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Enregistrement W4405052607 · doi:10.1182/blood-2024-201658

Detection of Recurrent Mutations, Immunoglobulin Rearrangements and Copy Number Changes in Cell-Free DNA and Bone Marrow on Patients with Waldenstrom's Macroglobulinemia over a Course of Treatment

2024· article· en· W4405052607 sur OpenAlexaff
Signy Chow, Arnavaz Danesh, Kim Roos, Suzanne Trudel, Trevor J. Pugh, Christine I. Chen, Neil L. Berinstein

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésWaldenstrom macroglobulinemiaMacroglobulinemiaMinimal residual diseaseBone marrowRituximabLymphoplasmacytic LymphomaIbrutinibBiologyMedicineCancer researchMultiple myelomaAntibodyInternal medicineImmunologyChronic lymphocytic leukemiaLeukemiaLymphoma

Résumé

récupéré en direct d'OpenAlex

Background & Methods We developed a targeted-gene next generation sequencing (NGS) panel including immunoglobulin VDJ probes for use in Waldenstrom's Macroglobulinemia (WM) to characterize genomic alterations in treatment-naïve patients and follow genomic alterations over time in bone marrow (BM) and peripheral blood (PB) cell free DNA (cfDNA). Shallow whole genome sequencing (sWGS) is used to characterize copy number alterations. This panel was piloted on PB from 44 patients with a clinical diagnosis of WM, 8/44 with paired BM and is now being utilized to follow patients on the “Bendamustine-Rituximab in combination with Acalabrutinib in Waldenstrom's Macroglobulinemia (BRAWM)” study. We aim to characterize the mutation profile of treatment-naïve WM, follow genomic changes over time, explore minimal residual disease (MRD) analysis and compare PB to BM. Results In the pilot study, 5/8 paired PB/BM samples were positive for MYD88 L265P by clinical PCR and NGS on BM samples (100% concordant), however only 3/5 of the known MYD88 L265P mutations were detected in PB (60% concordant). CXCR4 mutations were identified in 3/5 MYD88 mutated samples. Chromosome 6 changes were detected in 2 samples, both of which were MYD88 mutated, and one of which also had a less common CXCR4 mutation (K331Rfs*12). In 36 cfDNA samples without paired BM, 5/36 were concordantly positive for MYD88 L265P compared to clinical PCR, 13/36 were concordantly negative, 13/36 were negative by NGS but clinical PCR positive and 5/36 were NGS positive but clinical PCR negative. Of the 13 discordant clinical PCR positive and NGS negative samples, 4/13 PB samples had insufficient DNA for NGS (<83ng), 4/13 had PB taken after treatment for WM, and there was not a clear explanation for remaining 5/13. On the BRAWM study, 14 paired BM/cfDNA screening samples and 4 cycle 7 samples were sequenced. 2/14 failed trial screening and did not have clinical data available. 11/12 remaining patients were MYD88 L265P mutated by clinical PCR. Concordance using targeted NGS was 100% for BM and 82% for cfDNA for pre-treatment samples. CXCR4 mutations were detected in 7/12 samples by clinical PCR and in 9/12 by NGS. 5/7 patients identified as CXCR4 mutated by clinical PCR had concordant results with NGS. The majority of CXCR4 mutations were S338*, however additional mutations were discovered by NGS. Only 3/9 CXCR4 mutations found in BM were detected in PB by NGS. Other mutations were identified in ARID1A, ATM, KMT2D, TBL1XR1, KMT2D, TP53 and CD79B genes. One sample showed an acquired PLCG2 mutation at cycle 7. VDJ rearrangements were found in all screening BM and PB samples. For MRD assessments at cycle 7, 2/4 were positive by VDJ and MYD88 analysis, 1/4 was positive by VDJ only and 1/4 was negative. sWGS identified 6q losses in 3/12 samples, an 11q loss in 1/12 and a chromosome 3 gain in 1/12 patients. Clinical cytogenetic information was not presently available for comparison. Conclusions: A custom targeted-capture panel NGS approach in WM with VDJ and gene probes can detect mutations and immunoglobulin rearrangements in WM in BM and PB, and can be used to follow somatic mutations that change with treatment and time, while sWGS can identify copy number alterations. Both mutation and VDJ sequencing can be used for MRD analysis. Targeted-capture sequencing may detect less common mutations within CXCR4 compared to PCR-based assays. Sensitivity in cfDNA is reduced compared to BM-derived genomic DNA. cfDNA analysis may not be sensitive in low disease burden states, including after treatment and for MRD analysis. Because of the potential clinical value in detecting molecular changes from cfDNA, further experiments to improve sensitivity in PB are being explored.

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)0,001
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,002

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
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 Opus0,008
Tête enseignante GPT0,266
Écart entre enseignants0,258 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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