scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

AbstractSingle-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution, but classical DNA amplification-based methods are low-throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN). The method has a throughput of up to 1,800 cells per run for copy number variation (CNV) detection. Also, it has a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy based on evaluation in cell lines and tumour tissues. We used this approach to profile the tumour clones in paired primary and relapsed tumour samples of hepatocellular carcinoma (HCC). We identified 3 clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumour containing additional alterations in chromosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumour, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution.

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Abstract 4696: High-throughput single-cell targeted DNA sequencing using an updated TapestriTMPlatform reveals rare clones and clonal evolution for multiple blood cancers

10.1158/1538-7445.sabcs18-4696 ◽

2019 ◽

Author(s):

Nianzhen Li ◽

Daniel Mendoza ◽

Adam Sciambi ◽

Mani Manivannan ◽

Jacob Ho ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

High Throughput ◽

Clonal Evolution ◽

Multiple Blood

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Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

10.1101/2020.02.07.925743 ◽

2020 ◽

Cited By ~ 5

Author(s):

Kiyomi Morita ◽

Feng Wang ◽

Katharina Jahn ◽

Jack Kuipers ◽

Yuanqing Yan ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Dna Sequencing ◽

Single Cell ◽

High Throughput ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Treatment Resistance ◽

Clonal Diversity ◽

History Of ◽

Acute Myeloid

SummaryOne of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1/FLT3-ITD, DNMT3A/NPM1, SRSF2/IDH2, and WT1/FLT3-ITD) and those that were mutually exclusive (e.g., NRAS/KRAS, FLT3-D835/ITD, and IDH1/IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity (≥4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

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Author Correction: Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics

Nature Communications ◽

10.1038/s41467-021-23280-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kiyomi Morita ◽

Feng Wang ◽

Katharina Jahn ◽

Tianyuan Hu ◽

Tomoyuki Tanaka ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

High Throughput ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Single Cell Genomics ◽

Acute Myeloid

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High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

10.1101/424945 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mandeep Singh ◽

Ghamdan Al-Eryani ◽

Shaun Carswell ◽

James M. Ferguson ◽

James Blackburn ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

High Throughput ◽

Clonal Evolution ◽

Primary Tumour ◽

Transcriptome Profiling ◽

Cost Effective ◽

Short Read ◽

Wide Range ◽

Long Read

AbstractHigh-throughput single-cell RNA-Sequencing is a powerful technique for gene expression profiling of complex and heterogeneous cellular populations such as the immune system. However, these methods only provide short-read sequence from one end of a cDNA template, making them poorly suited to the investigation of gene-regulatory events such as mRNA splicing, adaptive immune responses or somatic genome evolution. To address this challenge, we have developed a method that combines targeted long-read sequencing with short-read based transcriptome profiling of barcoded single cell libraries generated by droplet-based partitioning. We use Repertoire And Gene Expression sequencing (RAGE-seq) to accurately characterize full-length T cell (TCR) and B cell (BCR) receptor sequences and transcriptional profiles of more than 7,138 lymphocytes sampled from the primary tumour and draining lymph node of a breast cancer patient. With this method we show that somatic mutation, alternate splicing and clonal evolution of T and B lymphocytes can be tracked across these tissue compartments. Our results demonstrate that RAGE-Seq is an accessible and cost-effective method for high-throughput deep single cell profiling, applicable to a wide range of biological challenges.

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