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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

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Single-Cell Sequencing | August 20-21, 2014

CSHL, UCLA & Einstein to Lead Roundtable Discussions on Single-Cell Sequencing

Interactive discussions on three of the key questions researchers are facing when considering single-cell analysis will be held on the second day of the Single-Cell Sequencing Conference at Next Generation Dx Summit, taking place August 20-21, 2014 in Washington, DC. For full program details and to register, please visit NextGenerationDx.com/Single-Cell-Sequencing.

Making Single-Cell Analysis Cost Effective for Clinical Use
Moderator: James Hicks, Ph.D., Research Professor, Cancer Genomics, Cold Spring Harbor Laboratory

  • Methods for capture: What are the tradeoffs?
  • Combining RNA, DNA and protein analysis
  • What genomic assays are most informative?
  • Can assays be certifiable?

Finding a Needle in a Haystack: Towards Diagnosing Rare Soft Tissue Cancer Stem Cells (CSCs)
Moderator: Michael Masterman-Smith, Ph.D., Entrepreneurial Scientist, UCLA California NanoSystems Institute

  • Rethinking companion diagnostics for cancer to incorporate analysis of CSCs
  • Current direct methodologies of CSC detection/isolation
  • Current proxy methodologies of CSC detection/isolation
  • The hope and promise of single-cell assay tools and technologies

Why Single-Cell Sequencing?
Moderator: Jan Vijg, Ph.D., Professor and Chairman, Genetics, Albert Einstein College of Medicine
Sample limitations, e.g., prenatal diagnostics and CTCs

  • Sample limitations, e.g., prenatal diagnostics and CTCs
  • To study cell-to-cell variation, e.g., in tumors as well as normal tissues
  • To overcome technological constraints, e.g., detecting somatic mutations
  • Cell-to-cell fluctuations in gene expression can easily impair function, yet can be undetectable by measuring averages
  • How many different cell types are there?
View Brochure    |   Register (Advance Registration Ends July 18)  |   NextGenerationDx.com/Single-Cell-Sequencing


About the Conference

Sequencing data from bulk DNA or RNA from multiple cells provide global information on average states of cell populations. But with whole-genome amplification and NGS, researchers can detect variation in individual cancer cells and dissect tumor evolution. Such cancer genome sequencing will improve oncology by detecting rare tumor cells early, measuring intra-/intertumor heterogeneity, guiding chemotherapy and controlling drug resistance. The Single-Cell Sequencing conference explores the latest strategies, data analyses and clinical considerations that influence and aid cancer diagnosis, prognosis and prediction and will lead to individualized cancer therapy.

Sessions include presentations spanning the opportunities of clinical single-cell analysis from:

  • Sunney Xie, Ph.D., Mallinckrodt Professor. Chemistry and Chemical Biology, Harvard University
  • Maximilian Diehn, M.D., Ph.D., Assistant Professor, Radiation Oncology, Stanford Cancer Institute, Institute for Stem Cell Biology & Regenerative Medicine, Stanford University
  • Denis Smirnov, Associate Scientific Director, US Biomarker Oncology, Janssen R&D US
  • James Hicks, Ph.D., Research Professor, Cancer Genomics, Cold Spring Harbor Laboratory
  • Jan Vijg, Ph.D., Professor and Chairman, Genetics, Albert Einstein College of Medicine
  • John F. Zhong, Ph.D., Associate Professor, Pathology, University of Southern California School of Medicine
  • Mark Hills, Ph.D., Research Scientist, Peter M. Lansdorp Laboratory, BC Cancer Research Centre
  • Michael Masterman-Smith, Ph.D., Entrepreneurial Scientist, UCLA California NanoSystems Institute
  • Parveen Kumar, Research Scientist, Thierry Voet Laboratory, Human Genetics, University of Leuven
  • Peter Nemes, Ph.D., Assistant Professor, Chemistry, George Washington University
  • Theresa Zhang, Ph.D., Vice President, Research Services, Personal Genome Diagnostics
  • Yong Wang, Ph.D., Senior Postdoctoral Fellow, Nicholas E. Navin Laboratory, Genetics, Bioinformatics, MD Anderson Cancer Center
  • Zivana Tezak, Ph.D., Associate Director, Science and Technology, Personalized Medicine, Office of In Vitro Diagnostic Device Evaluation and Safety (OIVD), Center for Devices and Radiological Health (CDRH), FDA

 


Recommended Pre-Conference Courses

NGS Data Analysis – Determining Clinical Utility of Genome Variants
Monday, August 18 | 9:00am – 12:00pm
This course will explore the strategies of genomic data analysis and interpretation, an emergent discipline that seeks to deliver better answers from NGS data so that patients and their physicians can determine informed healthcare decisions. View Details

NGS as a Diagnostics Platform
Monday, August 18 | 2:00pm – 5:00pm
The focus of this short course will be on understanding the use of NGS in clinical diagnosis, practical implementation of NGS in clinical laboratories and analysis of large data sets by using bioinformatics tools to parse and interpret data in relation to the clinical phenotype. The concluding presentation will be dedicated to quality and standardization of NGS assays. View Details

Register   |   View Agenda   | NextGenerationDx.com

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Next Generation Dx Summit 2014
Cambridge Healthtech Institute, 250 First Avenue, Suite 300, Needham, MA 02494
www.healthtech.com

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Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell(1)

Reporter, Writer: Stephen J. Williams, Ph.D.

