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July 15, 2022 by Elise Morrison 5 min read

Q&A: Single-Cell DNA Sequencing for Novel Cancer and Precision Medicine Analysis

In an Inside Precision Medicine webinar, Mission Bio’s CMO Todd Druley, MD, PhD, and Senior Director of Clinical Applications Aaron Llanso discussed how to apply single-cell DNA sequencing to MRD, clonal hematopoiesis (CHIP), solid tumor, and precision medicine, as well as the translational applications of the Tapestri Platform throughout a patient’s journey with disease. A live Q&A session followed their discussion – here are just a few of the great questions posed by attendees. Questions and responses have been lightly edited for length and clarity.

 

What are the advantages of single-cell DNA sequencing versus single-cell RNA sequencing?

Aaron Llanso: That’s a great question. So I think that single-cell technologies are evolving rapidly and there’s lots of applications in which both these technologies are very powerful tools. In the research space, especially on the discovery side of the spectrum, single-cell RNA sequencing is a fantastic way to profile phenotypic profiles of these particular populations and to look at gene expression profiles that are functions of particular pathways that might be targetable.

On the single-cell DNA sequencing side, we’re really specializing in sensitive detection of single nucleotide variants, of course, with copy number and other structural variants as mentioned in the talk. This can really be done with power that’s not possible by single-cell RNA sequencing. I think there are great value-add approaches such as being able to impute some mutations from expressed transcripts, but there is considerable biology within the noncoding genome as well as in many of these indications.

The ability to very sensitively resolve the subclonal architecture is really a strength for single-cell DNA. As we move forward in multi-directions for both technologies, we’re starting to uncover novel biomarker correlations. But I think it’s really about choosing the right tool for the job and that depends on what types of questions our researchers or clinicians want to ask.

I would point out that from an analyte perspective, I think with a targeted-based platform as Mission Bio has, it’s really well suited more towards translational research and answering refined questions that follow on some of the discovery, whole genome or whole transcriptome type questions. That’s a little easier to translate towards the clinic. And I think that’s where we’re really seeing the velocity or the momentum of a lot of the clinical utility published in the translational research space pulling us towards hopefully implementing this to impact patient management in the future.

 

How does having a complete understanding of the clonal landscape impact treatment regimens?

Todd Druley: We have good anecdotal examples where you take this bulk sequencing assay and there are rare clonal or subclonal mutations within the population and it’s difficult to tell if that mutation is in a separate clone. It’s actually impossible to tell is that in a separate clone, is that co-occurring with other mutations?

Patients get treated by the most common treatable mutation within the bulk sequencing result. However, now all you’re doing is selecting for these subclones that have resistance mutations or might have different therapeutic targets. What we see a lot of times is what doctors like to call a game of “whack-a-mole.” We treat this clone and it disappears, and now another clone arises and we treat that, then the next clone. What if we could see all of these clones in parallel at the time that the disease was diagnosed and make treatment decisions accordingly. That’s our goal. Let’s spare this long toxicity, let’s treat multiple clones simultaneously and hopefully, what we expect to demonstrate from this is that patients will enjoy better outcomes if we’re able that type of therapeutic decision-making.

 

How is single-cell DNA used in patient stratification?

Aaron: Currently, Tapestri is not being used right now for patient stratification. I think this is where a lot of our partners would like to see the technology move and where we see it moving in the near future – that would require diagnostic grade assays. We’re certainly pursuing paths forward in that direction, but we’re not quite there yet.

So I could answer that question hypothetically in that, as Todd mentioned, the value of the clonal architecture at baseline and also as I showed in the therapeutic resistance story might inform the clinician or folks running clinical trials, for example, what types of clones may be expected to respond to a certain therapeutic strategy, or perhaps if there are clones that are detectable that suggest that this patient may not do very well with a certain therapy, because there, for example, in the FLT3 inhibitor study that I showed. With that patient’s tumor, there was a clone that did not harbor a mutant target of Gilteritinib for the FLT3 inhibition.

When you’re thinking about clinical trial designs of the future, being able to stratify based off of clonal architectures may present a big advantage in deploying these types of technologies to improve cohort enrichment and hopefully overall patient performance and understanding exactly when the right scenario is to use a certain drug or perhaps a combination of drugs in the future.

 

How is single-cell DNA used in the context of patient surveillance?

Todd: Just to reiterate what Aaron said, [Tapestri] is not currently being used for patient surveillance, but that’s where we would like to go and start to support clinical applications and decision-making for patient care. But this is yet to be established.

But what one could envision is that current methods want to focus on optimal sensitivity for a particular mutation that you discover at the time of diagnosis. If you see this mutation at some really, really low level, it could herald a reemergence or recurrence of the disease at some point in the future.

What I think the Tapestri Platform is really well geared to do is not just tell the physician, yes, that particular disease-based mutation is present, but we’re also surveying other targets that have resistance implications, and targeted therapies available. So we want to provide the oncologist, not just with a yes or no answer that something bad might happen in the future, but yes, you need to be concerned about this patient’s disease recurring and here are the potential treatments that may provide a good outcome for your patients.

It’s the actionability that I think we really want to provide to the oncologist that will help drive patient surveillance and improve patient outcomes. That’s where we’re excited to go.

 

What sensitivity can we currently expect for variant calling by SCD and ASEQ considering small variants versus CNAs?

Aaron: So sensitivity in terms of limited detection for the platform, if you’re loading a single sample, optimally, you want to load about a 100,000 cells onto the cartridge. And so from that single sample, we would expect to detect clones down to the 0.1% clonal frequency and that’s without any enrichment. As I mentioned in part of the MRD overview, we’re actually deploying enrichment for particular cellular populations.

This can be really customized to a study of any cell type that is relevant to a particular disease. With enrichment, we’ve been able to show that by zooming in on the blast population in the AML example, can give us enrichment of blast interrogation upwards somewhere between 10 to 20 fold increase in number of blasts that are actually sequenced and reported out on the platform. That’s been corresponding directly with an improvement of sensitivity in our proof of principle studies.

That enables us to get down to the one in 10,000 mark, which is at the 0.01% clonal frequency. Importantly, that is for known variance where there has been prior bulk characterization. And so we know what we’re looking for. For true de novo calls, we would still be at the 0.1 threshold.

 


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