The concept of personalised medicine actually dates back hundreds of years in the West, transforming from an idea into practice especially with the sequencing of the human genome at the turn of the 21st century. Personalised medicine (more recently referred to as precision medicine) looks at an individual’s unique genetic identity or molecular phenotype to better diagnose and select treatment options – increasing the chances of a successful outcome and reducing possible adverse reactions by allowing physicians to go beyond the ‘one size fits all’ treatment model. It also predicts someone’s susceptibility to diseases, which can be used to help avoid or reduce the extent to which they will experience that disease.
Our current difficulty in predicting treatment success for many diseases and conditions means that clinicians have no choice but to follow a less than optimal approach to prescribing drugs and other treatment options.
One of the first steps in personalising medicine is identifying biomarkers – found in blood and other body fluids such as urine, sputum and cerebrospinal fluid (CSF), or tissue – that represent normal or abnormal cell functioning. Measuring and quantifying biomarkers can better educate doctors about the characteristics of an individual’s condition or disease to help them make informed decisions about the value of possible treatments.
Over the past decade, new technologies have been emerging to advance personalised medicine, to treat and monitor patients more effectively and in ways that better meet their needs. Digital polymerase chain reaction (dPCR) is one of these technologies. Scientists are recognising dPCR as a potentially useful tool for finding these biomarkers and using them to develop assays that will allow for more personalised medicine. Significant progress is already being made in applying the technology to personalised cancer care and treatment.
Digital PCR is a nucleic acid molecule counting method that works by finely partitioning the sample such that either zero or only a small number of nucleic acid molecules of interest (i.e. targets) are present in each partition. This partitioning happens either by dividing the sample into chambers or droplets (or historically, wells), the sample is then thermocycled to endpoint in a PCR reaction mix. Scoring of the positively and negatively fluorescent partitions gives users an absolute measurement of the target concentration and, unlike quantitative PCR (qPCR), does not require the user to compare an unknown with a standard, thus eliminating the need for a standard curve.
Providing absolute counts – corrected by Poisson statistics – and eliminating the standard curve reduces error and improves precision, and in turn enables improved day-to-day and lab-to-lab reproducibility.
While there are several approaches to performing dPCR, droplet digital PCR (ddPCR) has been the most widely adopted. Arriving in late 2011, ddPCR offers greater ease-of-use, enhanced performance, higher throughput and lower costs compared with previously existing dPCR implementations, such as fluidic chip-based methods. Of the available droplet-based dPCR systems, Bio-Rad Laboratories offers the QX200 ddPCR system, which partitions samples into 20,000 droplets to provide an absolute measure of target DNA molecules with the capability for high accuracy, precision and sensitivity. This single-copy PCR resolution is enabling researchers to accelerate discoveries and develop new strategies for developing personalised treatments (see Figure 1).
Figure 1: In the Bio-Rad droplet digital PCR workflow, reaction mixes are partitioned into droplets where a PCR reaction takes place. A reporter dye will emit a fluorescent signal to be read in a droplet if the target sequence is amplified there
Sample partitioning provides another key benefit. It effectively reduces the high concentrations of background nucleic acid, lessening competitive effects, which allows for greater discrimination between similar sequences.
The level of sensitivity offered by Bio-Rad’s ddPCR systems in quantifying cancer biomarkers overcomes the limitations posed by other methods. Cancer-associated mutations often evade detection due to their low concentrations relative to the background of wild-type DNA in a given sample. But because of its high sensitivity, the QX200 system can easily scale to quantify target concentrations as low as one out of 1,000,000 (0.0001%) total copies. What was previously undetectable with other methods, can now be quantified.
Measuring predictive biomarkers
Dr Muneesh Tewari, a University of Michigan researcher, sees the immense potential of biomarkers, such as microRNAs (miRNAs), as a diagnostic tool for cancer and other diseases. His ultimate goal is to develop biofluid-based approaches for disease detection and monitoring.
Small regulatory RNA molecules, miRNAs have diverse cellular functions, making them a hot area of biological research. Unfortunately, the limitations of traditional technologies such as quantitative real-time PCR (qPCR) have prevented researchers from measuring these molecules in serum or plasma with confidence because they cannot be reliably compared from one day to the next.
