Section: Single-cell Sequencing In Measurable Residual Diseasementioning

confidence: 99%

“…Their methodology allowed sensitive discernment of residual leukaemia from preleukemic clones and also chronicled genotype-phenotype correlations. Notably, their investigations included samples from patients post allogeneic stem cell transplant and through germline SNP-based analysis, allowed concomitant determination of chimerism and MRD [65 ▪ ]. The germline SNP-based analysis offers the possibility of future sequencing technologies that could enable joint interrogation of somatic and germline variants towards the goal of personalized, comprehensive characterization of disease and risk.…”

Section: Single-cell Sequencing In Measurable Residual Diseasementioning

confidence: 99%

Is it the time to integrate novel sequencing technologies into clinical practice?

VanOudenhove

Halene²,

Mendez³

2022

Current Opinion in Hematology

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Purpose of reviewThe aim of this study was to provide insight into how novel next-generation sequencing (NGS) techniques are set to revolutionize clinical practice.Recent findingsAdvances in sequencing technologies have focused on improved capture of mutations and reads and cellular resolution. Both short and long read DNA sequencing technology are being refined and combined in novel ways with other multiomic approaches to gain unprecedented biological insight into disease. Single-cell (sc)DNA-seq and integrated scDNA-seq with immunophenotyping provide granular information on disease composition such as clonal hierarchy, co-mutation status, zygosity, clonal diversity and genotype phenotype correlations. These and other techniques can identify rare cell populations providing the opportunity for increased sensitivity in measurable residual disease monitoring and precise characterization of residual clones permitting distinction of leukemic from pre/nonmalignant clones.SummaryIncreasing genetics-based mechanistic insights and classification of myeloid diseases along with a decrease in the cost of high-throughput NGS mean novel sequencing technologies are closer to being a reality in standard clinical practice. These technologies are poised to improve diagnostics, our ability to monitor treatment response and minimal residual disease and allow the study of premalignant conditions such as clonal haematopoiesis.

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Pre-emptive detection and evolution of relapse in acute myeloid leukemia by flow cytometric measurable residual disease surveillance

McCarthy,

Gui,

Dumezy

et al. 2024

Leukemia

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Measurable residual disease (MRD) surveillance in acute myeloid leukemia (AML) may identify patients destined for relapse and thus provide the option of pre-emptive therapy to improve their outcome. Whilst flow cytometric MRD (Flow-MRD) can be applied to high-risk AML/ myelodysplasia patients, its diagnostic performance for detecting impending relapse is unknown. We evaluated this in a cohort comprising 136 true positives (bone marrows preceding relapse by a median of 2.45 months) and 155 true negatives (bone marrows during sustained remission). At an optimal Flow-MRD threshold of 0.040%, clinical sensitivity and specificity for relapse was 74% and 87% respectively (51% and 98% for Flow-MRD ≥ 0.1%) by ‘different-from-normal’ analysis. Median relapse kinetics were 0.78 log10/month but significantly higher at 0.92 log10/month for FLT3-mutated AML. Computational (unsupervised) Flow-MRD (C-Flow-MRD) generated optimal MRD thresholds of 0.036% and 0.082% with equivalent clinical sensitivity to standard analysis. C-Flow-MRD-identified aberrancies in HLADRlow or CD34+CD38low (LSC-type) subpopulations contributed the greatest clinical accuracy (56% sensitivity, 90% specificity) and notably, by longitudinal profiling expanded rapidly within blasts in > 40% of 86 paired MRD and relapse samples. In conclusion, flow MRD surveillance can detect MRD relapse in high risk AML and its evaluation may be enhanced by computational analysis.

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Single Cell Genotypic and Phenotypic Analysis of Measurable Residual Disease in Acute Myeloid Leukemia

Cited by 6 publications

References 0 publications

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Is it the time to integrate novel sequencing technologies into clinical practice?

Pre-emptive detection and evolution of relapse in acute myeloid leukemia by flow cytometric measurable residual disease surveillance

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