Plenary Talk: Nanopore Conference 2021, New York, US

https://nanoporetech.com/ncm21

Panel Plenary - Pushing the limits of clinical research

From an early-career researcher, we segued into a panel plenary discussion with some of the most impressive characters using nanopore sequencing in the translational human research field. Areeba Patel, Thidathip Wongsurawat, Franz-Josef Müller, Helene Kretzmer and Luna Djirackor held a fascinating discussion on how the boundaries of using nanopore sequencing in clinical research could be extended, but first we heard their talks. All were on the topic of classifying central nervous system (CNS) tumours using nanopore sequencing technology.

Rapid-CNS²: rapid‚ comprehensive adaptive nanopore sequencing of CNS tumors — a proof-of-concept study - Areeba Patel (German Cancer Research Center (DKFZ) &
University Hospital Heidelberg)

Areeba opened by stating that CNS tumours are some of the hardest to treat. They are also some of the hardest to classify: this is evidenced by the current WHO 2021 classification incorporating mutations, copy number alterations, and target gene methylation status into its criteria. With this many considerations it can take around 22 days to classify a tumour, including time taken to even obtain the necessary number of samples. Areeba's long-term aim therefore is to develop a classification solution which is accessible and affordable, steps for which she has alrady begun with her use of the ReadFish algorithm for adaptive sampling, targeting a neuropathology gene panel and CpG sites relevant for methylation classification. She has termed this workflow Rapid-CNS2, available on Github no, and consisting of 2 parallel analysis workflows: 1) methylation calling withh Megalodon, 2) variant calling including SNV, copy number variant and structural variant calling.

The pipeline's performance on 35 glioma research samples showed higher resolution than the standard panel sequencing methods, and were comparible to the gold standard EPIC arrays. Overall, complete concordance of results from Rapid-CNS2 compared to convential methods was seen in the majority of research samples analysed.This all came with the beenfits of low capital cost, one assaycombining several results, and drastically reduced turn around times. 

Nanopore-based copy number variation approach for adult glioma classification — WHO 2021 - Thidathip Wongsurawat (Mahidol University)

Following Areeba, Thidathip introduced her research into the potential for nanopore sequencing to perform copy number variation-based calssification of adult glioma, in line with WHO 2021 criteria. In 2007 the WHO glioma classification criteria were based entirely on phenotype; in 2016 genomic data was incoroprated by the WHO for the first time; and finally in 2021 the guidelines included an extended list of genetic markers. A new marker, a CDKN2A/B deletion, has just been added but has no test for its identification currently. More concerningly FISH is commonly used for tumour classification but there is no standard method for identifying the CDKN2A/B deletion detection with FISH. Thidathip wants to develop an approach for just this, but crucially one that is simple to perform, has a quick turnaround time and, ideally, is more informative than FISH. She believe nanopore sequencing could provide this.

For her research she had access to 19 samples known to have the IDH mutations, which are included in the current ypical testing algorithm for adult glioma. By adapting SMURF-seq, and using the MinION devices followed by CNV calling, she detected 1p deletion, 19q deletion, and CDKN2A/B deletion. This answer could be obtained after even just 8 minutes of sequencing (it can take longer to drink a cup of tea!). After analysis with all 19 samples, her nanopore-based method showed 100% concordance with current testing methods, but crucially at a lower cost. Thidathip took great delight in pointing out also that, thanks to its use of MinION, almost any lab world-wide can access this (and they also got the same results with Flongle). In a hypothetical work day, her process could start at 9 am and you could have your CNV profile by 1pm - even including an hour for lunch.

Real-time cancer classification with nanopore sequencing - Franz-Josef Müller (University Hospital Schleswig-Holstein) & Helene Kretzmer (Max Planck Institute for Molecular Genetics)

Doubling the numbers of our speakers in one swoop, next we heard from Franz-Josef and Helene on their work for real-time cancer classification. In fact rather than just heard from, it was saw from, as the first half of their shared talk focused on an engaging video demonstration of their lab workflow for brain tumour classification based on a sample's methylation profile.

