Detecting Rejection Earlier: Customer Experience with One Lambda Devyser Accept cfDNA and VH Bio’s Support

How a transplant surgeon and PhD researcher used donor-derived cell-free DNA testing to explore personalised transplant monitoring and improve rejection surveillance   

Interviewee: Mr George Nita
Role: Clinical Research Fellow in Transplantation Surgery, Royal Liverpool University Hospital; PhD Research Fellow in Bioinformatics, University of Liverpool 

The Brief 

Mr George Nita is a Senior Registrar in General Surgery who took time out of clinical training to complete a PhD focused on donor-derived cell-free DNA (dd-cfDNA), machine learning, and risk prediction in transplantation. 

His research aims to address one of the biggest challenges facing transplant medicine: identifying patients at risk of rejection before irreversible damage occurs. 

Current monitoring approaches vary significantly between transplant centres, with no universally accepted strategy for monitoring donor-specific antibodies (DSAs) or dd-cfDNA. George believes a more personalised approach is needed. 

Rather than monitoring all transplant recipients in the same way, his research seeks to develop a risk-stratification model that categorises patients according to their likelihood of rejection. This could enable intensive monitoring for high-risk patients while reducing unnecessary procedures for lower-risk individuals. 

Ultimately, his goal is to prevent avoidable graft loss and reduce the number of patients returning to transplant waiting lists. 

The Challenge  

Transplantation currently faces a growing demand crisis. As waiting lists continue to expand and more patients require repeat transplantation, preserving existing graft function becomes increasingly important. 

Traditional monitoring methods often identify rejection only after significant injury has already occurred. 

“By the time we’ve done DSAs and the histology result comes back, the kidney is already damaged. What we want is to detect problems before they happen.” 

George sees dd-cfDNA as a tool that can identify graft injury earlier, allowing clinicians to intervene before rejection causes irreversible harm. 

When evaluating available dd-cfDNA solutions, George focused on a cohort of high-risk transplant recipients, including: 

• Re-transplant patients 

• Highly-sensitised recipients 

• Patients with multiple transplanted organs 

• Patients receiving lymphocyte-depleting induction therapies 

For these complex cases, precise donor identification was essential. 

The deciding factor was the unique ability of One Lambda Devyser Accept cfDNA to distinguish between DNA originating from different donors. 

“Accept cfDNA is the only validated method that can tell apart the donors. I want to know that I’m monitoring the organ of interest.” 

This level of specificity was particularly important in patients with multiple previous transplants, where conventional approaches may not provide sufficient discrimination. 

The Research Outcome 

Initial Evaluation 

The project began as part of George’s PhD research programme investigating novel biomarkers for rejection monitoring. 

Working alongside the VH Bio Transplant Team, he evaluated whether the assay could be successfully implemented within a laboratory environment – despite having limited prior experience with next-generation sequencing (NGS). 

Training and Method Development 

Before beginning the project, VH Bio and Thermo Fisher Scientific provided hands-on training and implementation support. 

“You guys came and trained me and it just went so smoothly. The first batch we put on produced fabulous quality scores. It was a clean run.” 

Validation and Workflow Optimisation 

Following successful initial runs, the team evaluated sample collection and processing strategies. 

One key finding was the importance of selecting the appropriate blood collection tube based on workflow requirements. 

While EDTA tubes performed well when samples could be processed within four hours, cell-free DNA stabilisation tubes provided greater flexibility for remote collection and batch processing. 

George ultimately adopted Nonacus stabilisation tubes for later stages of the project due to improved sample stability and logistical advantages. 

He commented that laboratories already familiar with molecular techniques should have little difficulty implementing the assay. 

Software and User Experience 

The Devyser software received particularly positive feedback, with George highlighting the intuitive interface and automated analysis workflow. 

“The software does it all for you. You just click a few buttons and get these lovely graphical representations. It’s excellent.” 

The straightforward reporting system helped simplify interpretation and reduced barriers for users new to NGS-based testing. 

Technical Support  

Throughout implementation and ongoing use, George describes the support from VH Bio as ‘fantastic.’ 