Most tumors exhibit a level of diversity, at the cellular, histologic, and even genetic level (2).  This genetic heterogeneity within a tumor has been a focus of recent research efforts to analyze the characteristics, expression patterns, and genetic differences between individual tumor cells.  This genetic diversity is usually manifested as single nucleotide variations (SNV) and copy number variations (CNV), both of which provide selection pressures in both cancer and evolution.

As cancer research and personalized medicine is focused on analyzing this tumor heterogeneity it has become pertinent view the tumor as a heterogeneous population of cells instead of as a homogenous mass.  In, fact, studies have suggested that cancer cell lines growing on plastic in culture, even though thought of as clonogenic, can actually display a varied degree of expression differences between neighboring cells growing on the same dish.  Indeed, cancer stem cells show an asynchronous cell division, for example a parent CD133-positive cell will divide into a CD133-positive and a CD133-negative cell(3). In addition, the discovery that circulating tumor cells (a rare population of circulating cells in the blood) can be prognostic of outcome in cancer such as inflammatory breast cancer(4), it is ever more important to develop methods to analyze single cell populations.

Harvard University researchers, Dr. Chenghang Zong, Sijia Lu, Alec Chapman and Sunney Xie developed a new amplification method utilizing multiple annealing and looping-based amplification cycles (MALBAC)(1).   A quasilinear preamplification process is used on pictograms of DNA genomic fragments (form 10 to 100 kb) isolated from a single cell.   This is performed to reduce the bias associated with nonlinear DNA amplification.  A series of random primers (which the authors termed MALBAC primers, constructed with a common sequence tags) are annealed at low temperature (0 °C). PCR rounds produce semiamplicons.  Further rounds of amplification, after a step of looping the amplicons, result in full amplicons with complementary ends.  When the two ends hybridize to form the looped DNA, this prevents use of this loop structure as a template, therefore leading to a close-to–linear amplification.    The process allows for a higher fidelity of DNA replication and the ability to amplify a whole genome.  The amplicons are then sequenced either by whole-genome sequencing methods using Sanger-sequencing to verify any single nucleotide polymorphisms.  This procedure of MALBAC-amplification resulted in coverage of 85-93% of the genome of a single cell.

As proof of principle, the authors used MALBAC to amplify the DNA of single SW480 cancer cells (picked from a clonally expanded population of a heterogeneous population (the bulk DNA).  Comparison of the MALBAC method versus the MDA method revealed copy number variations (CNV) between three individual cells, which had been picked from the clonally expanded pool. Their results were in agreement with karyotyping studies on the SW480 cell line.  Meticulous quality controls were performed to limit contamination, high false positive rates of SNV detection due to amplification bias, and false positives due to amplification or sequencing errors.

Interestingly, the authors found 35 unique single nucleotide variations which h had occurred from 20 cell divisions from a single SW480 cancer cell.  This resulted in an estimated 49 mutations which occurred in 20 generations, yielding a mutation rate of 2.5 nucleotides per generation.  In addition, the authors were able to map some of these mutations on various chromosomes and perform next-gen sequencing (deep sequencing) to verify the nucleotide mutations and found an unusually high purine-pyrimidine exchange rate.

In a subsequent paper, investigators from the same group at Harvard used this technology to sequence 99 sperm cells from a single individual to study genetic diversity created during meiotic recombination, a mechanism involved in evolution and development(5).

Reference:

1.            Zong, C., Lu, S., Chapman, A. R., and Xie, X. S. (2012) Science 338, 1622-1626

2.            Cooke, S. L., Temple, J., Macarthur, S., Zahra, M. A., Tan, L. T., Crawford, R. A., Ng, C. K., Jimenez-Linan, M., Sala, E., and Brenton, J. D. (2011) British journal of cancer 104, 361-368

3.            Guo, R., Wu, Q., Liu, F., and Wang, Y. (2011) Oncology reports 25, 141-146

4.            Giuliano, M., Giordano, A., Jackson, S., Hess, K. R., De Giorgi, U., Mego, M., Handy, B. C., Ueno, N. T., Alvarez, R. H., De Laurentiis, M., De Placido, S., Valero, V., Hortobagyi, G. N., Reuben, J. M., and Cristofanilli, M. (2011) Breast cancer research : BCR 13, R67

5.            Lu, S., Zong, C., Fan, W., Yang, M., Li, J., Chapman, A. R., Zhu, P., Hu, X., Xu, L., Yan, L., Bai, F., Qiao, J., Tang, F., Li, R., and Xie, X. S. (2012) Science 338, 1627-1630

Other related posts on this website regarding Cancer and Genomics include:

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

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