Franz-Josef spoke first, introducing their setup which had been optimised for the fastest possible classificaton of brain tumour types: they prepare libraries with the rapid barcoding kit before sequencing on the MinION Mk1B and performing analysis with Megalodon. He then moved smoothly into their 'Clinical Demonstrator' video, so called as at the core of what they are doing is the desire to transition their research on epigenetic features of cancer subtypes into the clinic: biopsies fresh from the theatre were bought to the sequencing lab, prepared for sequencing, and then all analysis was carried out locally. 44 minutes after biopsy processing started, they see their first predicction on classification type come in.

Helene then took over the explain that their CpGs are filtered within their pipeline to those covered by commercially available methylation arrays. In real0time, they see ~500 CpG sites sequenced within the first few minutes of a MinION run, rising to 2,000-5,000n within the first 30 minutes. At 4-5 hours, no new CpGs are sequenced. Building on research by their peers, their training set for classification consists of >2,800 samples and covers over 90 different tumour classes - very comprehensive. 

Helene concluded by sharing some example results. stating that they see a stable classification of brain tumour class after only 1 hour of sequencing, and showing the read out from their pipeline includes a list of tumour classes, as well as the classificaiton probabilities.

Discussion - Joined by Luna Djirackor (Oslo University Hospital)

After three thoroughly impressive talks demonstrating the innovative research this group are carrying out, they were joined by an incredibly worthy peer in the form of Luna Djirackor, who at London Calling 2021 presented her work on the potential of intraoperative (yes, whilst they are on the operating table!) nanopore-based methylation classsificaiton of brain tumours.

A question first on library prep kits and workflows, Areeba noted that one of the reasons adaptive sampling had been so attractive to her was, well - there's no different sample preparation methods required for targeting sequences by this method. The amount of data they required - their coverage - was up next, with different answers for different applications. Areeba had sufficient data for variant calling at 10x, but could conduct methylation profiling with just one read. One read. Thidathip required less than 1x depth of covrage to obtain the correct result, while Luna stated that to get results back during surgery they needed ~ 3,500 CpG sites called. Helene determins CpG methylation as a binary result, and single sites were rarely covered more than once during their sequencing run but this wa sufficient for classification.

Moving onto data analysis and infrastructure, for Franzef and Helene everything was carried out locally. Areeba used a cluster and a local workstation, with the main compute requirement for adaptive sampling being a GPU. Thidathip turned to a bioinformatician for analysis, but hope to use real-time analysis in the future. Luna echoed Franzef and Helene, with a local setup including a GPU-laptop, a MinIT, and a MinION.

Luna, Franzef and Thidathip all used a MinION in their research, but saw the benefits in GridION for allowing multioke on-demand experiments to take place, and more resource locally. Areeba agreed that the flexibility of the GridION was a key for it. Further savings in time and cost could be achieved with multiplexing on a single flow cell, or washing a flow cell and re-using for later libraries.

The last topic of discussion was whether any training would potentially be needed for the real-world implementation their workflows. Franzef emphasised the need for good and reliable ways to communicate certainty, as well as uncertainty, of the results. Areeba explained how the report outputted from her team’s workflow is as comprehensive as possible, detailing not just what tumour class has been detected, but also what might those results imply, and the level of certainty.

Credit: https://community.nanoporetech.com/posts/panel-plenary-pushing-the

In the NCM21 panel plenary on ‘Pushing the limits of clinical research’, four speakers presented their cutting-edge research into classifying central nervous system tumours using nanopore sequencing technology: Areeba Patel, a doctoral researcher at the German Cancer Research Center (DKFZ) and University Hospital Heidelberg; Thidathip Wongsurawat, a group leader in Faculty of Medicine at Siriraj Hospital, Mahidol University; Franz-Josef Müller, who works clinically as a neuropsychiatrist at the University Hospital Schleswig-Holstein in Germany; and Helene Kretzmer, who heads the bioinformatics group at the Department for Genome Regulation at the Max Planck Institute for Molecular Genetics in Berlin, Germany.

