RNA Sequencing-Based Measurement of Fusion-Transcript for Minimal Residual Disease (MRD) Monitoring in Core-Binding Factor Acute Myeloid Leukemia (CBF-AML)
Notice bibliographique
Résumé
Abstract Introduction Recent studies utilizing NGS demonstrated that residual allelic burden at complete remission (CR) is associated with worse overall survival (OS) and relapse incidence in AML. Quantitative PCR (qPCR) based disease monitoring is current practice in CBF-AML. However, qPCR requires standardization and the result for the same sample may vary depending on several factors. Also, other known prognostic factors such as cKIT mutation require an additional test. As RNA-seq can detect gene rearrangement as well as somatic mutations, we hypothesized that RNA-seq on samples taken at diagnosis and at remission can be used to monitor these genetic alterations simultaneously and can be utilized for minimal residual disease (MRD) monitoring in CBF-AML. Patients and Methods This study included 42 CBF-AML patients (23 RUNX1-RUNX1T1 and 19 CBFB-MYH11 AML). Overall, 84 bone marrow samples (42 diagnosis-CR pairs) were subjected to targeted RNA-seq using Illumina TruSight Pan-Cancer panel. After read mapping, gene count was measured using HTSeq followed by DEseq2 for gene expression quantification. Average number of sequenced reads was 3.5M reads with 87% overall mapping rate. Gene fusions in diagnostic samples were detected using EricScript. All 84 samples as well as 42 samples from T-cell fraction (CD3+, as a control) were also subjected to DNA sequencing, targeting a panel of 84 genes (Agilent SureSelect custom gene panel). Average on-target coverage was 1,606x. All other computational analyses were done using R and python. Results In diagnostic samples, class-defining gene fusion events were detected in all 42 patients. In CR samples, we tracked identical junctions identified in corresponding diagnostic samples. As expected, both CBFB-MYH11 and RUNX1-RUNX1T1 showed significant reduction in all CR samples compared to their corresponding diagnostic samples (p < 2.2e-13 and p < 6.3e-05, Fig A and B). CBFB-MYH11 was detectable in 6/19 CR samples (32%) and RUNX1-RUNX1T1 was detectable in 15/23 CR samples (65%). Reduction level of RUNX1-RUNX1T1 measured by RNA-seq showed positive correlation with the reduction level measured by qPCR (Pearson's Rho = 0.74, p < 5.4e-05, Fig C). As per mutational profile at diagnosis, we detected 74 mutations in 38 samples (n=38/42, 90%). NRAS (36%), KIT (36%), KRAS (17%) and, ASXL2 (17%) were commonly mutated. Survival analyses on each gene and each protein locus identified cKIT-D816 mutation as an adverse prognostic factor (HR = 3.57, [1.15 - 11.11], p = 0.028). We were able to detect all cKIT-D816 mutations in RNA-seq. Using information from NGS, we built a prognostic model for RUNX1-RUNX1T1 AML (n = 23). Decision tree analysis identified three distinct subgroups of RUNX1-RUNX1T1 AML on the basis of reduction level of RUNX1-RUNX1T1 and mutation profile (Fig D). Consistent with previous studies, 3-log or deeper reduction of RUNX1-RUNX1T1 transcript level was the most significant prognostic factor (low risk group). The algorithm further divided the patients who failed to achieve 3-log reduction according to the presence of cKIT-D816 mutation at diagnosis (intermediate and high risk group). For three defined groups, 2-year OS rates were 87%, 74%, and 33% (p = 0.08, Fig E) and 2-year relapse incidence rates were 13%, 42%, and 67% (p = 0.048, Fig F). Conclusion RNA-seq can be utilized to quantify RUNX1-RUNX1T1 and CBFB-MYH11 transcripts on diagnostic and CR samples in CBF-AML. We also showed that RNA-seq can stratify RUNX1-RUNX1T1 AML patients into three risk groups according to their long-term prognosis. Figure. Figure. Disclosures No relevant conflicts of interest to declare.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,002 | 0,001 |
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 ».