Single-cell DNA Analysis: Driving Dynamic Changes in the Treatment of Cancer
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Complex diseases, such as cancer evolve, so understanding genetic variability at the single-cell level is vital. Ensuring researchers have the tools to unlock single-cell biology enables the discovery, development, and delivery of precision medicine.
We recently spoke to Charlie Silver, CEO and Co-Founder of Mission Bio, to learn more about how they are helping researchers profile mutational heterogeneity at the single-cell level.
Laura Lansdowne (LL): What challenges are associated with tailoring treatments to specific variants in oncology?
Charlie Silver (CS): There are a couple of challenges. Current bulk sequencing technologies don’t always have the sensitivity to detect rare variants that may be treatable. Additionally, when a variant is detected through bulk sequencing that may match with a current therapy, it’s not always known whether that mutation is clonal (in the majority of the disease cell population), or subclonal (in the minority of the disease cell population). If the mutation is subclonal, it cannot be teased apart with bulk data, and the targeted treatment may not be as effective. Only single-cell targeted DNA sequencing can reveal subclonal populations with precision. Also, while current bulk sequencing technologies can provide a view of the multitude of variants a tumor sample may have, the data isn’t specific or sensitive enough to give an accurate representation of whether mutations are co-occurring in specific cell populations. If treatments exist to target both mutations, it’s helpful to know whether these mutations are co-occurring in the same cell or in different cells, to then better tailor treatments.
LL: How is Mission Bio enabling researchers to profile mutational heterogeneity at the single-cell level?
CS: Whereas bulk sequencing provides an average readout of a mutational profile for the 10s of 1000s of cells in any given sample, the Mission Bio Tapestri Platform uses droplet microfluidics to reveal the mutational profiles of each single cell in a bulk sample, producing 1000s of mutational profiles for each cell in a sample, rather than one mutation profile for the one sample.
Ruairi Mackenzie (RM): What is the purpose of a single-cell DNA panel?
CS: A single-cell DNA panel allows researchers to focus in on the specific actionable areas of the genome where there is some level of heterogeneity. When we focus our efforts on these specific genes/regions of interest based on the particular disease or indication (e.g. acute myeloid leukemia, chronic lymphocytic leukemia) rather than expanding to the whole genome, this can mean saving both money (lower sequencing costs) and time for analysis, accelerating research and drug development.
RM: What is the process for a researcher hoping to design a panel using Designer?
CS: It’s a very simple process. First, a customer creates a login for an account to use Tapestri Designer. A customer then provides the list of genes or targets they are interested in further exploring (signals of heterogeneity) for their custom panel. They then submit this list on the web-based user interface.
RM: How quickly will researchers receive their panels after the design process is finished?
CS: After completing this process, which typically takes minutes, the design is also typically completed within minutes.
RM: How can artificial intelligence (AI) and machine learning (ML) algorithms further improve single-cell DNA analysis?
CS: Panel design is one area where AI/ML will help. With every custom panel design, we perform a QC run, which gives us a view of the performance of the panel. This includes available data on what genes/targets perform well or poorly, and what may be over/under represented in the panel. We then use this information to mix the genes (or primers) at different proportions to account for over/under performers. We feed this information back into the design pipeline to improve our design pipeline algorithms and help inform future custom panel designs. This is the first commercial multiplex panel designer that leverages AI/ML to optimize panel design, so every customer gets the best performance at the lowest cost on the first shot.
Big data – This is where single-cell data actually necessitates AI. Our output is a set of high dimensional data where relationships are not linear, giving rise to a real data set in genomics that lends itself to AI/MI. Given that our matrix of single-cell data is at least four orders of magnitude greater than standard bulk sequencing, we also have a larger body of data to help us train and improve our variant calling. Our AI/ML engine will help inform predictive modeling with clinical/patient outcomes at the back end of each sample processed. Our solution is bookended by machine learning algorithms, improving the quality and cost of the assay at the front end – whilst increasing the power of the data at the back end. These tools power our best-in-class technology so it can improve the precision of sequencing by 50x, enabling our customers to accelerate their research and drug development in service of better patient care.
Charlie Silver was speaking with Laura Elizabeth Lansdowne and Ruairi J Mackenzie, Science Writers for Technology Networks.