For the panel discussion, they were joined by Luna Djirackor, a postdoctoral fellow at the Oslo University Hospital, Norway. Luna had presented her research on the potential of intraoperative nanopore-based methylation classification of brain tumours at London Calling 2021, and returned here to contribute her expertise and insight in this area. Luna’s team have also recently published this work in Neuro-Oncology Advances: https://doi.org/10.1093/noajnl/vdab149.

Areeba Patel — Rapid-CNS2: rapid comprehensive adaptive nanopore sequencing of central nervous system tumours

Areeba Patel opened the panel plenary session by introducing how central nervous system (CNS) tumours are some of the most difficult tumours to treat, with extensive morphological and molecular heterogeneity; this is evidenced by the fact that current WHO 2021 classification incorporates mutations, copy number alterations, and target gene methylation status into its criteria. When it comes to molecular classification of a tumour, and in particular, methylation classification, this process can take around 22 days; a major limiting factor is the number of days required to obtain sufficient samples. Areeba also mentioned that conventional sequencing-based disease detection is associated with high costs. Areeba’s long-term aim is to develop a potential solution that is ‘accessible and affordable’.

To this end, Areeba is investigating the potential of adaptive sampling, based on the ReadFish algorithm (Payne et al. Nat. Biotech. 2020) for real-time read enrichment on the MinION device. The workflow, termed Rapid-CNS2, involves preparing a sample using the Ligation Sequencing Kit, sequencing one sample per MinION Flow Cell over a period of 72 hours, and performing real-time enrichment with ReadFish during the run — targeting reads based on a neuropathology gene panel and CpG sites relevant for methylation classification, flanked by 10 kbp of sequence. Areeba showed a visualisation of successful target enrichment over a region of interest.

The bioinformatics workflow for Rapid-CNS2 is a fully automated pipeline, available on GitHub, involving two parallel analysis workflows using the FAST5 files: methylation calling (with Megalodon), including determination of MGMT gene promoter methylation status; and variant calling, including SNV, copy number variation, and structural variation calling, of variants known to be associated with diffuse glioma (e.g. IDH1/2 mutation status and EGFR amplification). The pipeline typically takes 24 hours, and the output is a PDF report.

To investigate the performance of the Rapid-CNS2 workflow, Areeba’s team tested it on 35 diffuse glioma clinical research samples, which had matching sequencing and array data. Compared to the standard method of panel sequencing, Rapid-CNS2 showed ‘much higher resolution’, and the profiles were also comparable to the “gold standard” EPIC arrays. Overall, complete concordance to conventional methods was seen in the majority of the clinical research samples analysed. Areeba concluded by highlighting the advantages of their approach, which are that it has low capital costs, and ‘combines mutational, methylation, and copy number analysis in one assay’. As it allows single sample processing, turnaround times are ‘drastically reduced’, meaning it is potentially ‘efficient in low-throughput settings’. Moreover, the adaptive sampling method is highly flexible, and target selection is ‘as simple as altering a BED file, with no additional library preparation’. The team are now setting this method up on the GridION and looking to further decrease the sequencing and analysis time.

Read Areeba’s recent medRxiv pre-print on Rapid-CNS2 here: https://doi.org/10.1101/2021.08.09.21261784

Thidathip Wongsurawat — Nanopore-based copy number variation approach for adult glioma classification — WHO 2021

The next speaker in the panel, Thidathip Wongsurawat introduced how she is researching the potential of nanopore sequencing to perform copy number variation-based classification of adult glioma, in line with WHO 2021 criteria. Thidathip explained that glioma is a type of brain tumour that develops from glial cells; it has high mortality and is most common in those aged 45–65. In 2007, the WHO glioma classification criteria were based entirely on phenotypic data; in 2016, ‘for the first time’, the WHO incorporated genotypic data alongside phenotypic data in glioma classification; and finally, in the updated 2021 criteria, the guidelines included an extended list of genetic markers. Thidathip highlighted a new marker, CDKN2A/B deletion, which, she had been informed, currently has no test for its identification; this deficiency isn’t just found within Thailand, it is a worldwide problem.

