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Enregistrement W2907714971 · doi:10.1182/blood-2018-99-116241

Identification of High Risk Group for Leukemic Transformation in Higher Risk MDS Patients Using Targeted RNA-Sequencing: Hematopoietic Stem Cell Signature As a High Risk Profile for Leukemic Transformation

2018· article· en· W2907714971 sur OpenAlexaff
Joon Ho Moon, Tae-Hyung Kim, Jae‐Sook Ahn, Seo‐Yeon Ahn, Sung‐Hoon Jung, Deok‐Hwan Yang, Je‐Jung Lee, Seung-Hyun Choi, Yoojin Lee, Sang Kyun Sohn, Hyeoung‐Joon Kim, Zhaolei Zhang, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensUniversity Health NetworkPrincess Margaret Cancer CentreOccupational Cancer Research CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésDecitabineOncologyAzacitidineInternal medicineMyelodysplastic syndromesLeukemiaMyeloidMyeloid leukemiaBone marrowInternational Prognostic Scoring SystemMedicineBiologyBioinformaticsComputational biologyGeneGeneticsDNA methylationGene expression

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Myelodysplastic syndrome (MDS) is a heterogeneous group of myeloid disorder characterized by defective bone marrow (BM) hematopoiesis with peripheral blood cytopenias and risk for progression to acute myeloid leukemia. Accurate determination of prognosis is critical to select an appropriate therapy and to detect any case progressing to leukemic transformation which brings ominous prognosis in patients with MDS. Despite clinical risk models, additional molecular data are needed to enhance the prediction of patients' clinical courses and to aid disease management. Therefore, the present study attempted to classify the higher risk MDS (HR-MDS) patients according to their molecular risk through targeted RNA-sequencing, to correlate it with clinical risk models, and analyzed molecular risk grouping for prognostic stratification power, especially for leukemic transformation in higher-risk patients with MDS treated with hypomethylating agents (HMA) including azacitidine or decitabine. Patients and Methods A total of 30 patients were included with HR-MDS by International Prognostic Scoring System (IPSS). Overall, 60 bone marrow samples (30 diagnosis and follow-up pairs) were subject for targeted RNA-seq using Illumina TruSight Pan-Cancer panel. After read mapping by Tophat2, gene count was measured using HTSeq followed by DEseq2 for differential gene expression quantification. All 60 samples as well as 30 samples from T-cell fraction (CD3+, as a control) were also subjected for DNA-seq targeting a panel of 84 commonly mutated genes in myeloid malignancies (Agilent SureSelect). All downstream computational and statistical analyses were performed using R and Python. Results The median age was 65 years (range 40-84 years) with 16 male patients (53%). Twenty-seven (90%) and 3 (10%) patients were intermediate-2 and high risk by IPSS, respectively. According to revised IPSS (IPSS-R), the distribution of risk groups was as follows: low (n=5, 17%), intermediate (n=8, 27%), high (n=11, 37%), and very high (n=6, 20%). A total of 56 mutations were detected in the diagnostic samples from 30 patients. Frequently mutated genes were DDX41 (n=5) and TP53 (n=4). Best response to HMA (16 azacitidine and 14 decitabine) was achieved in median 4 cycles (range 3-8). Complete response (CR) including marrow CR was achieved in 18 patients (60%), and 10 patients (33%) received allogeneic hematopoietic cell transplantation. Overall survival (OS) rate was not well correlated with IPSS-R risk groups. With median follow-up duration of 28.2 months (range 3.8-95), 3-years' OS rate showed 40%, 75%, 36%, and 67% in low, intermediate, high, and very high risk, respectively. Unsupervised clustering using top 100 genes with highest variance revealed 3 distinct clusters (n=8, 9, and 13 in group 1, 2, and 3), 3-years' OS rate of which showed 73%, 57%, and 35% in group 1, 2, and 3, respectively (p=0.004 between group 3 vs group 1/2). Despite inferior long-term outcomes in the group 3, the baseline clinical variables of some patients were classified as favorable implying that clinical factor does not reflect adverse long-term outcomes: 4 out of 13 patients with low risk by IPSS-R eventually experienced adverse outcome. The 3-years' leukemic transformation rate was 0%, 33% and 57% in group 1, 2, and 3 (p=0.039 between group 3 vs group 1/2). In the multivariate analyses, besides achievement of CR, the risk group 3 by RNA-seq were identified as independent adverse prognostic factors for OS (p=0.007, HR 6.75 [1.68-27.17]) as well as leukemic transformation (p=0.013, HR 6.91 [1.49-31.95]). In the gene set enrichment analysis using MSigDB, hematopoietic stem cell genes were enriched in RNA-seq group 3, suggesting that the high-risk signature on RNA-seq is linked with stemness of hematopoietic stem cells. Conclusion RNA-seq can be utilized to identify the higher risk patients with MDS. The higher risk group by RNA-seq enriched with genes with hematopoietic stem cells, which suggests that stemness in hematopoietic stem cells is linked with resistance to HMA therapy and increasing risk of leukemic transformation in HR-MDS. Figure. Figure. Disclosures No relevant conflicts of interest to declare.

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: 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
Métarecherche0,0000,000
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 GPT0,263
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
Publié2018
Routes d'admission1
Résumé présentoui

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