MétaCan
Menu
← Retour à la cohorte
Enregistrement W7108466202 · doi:10.1182/blood-2025-495

Ultra-deep whole-genome sequencing of cell-free DNA recovers most bone marrow-derived mutations in newly diagnosed multiple myeloma

2025· article· en· W7108466202 sur OpenAlexaff

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensOntario Institute for Cancer ResearchAlberta Cancer FoundationUniversity of AlbertaQueen Elizabeth II Health Sciences CentreUniversity of TorontoUniversity of New BrunswickDalhousie UniversityVancouver General HospitalSaint John Regional HospitalPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaGermlineDeep sequencingDNA sequencingSomatic cellCopy number analysisWhole genome sequencingStructural variationBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Cell-free DNA whole-genome sequencing (cfWGS) offers a non-invasive alternative to molecular profiling of multiple myeloma (MM), potentially reducing reliance on invasive bone marrow (BM) biopsies. Standard-depth (~30–40×) cfWGS reliably estimates tumor fraction and detects broad copy number alterations, but often fails to capture somatic single-nucleotide variants (SNVs) present at low variant allele frequencies (VAFs) due to tumor subclonality or low tumor shedding. Recent studies in solid tumors suggest that pushing cfWGS to ultra-deep coverage (≥100×) can rescue low-frequency somatic mutations, yet this has not been explored in MM. With sequencing costs declining, ultra-deep cfWGS has potential as a comprehensive tool for MM genomic profiling directly from peripheral blood (PB) cfDNA. To determine whether ultra-deep cfWGS can match the mutational yield of BM WGS and capture actionable variants, we compared standard-depth (40×) Illumina cfWGS with 150× cfWGS (targeted 200×) generated using the Ultima Genomics platform. Methods Paired BM and PB cfDNA samples with matched buffy coat germline controls were collected at diagnosis from 11 transplant-eligible patients enrolled in the Multiple Myeloma Molecular Monitoring (M4) study (n=8) and IMMAGINE study (n=3). DNA from BM CD138+ selected cells underwent 30-80× WGS on Illumina NovaSeq 6000 with somatic variants called versus a matched normal using MuTect2. Matched cfDNA libraries were prepared from a median of 20ng (range 20-83ng) of cfDNA extracted from 10-20mL of plasma for Illumina, and from 50ng (range 22-150ng) for Ultima. Libraries were sequenced to 40× on Illumina (aligned with BWA-MEM/GATK; variants force-called using MRDetect) and 150× (targeted 200×) on Ultima Solaris (prepared with PPM-Seq; variants force-called via Ultima's ‘Single Read SNV’ pipeline). Tumor fractions were estimated using ichorCNA. Results At diagnosis (median age 57, range 41–75 years; 6 male, 5 female), 4 patients were high risk, 2 standard, and 5 unknown. Subtypes were 4 IgG, 5 IgA, 1 light-chain only, and 1 unknown; ISS stages: I (n=3), II (n=6), III (n=1), and unknown (n=1). BM WGS identified a median of 3,087 somatic SNVs (range 467–4,059). Matched cfDNA tumor fraction was 7.6% (range 4.5-33.7%). Median deduplicated genome-wide coverage was 49× (range 25–57×) for Illumina and 155× (range 116–320×) for Ultima cfWGS, with median alignment rates of 98.4% and 99.0%, respectively. Duplication rates were higher for Ultima (median 20.2%, range 13.3–21.3%) than Illumina (median 8.5%, range 5.5–17.4%), consistent with increased sequencing depth. While this showed diminishing returns on efficiency at higher depths, coverage was sufficient to recover low-VAF mutations. Ultra-deep 150× cfWGS recovered significantly more somatic SNVs compared to 40× cfWGS (median 2,571 vs. 1,861; median increase of 21%, range -2–87%; paired t-test, p < 0.01). Compared to BM tumor DNA, 150× cfWGS recovered a median of 88% of somatic mutations (range, 67–97%) versus 66% (range, 36–91%) at 40×. Notably, 150× cfWGS detected 69% (11/16) of OncoKB-annotated oncogenic or likely oncogenic variants (including NRAS p.Q61R, KRAS p.A146V, and TP53 p.M237I), vs. 31% at 40×. Mutation recovery gains with ultra-deep sequencing were inversely correlated with cfDNA tumor fraction (Spearman’s ρ = –0.83; p < 0.01), with the greatest improvement (median 73%, range 33–87%) in cases with low (≤6%) cfDNA tumor fraction. Among variants present at ≥7.5% VAF in Ultima data, 4 of 5 actionable mutations were also detected at 40×, indicating strong concordance for high-burden alterations. Seven additional actionable mutations were uniquely identified at a 150× coverage, predominantly at low allele frequencies (≤6%, median VAF 3.8%, range 1.7–13.2%). Conclusions Ultra-deep cfWGS at 150× using Ultima sequencing recovered over twice as many actionable somatic mutations compared to 40× Illumina, despite lower sequencing costs in this pilot cohort. This tumor-informed analysis demonstrated that somatic mutations identified by Illumina BM WGS could be reliably recovered from cfDNA sequenced on the Ultima platform. These findings support the feasibility of ultra-deep cfWGS for comprehensive genomic profiling in MM and motivate further evaluation of cfDNA-based analyses as less invasive alternatives to BM biopsies. Future work will validate these findings in larger cohorts and extend analyses to other variant types, including translocations.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,260
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetMultiple Myeloma Research and Treatments→Travaux en français237 207→