In his interview, he said that training was comprehensive, questions were answered promptly, and practical advice was always available when required. 

“I got really good training in NGS. Any questions I had, little hints and tips that come with experience, the support was always there. I can’t fault it.” 

This support was particularly valuable given George’s transition from a predominantly surgical background into advanced molecular diagnostics. 

Key Recommendations for Other Laboratories 

Based on his experience, George recommends that laboratories:  

Consider Sample Collection Strategy Early 

• Use EDTA tubes when plasma can be processed within four hours. 

• Consider validated cfDNA stabilisation tubes for centralised testing or delayed processing. 

Implement Appropriate Quality Control 

Although not always essential long-term, George recommends using instruments such as a TapeStation during initial validation phases to establish confidence in extraction quality and workflow performance. 

Don’t Be Put Off by NGS 

“If I was able to do this, they will be able to do this.” 

George believes the workflow is accessible even to laboratories with limited previous NGS experience. 

Looking Ahead: The Future of dd-cfDNA 

George believes dd-cfDNA has the potential to transform transplant monitoring by enabling: 

• Earlier detection of rejection 

• Better risk stratification 

• Reduced reliance on unnecessary biopsies 

• More personalised immunosuppression management 

• Improved long-term graft survival 

Rather than replacing biopsy, he sees dd-cfDNA as a powerful triage tool: 

“Why would I do a biopsy if I don’t have to, if the test is negative?” 

A positive result can help identify patients who require further investigation, while a negative result may spare patients from invasive procedures. 

For George, dd-cfDNA represents more than just another biomarker. 

It is a practical tool that can help clinicians intervene earlier, make better-informed decisions, and ultimately improve outcomes for transplant recipients. 

“The INDEL-based NGS assay [One Lambda Devyser Accept cfDNA] is a promising novel tool for detecting and monitoring dd-cfDNA in renal transplant recipients with an easy-to-implement workflow.” 

Through ongoing research and collaboration, George hopes to contribute to the development of evidence-based monitoring strategies that allow transplant centres to deliver more personalised and effective patient care. 

Read the pilot study, published in the Clinical Transplantation and Research Journal: A novel INDEL-based next-generation sequencing assay for monitoring donor-derived cell-free DNA in renal transplant recipients— from bedside to results: a UK pilot study 

Get in touch with the VH Bio Transplant Team to discuss how the One Lambda Devyser Accept cfDNA assay can be implemented into your laboratory workflow or with any questions about dd-cfDNA.  

Learn more:  

Read VH Bio’s Interview with George 

Can you introduce yourself – what your clinical role is, what you’re studying towards and what you have been working on?  

My name is George, I’m a Senior Registrar in General Surgery and I took 3 years out of training to do a PhD. At present, I’m a PhD Research Fellow in Bioinformatics at the University of Liverpool University, and I work as a Clinical Research Fellow in Transplantation Surgery for Liverpool Hospital.   

My PhD focusses on a couple of things: donor-derived cell-free DNA (dd-cfDNA) as a novel biomarker – although we erroneously call it “novel,” it’s been around for some time – and some things regarding machine learning and artificial intelligence for risk prediction and rejection in transplantation.   

As we all know, the allocation scheme changed in 2019, which to some extent disadvantages older people. We want to see what the differential outcomes are based on age as we think there is not enough equity in the elderly population undergoing transplantation.   

How are you hoping DSAs and dd-cfDNA might inform that?  

First things first, we need to reach some consensus regarding standardisation; it’s one of the biggest problems in transplantation. It just reflects the real world: in the UK, there are 24 transplant units that provide kidney transplants, and each individual unit and H&I lab deals with things differently. At the moment, there is no standardised way that we monitor new patients with donor-specific antibodies (DSAs). dd-cfDNA is just emerging in the UK and we all have different cut-offs; it’s very hard to define how you’re going to treat and monitor patients.   

