EpiSwitch. Photo/Oxford Biodynamics
by Oxford Biodynamics
Effectiveness trials are urgently needed in cancer care to determine the best approach to personalized medicine. Practical, new solutions that exploit the power of 3D genomics are emerging.
Cancer is a hugely complex, multifaceted disease marked by uncontrolled proliferation of cells. Many interconnected layers of biological systems give rise to the clinical features of cancer. Understanding this complex network is a challenge, and it obscures our understanding of early cancer detection, cancer progression, and the discovery of new preventive and therapeutic interventions. Improved biomarkers can help understand disease drivers and better categorize patients by their potential disease risk, prognosis, or potential response to treatment.
The prevailing “multi-omics” approach generates large, expensive, high-dimensional datasets of many types, including genomic, epigenomic, transcriptomic, proteomic, and metabolic profiles. It depends on trust that subsequent machine learning data analyzes will capture meaningful insights from this data, even though there is often significant, naturally high, biological variability and noise.
Aided by rapid developments in high-throughput technologies, such as next-generation sequencing, this approach produces an unprecedented amount of data. The integration and interpretation of these large datasets to extract useful information is complex and requires increasingly large-scale collaboration. Blindly adding more data can be counter-productive and get in the way of uncovering actionable insights. If the clinical relevance of the data is low, this places an increased burden on statisticians and computational algorithms to cut through the increasing noise.
Despite enormous efforts and resources to develop valid biomarkers for well-classified patients for precision medicine, cancer biology has eluded any effective, reliable solutions.
3D Genomics: The Great Simplifier
Almost every cell in our body contains two meters of DNA, which is intricately folded to fit into the cell’s nucleus. This three-dimensional organization of DNA, your 3D genome, appears to be as important as the genetic code in controlling cellular fate and provides a wealth of untapped information relevant to health and clinical outcomes.
Looking at 3D genomics offers fundamental advantages. 3D genomic organization represents the integration of multiple multitomic signals1. Genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes can all be reflected in specific 3D genomic changes. 3D genomic shape acts as a powerful regulatory gatekeeper controlling how gene activity is modulated. DNA is joined in loops and brings distant parts of the linear genome together, thereby influencing each other. These loops are generally stable but can act as switches that undergo changes in response to the effects of genetics, environmental cues, metabolism, and cell-to-cell communication.2.
Importantly, common 3D genomic patterns can often be identified that are shared both universally and uniquely across certain diseases. This is because they are highly prevalent binary events, with a high signal-to-noise ratio. This approach is highly informative for patient classification.
3D genomics links multiomic complexity and clinical phenotype. Over the past 10 years, mounting evidence has shown that 3D genomic profiles can be effective biomarkers.3. With the right technology and methodology, it can boil down complex biological layers of regulation into a straightforward series of markers that provide robust stratification of clinical outcomes for challenging diseases.
Looking at the system is not just about cancer
Cancer is a systemic disease4. Immuno-oncology has greatly strengthened this notion. The immune system and microenvironment surrounding a tumor play a major role in determining whether a cancer spreads, stabilizes, or responds to therapy. Biomarker approaches that look exclusively at tumor biopsies miss this critical information.
It is well documented that when a set of genetic loci undergoes 3D genomic changes, this can be seen not only in circulating tumor cells, but also in white blood cells, even in the early stages of cancer.5. These changes in immune cells represent systemic changes associated with cancer and can be used to identify telltale information about distant tumors. In other words, a systematic barcode6 A convenient liquid biopsy composed of a set of binary 3D genomic biomarkers (a chromosome conformation signature, CCS) can serve as a biomarker.
The art of knowing where to look and what to measure
Over the years, analyzing 3D genomic interactions has involved a family of methods called chromosome conformation capture (“3C”, or its more common universal derivation, Hi-C). These, again, generate huge amounts of data and rely on deep sequencing to capture the presence of meaningful markers. However, the potential space for 3D genomic interactions is vast. Without a way to filter, these methods inevitably pick up many non-specific interactions that are transient and clinically meaningless.
