Identifying New Therapeutic Targets for Cancer With 3D Genomics
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Genomics is the study of an organism’s complete collection of DNA – known as the genome – that contains all the information required for the organism to develop and function. Next-generation sequencing technologies have allowed researchers to “read” genomes in great detail and with increasing speed and affordability. These techniques have many applications in fields such as diagnostics, drug discovery and cancer research.
Now, 3D genomics approaches are coming to the fore. Researchers using these new tools can explore the structural variation in genomes and discover how the physical proximity of different parts of the genome can affect each other. 3D genomics is particularly useful for cancer research, expanding our ability to characterize cancer genomes and identify potential therapeutic targets.
Technology Networks spoke with Anthony Schmitt PhD, senior vice president of science at Arima Genomics, to learn more about the advantages and applications of 3D genomics.
Sarah Whelan (SW): Can you give us a brief explanation of what 3D genomics is and how it works?
Anthony Schmitt (AS): DNA sequencing produces a string of letters representing the bases of DNA in a linear fashion. However, in reality, DNA is like a ball of spaghetti tightly packed within the nucleus of a cell. 3D genomics seeks to help researchers understand the three-dimensional arrangement of that DNA, its effects on genome function and how that arrangement changes across cell types, time, in a disease state or response to a particular treatment.
Arima Genomics' 3D genomics technology uses a process called Hi-C (high-throughput chromatin conformation capture). First, we fragment and re-ligate the DNA molecules in spatial proximity in the cell's nucleus. We then sequence across the ligation junctions to identify DNA molecules that were spatially proximal in the cell's nucleus.
Credit: Arima Genomics
SW: What are some advantages of 3D genomics over standard "linear" genomics approaches?
AS: To date, more than 300 peer-reviewed studies have been published using Arima Genomics technology across various applications, including cancer, neurobiology, immunology and other complex diseases. The common thread across this research is that there is critical information encoded in the 3D structure of the genome that simply cannot be derived from just the linear sequence.
The main advantages that 3D genomics offers over linear genomics are:
- Capturing more information in a single assay – instead of just obtaining the linear sequence, 3D genomics provides insights into the genome's sequence, structure and regulatory landscape.
- 3D genomics provides a high-resolution map of long-range genetic loci interactions that do not involve changes to the genome's sequence. This information enables the identification of promoter–enhancer interactions for gene regulation studies, detection of structural rearrangements and scaffolding contigs for genome assemblies to define chromosomes de novo.
- 3D genomics can also be integrated into any standard NGS workflow without the need for additional equipment.
SW: What kind of data processing/bioinformatics analysis is required after sequencing? How does this differ from linear genomics?
AS: 3D genomics experiments use the same next-generation sequencing instruments as linear genomics experiments. This means that the output files of 3D genomics are similar to those created with linear genomics and can be processed with many of the same open source bioinformatic tools. However, unlike linear genomics, the processed 3D genomics data is often visualized as a 2D heatmap. This heatmap lays out all the chromosomes in a genome from which you can extract information for genome assembly, detection of structural variants and gene fusions.
SW: How can 3D genomics be applied to cancer research? How can these techniques inform the discovery of novel biomarkers and therapeutic targets?
AS: Cancer genomics is where 3D genomics really shines, and we are so excited about the potential here. Structural variants are known to play a significant role in cancer. More than 95% of cancers have one or more somatic structural variants, and at least 30% of cancers have a known pathogenic structural variant. This information can be used in diagnosis or patient stratification. However, many important driver mutations or gene fusions remain undetermined using standard-of-care diagnostic tools.
3D genomics can identify structural variants and gene fusions that play an important role in tumorigenesis and can be useful in identifying druggable targets both for treating individual cancers and developing new cancer therapies.
Some of our most recent findings show that using Arima technology can detect clinically actionable targets in about 53% of previously characterized driver-negative patient samples. This fall, we will present some of this work at the American Society for Human Genetics in Los Angeles and the Association for Molecular Pathology in Phoenix.
Here you can hear our customer Matija Snuderl MD share how 3D genome analysis is being used to identify novel molecular drivers and potential therapeutic targets in tumors with no previously detectable drivers. One fascinating example is how he was able to identify a novel PD-L1 translocation that was missed by DNA and RNA sequencing in a patient with pediatric glioma.
SW: Can you give us some examples/case studies of how 3D genomics has been used in research?
AS: Our customers use our technology across a wide range of applications. Here are some of our favorite recent examples.
As part of their multi-omic approach to understanding breast cancer, scientists at Novartis used Arima technology coupled with CRISPR and single-cell RNA sequencing to reveal novel noncoding regulatory elements that contribute to oncogenic transcriptional programming and cell proliferation of ER+ breast cancer cells.
Another group found that H1 linker histones are a critical regulator of gene silencing through localized control of chromatin compaction, 3D genome organization and the epigenetic landscape. This example is exciting because it demonstrates just how much there is to learn about epigenetic regulation and the 3D genome.
SW: What do you envision the future of 3D genomics to look like? How do you think this technology will develop in the future?
AS: We are very excited about the future of 3D genomics. We believe utilization will continue to grow and become a standard technology used to provide essential insights into 3D gene regulation in human health and disease research. Additionally, the use of 3D genomics as a tool to detect (and characterize) structural variants from short-read sequencing data is very promising.
Together, I think we are just scratching the surface of understanding how the 3D genome impacts human health. We believe the foundational studies being done today in areas like cancer, immunology and neuroscience will one day enable the development of new diagnostic tools and approaches.
Dr. Anthony Schmitt was speaking to Sarah Whelan, Science Writer for Technology Networks.