August 1, 2014 (Vol. 34, No. 14)

Yan Zhang, Ph.D. Market Analyst BGI Tech
Joyce Peng, Ph.D. Marketing Director BGI Tech

Improving Cancer Detection and Therapy

As one of the most prevalent causes of death across the globe, the burden of cancer is still sharply increasing, predominantly due to the aging population. As such, the current focus of many researchers is on how to accurately diagnose and treat various cancer types. However, human cancers usually carry several different genomic variations, such as copy number variation and point mutations, which essentially lead to tumor heterogeneity. These tumors therefore display different cellular morphology, gene expression, metabolism, motility, proliferation, and metastatic potential. This phenomenon occurs both within individual tumors and between different tumors in the body.

This inherent variation of cancer cells causes significant issues in the development of targeted therapies. Drug development has previously focused on the genomic differences between complex mixtures of cells, employing techniques that may obscure the heterogeneity of single cells, leading to the development of less efficient treatments.

Single-cell genomics can facilitate the elucidation of cell lineage relationships. Individual cells can be isolated using micromanipulation with a mouth pipette or by serial dilution and the genome sequenced using next-generation sequencing technologies. Through a comparison of the genomes of individual cells, researchers can determine the mutation profile influencing a change in cellular morphology in the subsequent generation of cells. The major applications of this technique include profiling scarce clinical samples (i.e., circulating tumor cells), pre-implantation genetic diagnosis, measuring intra-tumor heterogeneity, guiding chemotherapy, and cancer cell evolution analysis during tumor progression.

Techniques—Whole-Genome Amplification

There are several different techniques that can be implemented to amplify the genomes of single cells for sequencing. There are however pros and cons associated with each of these, which will impact the quality of the sequence data:

PCR-Based Methods

A staple of the molecular biology laboratory, PCR is an accessible and reliable amplification technology.

  • Degenerate oligonucleotide-primed PCR (DOP-PCR) uses semi-degenerate oligos (where a section of the oligo uses degenerate bases—essentially a mixed base, which is ideal for amplifying an unknown target) and an increasing annealing temperature.
  • Primer extension pre-amplification PCR employs a pre-amplification step to add binding sites to the fragmented genome, allowing whole-genome analysis. This protocol uses random primers and a low annealing temperature. However, the use of Taq polymerase limits fragment length to 3 kb and can introduce sequence errors.

Both the primer extension and degenerate oligo methods have been found to exhibit amplification bias, where a sequence is over-represented in the amplified DNA due to preferential binding of primers to specific regions. 

  • Multiple annealing and looping based amplification cycles (MALBAC) is a newly developed PCR-based method of whole-genome analysis. It uses quasi-linear amplification to provide uniform resulting data, while generating low rates of false positives and negatives. The genome coverage at the single-cell level is less uniform than when sequencing in bulk; this approach is unable to detect approximately one-third of SNPs compared to bulk sequencing.

Non-PCR-Based Methods

  • Multiple displacement amplification (MDA) can rapidly amplify minute quantities of DNA. Hexamer primers are annealed to template DNA, and the synthesis is performed using a high-fidelity polymerase at a constant temperature. Compared to conventional PCR techniques, MDA generates larger products (the average product length is >10 kb) with lower error rates, which are optimal for the detection of copy number variation and structural variation.

Advancing the possibilities of single-cell sequencing in human research, BGI Tech (a subsidiary of BGI) has developed an innovative end-to-end solution for genomics and transcriptomics analysis at the single-cell level. Based on the findings mentioned above, MDA has been further enhanced and incorporated into BGI’s whole-genome amplification protocol, within the single-cell sequencing service. Following whole-genome amplification, quality control protocols employ eight housekeeping genes as internal indicators, thus identifying sequence-ready samples in a cost-effective manner, without affecting the genes of interest. 

Combining whole-genome amplification with next-generation sequencing, BGI Tech is able to obtain single-cell sequence data for disease research and the development of personalized medicine. The methodology can be applied to discover genetic information within single cells, allowing for the identification of the mutations associated with the development of cancerous cells, and consequently those that play a more causal role in disease progression and metastasis.

Case Study

Tumor heterogeneity presents a significant challenge when attempting to determine clonal evolution and the identification of the genes underlying cancer progression. Here, we analyze tumor evolution and the intratumoral heterogeneity of myeloproliferative neoplasm (1) and clear cell renal cell carcinoma (ccRCC) (2). Both cases confirm high heterogeneity within cancer cell populations, with several novel candidate mutations identified. Thus, single-cell sequencing is proven to generate a fresh wave of biological insights into cancer research.

Case 1—Myeloproliferative neoplasm

Single-cell sequencing of a sample from a patient with a myeloproliferative neoplasm was performed to reconstruct tumor ancestries and identify the mutations driving this cancer.

1. High-throughput whole-genome single-cell sequencing was conducted on two single cells from a lymphoblastoid cell line. Whole-genome recovery, amplification uniformity, sensitivity, and specificity were evaluated.

2. Whole-exome single-cell sequencing was performed on 90 single cells from the patient. The sequencing data from 58 cells passed the quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution (Figure 1). 


Figure 1. Method of sequencing samples from a JAK2-negative myeloproliferative neoplasm patient.

Fifteen genes were found to be likely to contain protein-damaging mutations (including protein truncation). Eight genes were subsequently identified, which had a significantly higher prevalence of protein-function-alternative somatic mutations (Figure 2). A single-cell sequencing method was therefore established, which provides detailed analysis of a variety of tumor types, including those with high genetic variation between patients.

 


Figure 2. Key gene identification of the patient. The driver gene prediction analysis of the 18 candidate genes is indicated as a Q score. The vertical axis is the Q score, and the circle diameter indicates the cell mutation frequency.

Case 2 – Clear cell renal cell carcinoma

ccRCC is the most common kidney cancer, but the patients share very few common mutations. To investigate the intratumoral heterogeneity of ccRCC, single-cell exome sequencing was conducted on a ccRCC tumor and its adjacent kidney tissue, enabling the generation of a detailed intratumoral genetic landscape at the single-cell level (Figure 3).

Quantitative population genetic analysis indicated that the tumor did not contain any significant clonal subpopulations and that mutations that had different allele frequencies within the population also had different mutation spectrums.


Figure 3. Research strategy for single-cell exome sequencing of a ccRCC tumor and its adjacent kidney tissue.

Conclusion

Research into the development and progression of cancer is a complex task. Conventional strategies for cancer research are often labor intensive and limited by throughput, making them inefficient. The use of next-generation sequencing for genome-wide variant analysis therefore presents a significant development within this field. Since cancer is principally a disease of accumulated genomic alterations, whole-genome analysis will help to devise novel personalized strategies for cancer diagnosis and therapy.

As demonstrated here, single-cell sequencing has been proven as a valuable tool within human disease research. However, the development and fine-tuning of these methods is still in progress. Single-cell isolation is crucial for sequencing efficiency, and BGI Tech is currently in the process of testing new automated cell-isolation technologies with the aim of enabling accurate sequencing of single cells to consequently develop even more effective cellular targeted therapies. It is also expanding its R&D pipeline to enable the expansion of single-cell applications to cover stem cells and the human brain.

Yan Zhang, Ph.D., is market analyst and Joyce Peng, Ph.D. (joyce.peng@bgiamericas.com), is marketing director at BGI Tech.