What I’m hoping is that, by analysing our database, it is going to give us an idea of how to risk-stratify patients. Because obviously if someone is having their first transplant, they won’t have the same risk of alloimmune pathology as someone who has had three transplants, ten blood transfusions, and started with a cRF/PRA of 98%. At the moment, we monitor all patients the same way, with an umbrella approach which is haphazardly thrown on everyone who comes through the transplant unit. It doesn’t seem right. I think we all fall under different categories of risk. So that’s what I’m trying to answer – is there a way to determine who is high risk? – and come up with a red/amber/green system. My train of thought is that some people would benefit from early, aggressive monitoring post-transplant with dd-cfDNA; if that’s positive, go forward with a biopsy to get the histological diagnosis, which is still the ‘gold standard’. For somebody who’s in the ‘green’ category – say, first transplant with a cRF of 0% and a well-matched organ, there would be a ‘reassurogram’ dd-cfDNA if you like, perhaps once a year, would be sufficient. That would eliminate the need for protocol biopsies – we don’t do protocol biopsies, but some centres do – and that would be a reassuring test for both yourself and the patient. Some units discharge the patient at one year and this would be a nice way of saying, “Well, listen, I know your graft is functioning properly.” Then there will be those in the middle where we won’t know how exactly to monitor but hopefully the research I’m doing will answer some things. We need a standard risk stratification because we haven’t yet decided how we’re going to use this test.   

You’re close to completing your PhD, which will make you a surgeon-scientist. What is the problem you are trying to address with Accept cfDNA?  

The problem is: I want to prevent patients from returning onto the waiting list. The projection is completely dire, and I keep bringing up this Kidney Research UK report published in 2023 and the findings are shocking. The unconstrained projection of people who will need a transplant will most likely quadruple in the next decade. If you think about the sheer numbers, we’re talking about 10,000 patients; as a UK consortium we’re performing about 3,000 transplants a year; in Liverpool we do around 100-120 kidney transplants a year. If you think about the number of patients needing, essentially, a life-saving procedure, quadrupling, and then you have more people returning onto the waiting list, now much more heavily sensitised; you’re basically shutting the door on them.   

So how do we avoid that? We detect problems before they happen. What we’re doing at the moment is like walking into a murder scene as a detective and looking at the blood, the knife and the victim and wondering, “how did we end up here?” By the time we’ve done DSAs, by the time the histology result comes back, the kidney – pardon my expression- is already “toasted.” The antibodies have done some damage, we don’t know if we’ll be able to recover the kidney or salvage it, if the function will return to some predicted baseline function. The likelihood is it’s going to get much worse and what you don’t want to do is transplant someone, sensitise them, and basically render them ‘untransplantable’ in the future, adding them to an ever-expanding waiting list. That’s what I’m hoping to do: to come up with a better personalised way of monitoring patients, detecting problems before they appear, and intervening earlier with treatment, so we prevent this disaster from happening, or at least slow it down.   

You were the first in the UK to use Accept cfDNA after it was launched. What was your decision behind using this assay over the alternatives?   

It’s very clear. Because we didn’t have a large cohort of patients, we focussed on people at high risk of rejection. In our unit, patients who have a cRF >20%, re-transplanted, level IV HLA MM, all have lymphocyte-depleting therapies (Aletuzemab) because we know they’re at higher risk or rejection, so I wanted to focus on that population. Now, the problem is, within that population, you have a high number of patients who are re-transplanted, even with multiple organs: liver, kidney, kidneys from different donors, and so on. Accept cfDNA is the only validated method that can tell apart the donors. What I want to know is that I’m monitoring the organ of interest. I’m not interested in the kidney that was rejected 5 years ago. This assay involves a screening of the donor DNA; you can be extremely precise. And if you specifically want a diagnostic tool, you’re going to have to be precise; you can’t afford to say, “maybe it’s rejection,” then you haven’t achieved anything.   

How did you find the assay and the software?  