This introduces very high levels of random noise, dwarfing the signal from key regulatory loops. Low sensitivity and reproducibility result in high data mining costs and low complexity datasets, which have limited this approach in research applications.
The story so far
Overcoming these limitations and “reducing to practice” an end-to-end platform for the discovery, development, and commercial clinical operation of 3D genomic assays led the founders of Oxford Biodynamics (OBD), an Anglo-American biotech, to develop it. EpiSwitch Platform. The EpiSwitch Explorer Array, a commercial whole-genome microarray developed in collaboration with Agilent, can simultaneously interrogate ~1 million potential 3D genomic interactions.
The high-throughput array is encoded with probes that select only for highly reproducible 3D genomic markers, thereby generating rich, clinically meaningful data. Once significant marker leads are identified, these are translated into a MIQE-compliant qPCR format, reduced to a minimal signature feature, validated in independent clinical cohorts, and then tech-transferred to a CLIA-lab for independent validation and operation. Using standard equipment.
Using established EpiSwitch technology and methodology, OBD has now developed its own portfolio of tests, aimed at enabling clinicians to easily screen and triage patients using only blood tests. The first cancer trial to use 3D genomics was launched in 2022 – the EpiSwitch CiRT (Checkpoint Inhibitor Response Test).
It is a smart blood test for cancer patients that provides guidance in navigating the most difficult challenges of immunotherapy, such as treatment planning, pseudo-progression, and adverse events.7. The first test of its kind predicts, with 85% accuracy, a person’s therapeutic response to immune checkpoint inhibitors (ICIs), a family of widely used immunotherapies that give some patients a real boost to their cancer recovery and survival.
While they may offer an unprecedented extension of life, only one in four patients see an overall anti-cancer benefit, and many remain on the drug despite positive results, significant expense, and up to 40% reduction in immune risk. Related side effects, which can be serious8.
By exploiting systemic 3D genomics, which incorporates signals from the host immune landscape, CiRT has demonstrated best-in-class performance in over 14 broad oncological indications.9. This test is commercially available as a US CLIA-lab service and is routinely adopted by oncologists, surgeons, and interventional radiologists.
OBD believes that the adoption of 3D genomic testing will enable precision medicine, such as immuno-oncology treatments, to be more effective, safer and more accessible, allowing them to be used more effectively in patients who are likely to respond well.
1: Tordini, F., et al. (2016). Genome conformation as an integrator of multi-omic data: an example of diffuse damage in cancer. Frontiers in Genetics, 7. https://doi.org/10.3389/fgene.2016.00194
2: Alshakar, H., et al. (2022). Monocytes acquire prostate cancer specific chromatin conformations in indirect co-culture with prostate cancer cells. in front Oncol., 12. https://doi.org/10.3389/fonc.2022.990842
3: Crutchley, JL, et al. (2010). Chromatin conformation signatures: ideal human disease biomarkers? In Biomarkers in Medicine (Vol. 4, Issue 4). https://doi.org/10.2217/bmm.10.68
4: Coussens, LM, & Werb, Z. (2002). Inflammation and cancer. In Nature (Vol. 420, Issue 6917). https://doi.org/10.1038/nature01322
5: Jakub, JW, et al. (2015). A pilot study of chromosomal aberrations and epigenetic changes in peripheral blood samples to identify patients with melanoma. Melanoma Research, 25(5). https://doi.org/10.1097/CMR.0000000000000182
6: Bastonini, E., et al. (2014). Chromatin barcodes as biomarkers for melanoma. Pigment Cell and Melanoma Research, 27(5). https://doi.org/10.1111/pcmr.12258
7: Oxford BioDynamics Plc. (2022). EpiSwitch CiRT. https://www.mycirt.com
8: Zhao, B., et al. (2020). Efficacy of PD-1/PD-L1 blockade monotherapy in clinical trials. Therapeutic Advances in Medical Oncology, 12. https://doi.org/10.1177/1758835920937612
9: Hunter, E., et al. (2021). Development and validation of blood-based predictive biomarkers for response to PD-(L)-1 checkpoint inhibitors: evidence from a global systemic core of 3D immunogenetic profiling in multiple oncological indications. MedRxiv, 2021.12.21.21268094.