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Enregistrement W4389231113 · doi:10.1182/blood-2023-190978

Characterization of Clonal Hematopoietic of Indeterminate Potential (CHIP) Mutations in an Imid-Naïve Multiple Myeloma (MM) Autologous Stem Cell Transplant (ASCT) Population: First Results from a Pre-Transplant Time Point in a Prospective, Longitudinal Study

2023· article· en· W4389231113 sur OpenAlexaff
Sahar Khan, Salman Basrai, Donna Reece, Sita Bhella, Vishal Kukreti, Anca Prica, A. Keith Stewart, Suzanne Trudel, Harjot Vohra, Chloe Yang, Sagi Abelson, Christine Chen

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity Health NetworkOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésPopulationMedicineOncologyInternal medicineMelphalanTransplantationHematopoietic stem cell transplantationMultiple myeloma

Résumé

récupéré en direct d'OpenAlex

Background: Clonal Hematopoiesis of Indeterminate Potential (CHIP) is defined by the acquisition of ≥ 1 somatic mutations in the blood of healthy adults, but can also be detected at higher prevalence in patients with malignancies, particularly after DNA damaging therapy. The presence of CHIP mutations in MM confers a reduced overall survival (Mouhieddine, JCO 2020) but high quality, prospective evaluation of CHIP mutation evolution over the transplant sequence, including the impact of high-dose melphalan and immunomodulatory (IMiD)-based therapy, is largely under-studied. Serial assessment of CHIP over the transplant course has been reported from the coMMpass dataset, demonstrating a 4-fold increase in mutation prevalence from 5.8% at diagnosis to 25% after 3.1 years (Mouhieddine, Blood, 2021). In this data set, most patients were exposed to prior IMiD-containing at some point during therapy. Whether the survival benefit reported with IMiD-based maintenance therapy post-transplant is associated with CHIP mutation modulation is unclear. We are therefore investigating the longitudinal evolution of CHIP in a cohort of transplant-eligible, largely IMiD-naïve patients (pts) over the pre- and post-ASCT course in an ongoing study. Here, we present preliminary findings from the pre-transplant time-point after non-IMiD-containing induction but before high-dose melphalan and ASCT. Methods: The ongoing Princess Margaret Cancer Center ARCH 001 trial is a prospective, longitudinal study evaluating evolution of CHIP in a transplant-eligible MM population. Mutation testing is performed using the highly sensitive single-molecule molecular inversion probe (SmMIP) next-generation sequencing (NGS) technique (Abelson, Bioinformatics, 2022). After sequencing with a minimum sequencing depth of 4000x, the list of mutation calls produced by smMIP-tools is subject to various filters to reduce the likelihood of false positives. Synonymous mutations, as well as any variants falling within introns or splice regions are removed. The minor allele frequency of the variant (if available) is required to be above 0.1% so as to exclude mutations that are common SNPs. VAF frequency threshold of 1-30 % is set for calling somatic mutations, with a view to minimize false positives and germ-line mutations. Multiple time points for testing pertinent to therapy include: within 1 month pre-transplant (after induction and stem cell collection), 3 months post-transplant, and two subsequent samples 12 and 24 months post-sample 2. Here we report preliminary results of CHIP mutation testing in the first 66 pts at the pre-transplant time point. Results: Patient, disease and treatment characteristics for all pts and their stratification by the presence of CHIP mutations are outlined in Table 1. The total cohort is typical for a transplant-eligible population with median age 65 years, male predominance (M 59%/F 41%), 60% IgG subtype, 23% high-risk FISH cytogenetics. Most (90%) received a non-IMiD containing induction regimen CyBorD; only 6% received an IMiD pre-ASCT. CHIP mutations were identified in 28/66 pts, for a mutation prevalence rate of 43%; 6 patients (9%) carried ≥ 1 mutation. The median age at transplant as well as other baseline demographics did not differ between those with and without mutations (Table 1). Mutation profiles are shown in Figure 1, and are consistent with those described in the literature with the most commonly mutated genes being DNMT3a, TET2, ASXL1 and PPM1D, and the mutation prevalence rate highest for DNMT3a at 21%. 6 pts (9%) had ≥ 1 mutation (range 2-3) identified, with 3 of 6 involving the DNMT3a gene. Other genes involved in patients with multiple mutations were TET2 (n=3), ASXL1 (n=2), PPM1D (n=2), SRSF2 (n=1), SF3B1 (n=1). Mean allele frequency was 4%, ranging from 1-19. Conclusion: Our results using deep SmMIP sequencing show a mutation prevalence rate of 43% in a cohort of transplant-eligible, largely IMiD-naive MM pts studied after induction. These rates are higher than those generally described in the literature, which may relate to the high sensitivity of the assay used, variation in filtering between institutions, and the deliberate use of a relatively low VAF threshold with the intent to capture clinically significant variants for longitudinal tracking during the course of this ongoing study. Further studies during lenalidomide maintenance will be forthcoming.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
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 Opus0,019
Tête enseignante GPT0,271
Écart entre enseignants0,252 · 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
Publié2023
Routes d'admission1
Résumé présentoui

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