Look, I’m going to be very honest. I’m a surgeon, I’ve never done NGS (next-generation sequencing) before. When I started in the lab about 2.5 years ago, I did lots of DSAs and maybe some basic genomic DNA extractions. I don’t think I’d seen any NGS before my first NGS run. You guys, with Kevin [Gerritsen] from Thermo Fisher, came and trained me and I’m ever so grateful for your help and your support. It just went so smoothly, I couldn’t believe it. The first batch that we put on, we got fabulous >Q30% scores; it was a clean, clean run. The second and third time, it became easier and easier. If you master the techniques, pretty much be the ‘bread-and-butter’ for many scientists in H&I labs now, unlike myself, I put kidneys into people, it doesn’t get easier than that, like hooking up a washing machine. It becomes easier, so you’ll need less hands-on time.   

The software in particular, does it all for you. You don’t need to do anything. You just click a few buttons and you get all these nice, lovely graphical representations: very simple, minimalistic. It’s excellent, I didn’t have any issues with it at all. It’s pretty much self-explanatory.  

How was the technical support that was provided?  

It was fantastic. I had a really good experience, I got really good training in NGS. Following that, any questions I had, little hints and tips that you can’t always protocolise but come with experience, it was always there, within reach. I can’t fault it really. I’m extremely happy.   

So, if a lab was wanting to do this but they’d never performed NGS techniques, you think this would be straightforward for them to do?  

100%. If I was able to do this, they will be able to do this. I know there are methods that are much more complicated than this. The only problem with implementing this assay is cost, but then again, how much do you value human life? It becomes relative. If cost isn’t a barrier, everyone should do it.   

Do you have any recommendations for others considering doing this type of testing?  

I was quite happy with EDTA samples. The problem with that is that you have to be really strict and religious about your four-hour window.  I’ll give you a specific example: in the first 6 months, people would turn up to clinic. I would know exactly when they were coming in, I would process the samples myself within 4 hours of being collected. Once you’ve done that, separated the plasma, you can freeze them and they’ll be fine. If you haven’t done that and you breach that four-hour window, of course there’s a risk of contamination with cellular DNA and then you may get false results. If you’re thinking about centralised testing, or you aren’t sure if samples are going to be processed as soon as they arrive, I would advise to opt for cell-free DNA preservative tubes. There’s a number that are validated with this assay: STRECK, PAXgene and Nonacus. The reason I switched to Nonacus is because in the last six months, patients turn up more haphazardly, see phlebotomists on a Friday and I won’t see that sample until Monday. We’ve breached that four-hour window so what do I do in that scenario? With the Nonacus tubes, the quality and purity were much better and these are samples that were sent by post. You get a window of 7-14 days where the DNA is stable, so you can batch-process. That would be one of my recommendations.   

The assay recommends a TapeStation, do you think this is necessary?  

Well, it depends on your risk appetite, I think. In the first instance, I would definitely recommend it. We didn’t have a TapeStation so what we had to do was rely on our networking and our friends. I picked up the phone and called a previous mentor who works in Oncological Surgery, they knew someone at the University who had looked at cell-free DNA for oncology, who had a TapeStation. It depends a lot on what method you’re using for cfDNA, whether you’re using EDTA or cfDNA preservative tubes. But I think, in the first instance, definitely have it, so you at least set a baseline of what your quality, purity and concentrations are like before  you do your first batch. If you do another cfDNA extraction run and you’re happy with your methodology, you probably don’t have to continue do it. It depends on your risk appetite. If you want to go blindly and hope for the best – there will be a category of people who like that sort of stuff – I would like to make sure that these samples, for research especially, are not recoverable. We can’t afford to re-bleed them and ask patients to come into clinic just because we didn’t check basic quality control.  

What has been one of the most challenging aspects of your PhD and how have you overcome it?  

I always say, doing a higher degree in the middle of your surgical training is some bonkers military academy on steroids or something. You’re expected to output the equivalent of a full-time PhD research fellow, when most of the time, you’re still doing this part-time, and most of the time, you’re still doing clinics, you’re still doing on-calls. It’s hard to finish a 24-hour shift in transplant, then go into the labs and perform a batch of DSAs. That would be one, and the other would be: you have to develop some resilience. Technical failures, like DNA extraction machines failing mid-way while extracting these unrecoverable samples, you have to find a way to be resilient. From someone who is coming from a surgical background, where things are more often black and white and you have to make a decision and you’re used to instant gratification of seeing your work immediately, to wait weeks to know your cfDNA was just 10% in that sample is so frustrating but you learn a lot. Not only lab techniques, but broader clinical and life skills.   

