A fundamental shift in science will bring about a fundamental shift in healthcare, says Garvan Institute’s Professor Joseph Powell.
The human body is made up of 30 trillion cells, each of which contributes to health and disease in a unique way. Current clinical testing masks this variety by using ‘bulk’ techniques that generate data as an average signal over millions of cells. But technologies that can read and compare the genetic material inside hundreds of thousands of single cells in parallel are rapidly uncovering a new resolution of disease.
The potential is vast. What if we could detect the rare cancer cells likely to cause a relapse at the time of treatment? What if we could treat autoimmune disease with drugs specific enough to act only on malfunctioning cells, significantly improving treatment while reducing side effects?
This clinical impact is not a matter of if – it is a matter of when. In two articles recently published in Nature Reviews Genetics, we reviewed the steps required to translate single-cell genomics from the laboratory to the clinic and outlined emerging research to understand how our DNA acts at the level of individual cells, revealing unprecedented detail of human biology and disease.
Single-cell genomics has seen a rapid evolution. The first paper that reported the extraction and sequencing of genetic material from a single cell was published in 2009. Today, technology platforms have advanced enough to provide accurate data for hundreds of thousands of cells in parallel. Single-cell technologies are central to more than 200 clinical trials worldwide exploring the potential of improving diagnostics and treatment for cancer and immune disease.
One promising application is the detection of cancer cell ‘states’ – a critical early warning sign of rare and dangerous cancer cells.
An example is Associate Professor Christine Chaffer’s team at Garvan, who are investigating changes in breast cancer cells that can ‘tip’ them from one state to another, resulting in cells both resistant to chemotherapy and at risk of activating and causing sudden relapse. My laboratory is conducting similar work in brain cancer, where the detection of rare and dangerous cells following surgery could transform treatment strategies.
Such tests, integrated into the clinic as a part of routine monitoring, have the potential to be lifechanging for cancer patients.
Tumours can also splinter off cells at the early stages of development before they become aggressively metastatic. These circulating tumour cells are a critical early trigger sign, detectable by single-cell genomics that could help triage patients into more effective therapies much earlier.
We predict that single-cell sequencing will become a key component of the diagnostic process over the next decade. There is currently enough preclinical proof of principle research to invest in implementing single-cell technologies into practice, which now require clinical validation studies and regulatory accreditation.
The level of investment will determine the rate at which single-cell technologies are integrated into the clinic – whether it will happen over 20 years or over five years. All the pieces of jigsaw are there; they just need to be put together through partnership with organisations that have the diagnostic genetics capabilities and the logistics of sample handling that can scale single-cell technologies from one site to every site.
In the early 2000s, the cost of sequencing a human genome was around 1 million dollars. Thanks to advances in technology, the cost is now in the hundreds. For single-cell sequencing, similar advances in technology and methods will reduce costs so that they can be incorporated into clinical routine.
Single-cell genomic technologies are allowing us to investigate the cellular makeup of tissues at unprecedented level. The techniques have become powerful enough to take this concept even further – to compare single-cell differences between thousands of individuals.
In each of our 30 trillion cells is an entire copy of our DNA – a string of three billion bases. We know that the DNA differences between people lead to functional differences in every cell in our body.
The question we are asking ourselves is: can we not only predict whether a patient will respond to current therapy based on their DNA differences, but also specifically treat malfunctioning cells? This would lead to new therapies and a new level of therapy personalisation.
Our vision is for every patient to walk into the clinic and have access to a simple, inexpensive test that will inform their doctor of which treatment they will have an increased chance of responding to.
This goal could be achieved by analysing SNPs (pronounced ‘snips’) – locations along our genome that vary among people and occur once in every 1,000 DNA bases. While a person has the same version of a SNP in every cell, we found their action varies between cell types, including the cells that are the target for therapies.
This has implications for therapies such as cancer immunotherapy, and some autoimmune disease drugs, which can be highly effective at treating the disease, but often only work in 20% of patients.
We found that SNPs can lead to vast changes in a patient’s likelihood of responding to a drug. For example, by studying single cells of people with Crohn’s Disease, we discovered that a single SNP can impact the efficiency of their treatment response by 10%. By increasing the number of SNPs we analyse, we will likely predict even more significant differences in treatment effectiveness between individuals. We are currently in the process of expanding our studies into clinical trials.
The ultimate goal of this research, and the aim of our Genomics-led Drug Discovery Program at Garvan, is to use single-cell genomics to identify pathways and genes that are disrupted in disease – and crucially, to target them to move this pathway back into the healthy state. This could be achieved using RNA-based therapies that can change the way genetic material is processed inside cells, thereby changing their function.
We are developing the theory and the solutions to scale up the experiments and developing the AI and the RNA methods to identify cellular targets. Further advances in data generation and analysis methods will enable clinical translation that will fundamentally change how we detect, monitor and treat disease.
Source – Garvan Institute of Medical Research