Evaluating targeted long-read sequencing as a diagnostic tool for burkitt lymphoma
Notice bibliographique
Résumé
Abstract Introduction: While sporadic in developed countries, Burkitt lymphoma (BL) is endemic in resource-limited regions. Given the one-year survival rates of 61% (Mezger, The Oncologist, 2023), BL patients in sub-Saharan Africa could benefit from improved diagnostic methods, particularly those that facilitate the evaluation of new targeted agents. Recent work from our group has found that BL can now be divided into molecular subgroups based on mutations (Thomas, Blood, 2023) and into epitypes using DNA methylation patterns (Thomas, BCD, 2025). Nanopore long-read sequencing is becoming a robust and efficient approach to rapidly profile patient DNA samples for structural variants (SVs), simple somatic mutations (SSMs) and base modifications. Using the adaptive sampling (AS) technique, sequencing can be focused on regions of interest (ROIs). This technique has shown promise in application to germline genetics (Vermuelen, Nature, 2023) but its potential application to somatic conditions remains largely unexplored. We are evaluating the utility of this approach to obtain genetic and epigenetic data from relevant genomic loci to facilitate differential diagnosis and accurate assignment of BL molecular subgroup and epitype. We aim to create a diagnostic assay using AS coupled with an automated bioinformatics pipeline that classifies and subtypes BL to inform treatment and improve outcomes in resource-limited regions. Methods: To establish ROIs, we selected driver mutations and features identified as relevant to differentiating BL from other B-cell lymphomas (Coyle, Blood Adv., 2025) as well as distinguishing epitype (Thomas, BCD, 2025) and molecular subgroup (Thomas, Blood, 2023). These include 179 lymphoma genes, 3 oncogenic SVs, 127 regions commonly affected by aberrant somatic hypermutation, the EBV genome, the IGH, IGK and IGL loci, collectively 31.5 megabases. Initially, we simulated AS data for 9 BL genomes by extracting reads corresponding to ROIs with samtools from whole genome sequencing data (PromethION). To generate real AS data, we prepared 32 µL of DNA library using the Raji BL cell line. Library preparation with 1.6 µg of gDNA was performed according to the Native Barcoding Kit 24 V14 (SQK-NBD114.24) protocol. The library was sequenced on a single PromethION flow cell (R10.4.1 chemistry) for 25 hours in enrichment mode using our ROI panel. Enrichment, coverage and relevant AS metrics were assessed using samtools, bedtools, Mosdepth, custom R scripts and MinKNOW output files. To evaluate epitype classification, CpG methylation status was inferred from simulated AS data with Modkit, analyzed with custom R scripts and compared to ground truth labels. To explore 5hmC modification and hypomethylation patterns, TET2 expression was determined from RNAseq data (n=126) and analyzed using R scripts. To examine mutation differences between epitypes, SLMS-3 (Thomas, Blood, 2023) variant calls within ROIs from Illumina data for the same 9 BL genomes were used; these were normalized and compared using custom R scripts. Results: The top 6,000 5mC positions with the highest variance were able to accurately recapitulate patient epitypes. These positions had an average CpG methylation level of 49% in HyperBL and 36% in HypoBL. HyperBL samples showed more mutations than HypoBL, consistent with the ground truth. TET2 expression was significantly greater in HypoBL, suggesting more demethylation and higher transient 5hmC modification during demethylation. Regarding AS metrics, 4.7% of fragments present were basecalled. A total of 1.7 million fragments were sequenced and 23.2% overlapped a target ROI. Mean read lengths for on and off-target fragments were 7,065 and 690 bases, respectively, and the range of quality scores for these reads was comparable. Average coverage across ROIs was 67.9, with an enrichment factor of 79.3. Conclusions: Preliminary analysis of real and simulated BL AS data suggests that a limited feature set can support successful subgroup classification concordant with Illumina ground truths. Evaluation of ONT-focused tools to resolve SSMs and SVs is ongoing. Our preliminary results suggest AS has the potential to serve as a portable, high-throughput diagnostic assay to inform treatment and improve outcomes for endemic BL patients in resource-limited regions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».