ESOT’s consensus recommendations are for serial plasma monitoring even in patients who seem well. What would your advice be to those who are wondering whether dd-cfDNA should be requested for-cause only?  

Again, it depends on your appetite for risk.  Everything in life carries a risk, even if you’re crossing the road. I would say there are patients where you can afford to do that – people at very low risk of rejection. That’s why you need your risk stratification. If somebody is straightforward, creatinine has always been fine, no symptoms indicating a problem, first transplant, cRF 0%, perfect match from a living donor, I would agree. But if you have a cohort of patients who would benefit from more aggressive, minimally invasive monitoring, I wouldn’t agree with intermediate-high risk being monitored for-cause, I don’t think that’s the right approach. This test can tell you months ahead if something is cooking and that’s when you want to know what you’re going to do.   

What about in a subclinical case, low risk at transplant but perhaps a single mismatch, or they are developing injury, but creatinine and symptoms are fine?   

Presumably they’d have DSA monitoring.   

Not all labs perform DSA monitoring post-transplant, many tend to monitor for-cause.    

Again, that’s why you need to come together with other centres to run some machine learning to determine this risk prediction and tailor monitoring. It might not be that you do it yearly in a low-risk patient, but every couple of months as a better way of catching things. For someone higher risk, it may be that you do it in the first month and if that’s positive, then more often from there. People ask me why are you doing dd-cfDNA if you’re going to do a biopsy anyway? Well, you’re asking the wrong question, you’re not looking through the lens of someone who’s at the end of the needle. We know biopsy is an invasive test. If you look from the lens of developing general complications to more serious ones, like risk to life or the graft or both; when it happens to you the risk isn’t 5%, it’s 100%. So, why would I bother doing dd-cfDNA if I’m going to do a biopsy anyway? It should be: Why would I do a biopsy if I don’t have to, if the test is negative?  

Rule out a biopsy if you have a negative result, but if the patient seems well and you have a positive result…  

Rule in for biopsy, so you know who needs it before disaster happens and the creatinine is 300.   

As a clinician, how would routine monitoring help you manage your transplanted patients?   

A couple of applications: One being treatment – knowing in advance it is happening, you may bring a biopsy sooner. There are some cases, e.g. some people come over the weekend or late Friday, tacrolimus normal, urine dip is fine, they had an ultrasound earlier in the week and perfusion was fine, there’s no anatomical problem, but the creatinine has jumped to 300. What do you do in that instance? Well, not much you can do, because the biopsy can’t be ordered until Monday. Start blindly treating with steroids in case it’s cell-mediated rejection? What if you had a blood test that could identify if it’s rejection or not and you have the results the next day, sparing you from giving people steroids.  

Another area that needs a bit more research: immunosuppression optimisation. We all know what the risks associated with immunosuppression are. If you could halve immunosuppression in those seeming to do fine with quiescent grafts -particularly the paediatric population – I know there’s a lot of interest and a few different studies… there are all sorts of things. I think this biomarker isn’t going to sort out all the problems, which is what everyone’s expecting. It’s just another tool to help you make better decisions, individually for every patient, according to a risk category, which is yet to be determined because we’re still at the very beginning. We don’t know what happens with baseline levels over long periods of time. I’ve been asked if this biomarker is going to end up in a “graveyard of biomarkers” like all the others and I don’t think so. I think it will be implemented and we’ll all be using it clinically; we just need to figure out what is the best approach because there are a number of applications. We can screen patients, we can diagnose them, we can use it for all sorts of things. At the moment, we’re just starting to understand how things should be. The same thing happened when DSA monitoring was implemented and to this day, we can’t agree on a common cut-off for what’s positive and what’s negative. We’ll get there, but there is certainly a world of opportunities in front of us.   

Thank you for your time, it’s been a pleasure to work with you and support your research. You’ve left a lasting impression as a clinician who cares for his patients, and we wish you every success.   

Disclosure: This review reflects the independent clinical experience of the authoring laboratory.