To address this concern, Dr Tewari performed a side-by-side test of qPCR and ddPCR, in which ddPCR demonstrated greater precision – up to 72% lower coefficients of variation – with respect to qPCR-specific variation. When comparing the two methods for detecting miRNAs in serum, ddPCR improved day-to-day reproducibility seven-fold relative to qPCR. It was also able to distinguish between case and control specimens with much higher confidence. These tests, published in Nature Methods, show that ddPCR standardises the microRNA quantification process, making it feasible for results to be compared at different times and presumably across different institutions. The precision and reproducibility of ddPCR will prove useful for further development of miRNA and other nucleic acids as circulating blood biomarkers.
Broad applicability in cancer care
A range of additional applications could benefit from use of ddPCR – among them, predicting patient outcome in ovarian cancer. Tumour-infiltrating T-lymphocytes, or TILs, are a special type of tumour-attacking immune cells that are known to improve cancer survival. Because an increase in TILs is a sign of the body’s immune response to cancer, clinicians can use TIL count to predict survival outcome and determine which patients may benefit from targeted immunotherapies. Unfortunately, current tests lack the precision necessary for clinical application.
However, in 2013, Dr Jason Bielas, Associate Member of the Public Health Sciences and Human Biology Divisions at the Fred Hutchinson Cancer Research Center, recognised that TILs have a genomic signature reflecting the rearranged chromosomal sequence of the T-cell receptors (TCR) expressed on the surface of each TIL. Dr Bielas found that ddPCR has the ability to comprehensively quantify these unique signatures and determine the number of TILs in a tumour. Based on this knowledge, the team developed the ddPCR-based QuanTILfy assay, which was used to demonstrate that accurate quantification of TIL counts can be used as a prognostic indicator for ovarian cancer survival. Moving forward, Dr Bielas and his team hope to translate this work more widely to examine the clinical utility of TILs as biomarkers of survival in a variety of cancer types. They’re also interested in developing a robust, standardised, DNA-based assay to quantify TILs.
Using blood instead of tissue from traditional biopsies, doctors will be able to track disease progression and resistance to certain treatments in each patient
And finally, towards fulfilling the promise of personalised medicine, the technology can more rapidly monitor the effectiveness of cancer treatment and adjust it as necessary in ‘real-time’ through the approach of the liquid biopsy, seen as the ‘holy grail’ of cancer therapy. Using blood instead of tissue from traditional biopsies, doctors will be able to track disease progression and resistance to certain treatments in each patient due to the presence of DNA from healthy and tumour cells sloughed into the bloodstream. One of the challenges preventing the test from becoming a clinical reality has been finding the cancerous DNA in a vast sea of healthy DNA. Fortunately, scientists are getting closer – thanks in part to digital PCR.
Recently, Dana-Farber Cancer Institute researchers Geoffrey Oxnard and Cloud Paweletz showed that genotyping cell-free DNA (cfDNA) and circulating tumour cell using ddPCR can detect resistance to cancer treatment up to 16 weeks earlier than radiographic imaging, which is the standard clinical measure.
The researchers measured the amount of cfDNA in blood plasma from advanced non-small cell lung cancer (NSCLC) patients receiving targeted therapy, and used ddPCR to measure several DNA mutations in their plasma. Particular ddPCR assays were developed for the mutations in different stages of treatment – in which they observed notable differences in the mutation counts – demonstrating that non-invasive genotyping of cell-free plasma DNA has potential as a clinical biomarker. This information provides new insight into the pharmaco-dynamics of targeted therapies, and shows promise for allowing oncologists to personalise cancer treatments throughout the course of the disease.
Where the future lies
While the promise of personalised medicine is still largely that, researchers, such as those described in this article, are making significant strides in the hopes of developing targeted therapeutics and predictive diagnostic tests. The advent of practical, higher throughput digital PCR in the form of ddPCR is proving to be an important tool aiding in the acceleration of this new world of tailored medicine.
