Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells

Gao, Yan; Ni, Xiaohui; Guo, Hua; Su, Zhe; Ba, Yi; Tong, Zhongsheng; Guo, Zhi; Yao, Xin; Chen, Xixi; Yin, Jian; Zhao, Yan; Guo, Lin; Liu, Ying; Bai, Fan; Xie, Xin; Zhang, Ning

doi:10.1101/gr.216788.116

Cited by 82 publications

(78 citation statements)

References 49 publications

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“…Thus, these authors concluded that the CNV evolution of CTC would be affected by therapeutic pressure in prostate cancer (Dago et al 2014 ). Gao et al analyzed the CNV of CTCs across 23 patients and concluded that the CNV tumor evolution process follows a convergent evolution model through primary tumor to CTCs but does not follow the classical gradual acquisition mode or the recent alert punctuated model (Gao et al 2017 ). However, more patients showed different variation spectrums during evolution, most of which still cannot be comprehensively explained.…”

Section: Inferring Tumor Heterogeneity and Evolution Dynamicallymentioning

confidence: 99%

Progress and challenges of sequencing and analyzing circulating tumor cells

et al. 2017

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Circulating tumor cells (CTCs) slough off primary tumor tissues and are swept away by the circulatory system. These CTCs can remain in circulation or colonize new sites, forming metastatic clones in distant organs. Recently, CTC analyses have been successfully used as effective clinical tools to monitor tumor progression and prognosis. With advances in next-generation sequencing (NGS) and single-cell sequencing (SCS) technologies, scientists can obtain the complete genome of a CTC and compare it with corresponding primary and metastatic tumors. CTC sequencing has been successfully applied to monitor genomic variations in metastatic and recurrent tumors, infer tumor evolution during treatment, and examine gene expression as well as the mechanism of the epithelial-mesenchymal transition. However, compared with cancer biopsy sequencing and circulating tumor DNA sequencing, the sequencing of CTC genomes and transcriptomes is more complex and technically difficult. Challenges include enriching pure tumor cells from a background of white blood cells, isolating and collecting cells without damaging or losing DNA and RNA, obtaining unbiased and even whole-genome and transcriptome amplification material, and accurately analyzing CTC sequencing data. Here, we review and summarize recent studies using NGS on CTCs. We mainly focus on CTC genome and transcriptome sequencing and the biological and potential clinical applications of these methodologies. Finally, we discuss challenges and future perspectives of CTC sequencing.

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Section: Inferring Tumor Heterogeneity and Evolution Dynamicallymentioning

confidence: 99%

Progress and challenges of sequencing and analyzing circulating tumor cells

et al. 2017

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“…Based on cell type enrichment analysis [32] on the gene co-expression modules unique to subtype 2 (i.e., the yellow and brown modules in Fig. 6), we found an erythroblast/stem-like signature that may be related to convergent evolution in tumors, since evidence of convergent evolution has already been noted through copy number alterations in circulating tumor cells in multiple cancer types [33]. Another possible explanation could be that subtype 2 is some form of malignant progenitor cell.…”

Section: Discussionmentioning

confidence: 93%

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Johnson

Huang³

et al. 2020

Preprint

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With the rapid advance of single cell sequencing techniques, single cell molecular data are quickly accumulated. However, there lacks a sound approach to properly integrate single cell data with the existing large amount of patient-level disease data. To address such need, we proposed DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer-learning framework which allows for cellular and clinical information, including cell types, disease risk, and patient subtypes, to be cross-mapped between single cell and patient data, provided they share at least one common type of molecular data. We call such transferrable information "impressions", which are generated by the deep learning models learned in the DEGAS framework. Using eight datasets from a wide range of diseases including Glioblastoma Multiforme (GBM), Alzheimer's Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility and broad applications of DEGAS in cross-mapping clinical and cellular information across disparate single cell and patient level transcriptomic datasets. Specifically, we correctly mapped clinically known GBM patient subtypes onto single cell data. We also identified previously known neuron loss from AD brains, then mapped the "impression" of AD risk to single cell data. Furthermore, we discovered novel differences in excitatory and inhibitory neuron loss in AD data. From the exploratory MM data, we identified differences in the malignancy of different CD138+ cellular subtypes based on "impressions" of relapse information transferred from MM patients. Through this work, we demonstrated that DEGAS is a powerful framework to cross-infer cellular and patient-level characteristics, which not only unites single cell and patient level transcriptomic data by identifying their latent links using the deep learning approach, but can also prioritize both patient subtypes and cellular subtypes for precision medicine.

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“…Gao et al studied the genomic alterations in single CTC and primary tumor cells from 23 patients of colorectal cancer, breast cancer, gastric carcinoma and prostatic cancer and found the similar CNV modal between CTC and primary tumor cells. They also detected those primary cells that have transfer ability, which is important for mechanism study of cancer transfer . Accordingly, single‐cell omics analysis helps in identifying heterogeneous cancer cell subtypes and holds a great potential for clinical diagnosis and precision medicine.…”

Section: Applicationsmentioning

confidence: 99%

Microfluidic Single‐Cell Omics Analysis

Wang

et al. 2019

Small

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The commonly existing cellular heterogeneity plays a critical role in biological processes such as embryonic development, cell differentiation, and disease progress. Single‐cell omics‐based heterogeneous studies have great significance for identifying different cell populations, discovering new cell types, revealing informative cell features, and uncovering significant interrelationships between cells. Recently, microfluidics has evolved to be a powerful technology for single‐cell omics analysis due to its merits of throughput, sensitivity, and accuracy. Herein, the recent advances of microfluidic single‐cell omics analysis, including different microfluidic platform designs, lysis strategies, and omics analysis techniques, are reviewed. Representative applications of microfluidic single‐cell omics analysis in complex biological studies are then summarized. Finally, a few perspectives on the future challenges and development trends of microfluidic‐assisted single‐cell omics analysis are discussed.

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Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells

Cited by 82 publications

References 49 publications

Progress and challenges of sequencing and analyzing circulating tumor cells

Progress and challenges of sequencing and analyzing circulating tumor cells

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Microfluidic Single‐Cell Omics Analysis

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