Many labs currently perform FISH for tumour classification. Problems with this method include that the pathologists need time to interpret FISH results, and that there is no standard method for CDKN2A/B deletion detection using FISH. Thidathip’s research goal is to develop an approach for identifying CDKN2A/B deletion that is simple to perform, has a quick turnaround time, and, ideally, can produce more information than that provided by FISH; for example, detection of both whole-arm or partial loss of the chromosome. Thidathip hypothesised that nanopore technology could have the potential to achieve these goals, and so to investigate this, Thidathip is comparing results from such a nanopore-based approach against parallel results obtained from currently used testing methods, such as FISH, EPIC arrays, and short-read whole-genome sequencing.

For this research, Thidathip was supplied with 19 samples which were known to have IDH mutations (mutations in IDH1/2 are included in the current typical testing algorithm for adult glioma). Thidathip adapted the ‘SMURF-seq’ protocol (Rishvanth K. Prabakar et al. Genome Biol. 2019), using the RAD004 or RBK004 rapid-chemistry-based kits for library preparation, and the MinION Mk1B and Mk1C devices for sequencing, followed by CNV calling. She shared results showing ‘the power’ of this adapted technique, with detection of 1p deletion, 19q deletion, and CDKN2A/B deletion. Furthermore, ‘even eight minutes after nanopore sequencing, we got the answer’ with this method. This would save both time and money compared to currently used methods (e.g. FISH). There was also high correlation between the CNV results using Thidathip’s method and those from short-read sequencing.

After analysis of all 19 samples, the nanopore-based method showed 100% concordance with current testing methods, and at a lower cost. And ‘the important thing is, because we use MinION… almost any lab worldwide’ can access it. Thidathip also noted some ‘good news’: that they got the same results using the Flongle device. Lastly, she presented a hypothetical working day incorporating this CNV workflow, explaining how, if you started at 9 am, the CNV profile would be reported by 1 pm, even including a 1-hour lunch break!

View Thidathip’s peer-reviewed publication on the simultaneous detection of IDH1/2 mutations and MGMT methylation in glioma research biopsy specimens, using Cas9 targeted sequencing: https://doi.org/10.1186/s40478-020-00963-0

Franz-Josef Müller & Helene Kretzmer — Real-time cancer classification with nanopore sequencing

Franz-Josef Müller and Helene Kretzmer presented the potential of ‘real-time cancer classification’ through an engaging video demo of their lab and analysis workflows for brain tumour classification prediction based on a sample’s methylation profile.

Franz-Josef introduced the sequencing set-up of the research workflow, which is optimised ‘for the fastest possible classification of brain tumour types’. Libraries are prepared using the Rapid Sequencing Kit (SQK-RAD004), these are then sequenced on the MinION Mk1B, and Megalodon is used for combined basecalling, alignment, and methylation calling. A set of custom scripts are also applied for tumour classification.

Franz-Josef moved on to the ‘Clinical Demonstrator’ video, called as such because ‘we deeply care about the clinical translation of our basic research and epigenetic features of cancer subtypes’. This began with fresh biopsies taken directly from the operating theatre; these clinical research samples are brought into their sequencing lab for processing via the aforementioned workflow. Franz-Josef suggested that by bringing nanopore sequencing directly to the operating theatre, intraoperative classification could potentially be ‘within reach’. The bioinformatics pipeline is all performed locally; ‘no cloud computers were harmed for this video’. Only 44 minutes after biopsy processing started ‘the first predictions are going to come in’. Franz-Josef stated that these predictions continue to update as more sequence data is generated during the run.

Helene explained that CpGs are filtered within their pipeline to those covered by a commercially available methylation array; in real time, around 500 CpGs are sequenced within the first few minutes of a MinION run, and 2,000–5,000 CpGs within the first 30 minutes. After around 4–5 hours, no new CpGs are sequenced. The training set that was used as the basis of their model for brain tumour classification prediction was derived from Capper et al. (Nature, 2018); this dataset consists of >2,800 samples and covers over 90 different tumour classes.

Helene concluded the presentation by sharing example results, stating that the classification of brain tumour class was stable after only 1 hour of sequencing, and showing how the read out from the pipeline includes a list of tumour classes and their classification probabilities.

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