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Enregistrement W3094944282 · doi:10.1182/blood-2020-142270

Comparison of the Transcriptomic Signatures in Pediatric and Adult CML

2020· article· en· W3094944282 sur OpenAlexaff
Minyoung Youn, Hee‐Don Chae, Stephanie M. Smith, Alex Gia Lee, Lara Murphy Jones, Michele Donato, E. Alejandro Sweet‐Cordero, Parveen Abidi, Henrique Bittencourt, Norman J. Lacayo, Gary V. Dahl, Catherine Aftandilian, Kara L. Davis, Jairo Matthews, Steven M. Kornblau, Min Huang, Nathan Sumarsono, Michele S. Redell, Cecilia Fu, I-Ming L. Chen, Todd A. Alonzo, Elizabeth A. Eklund, Jason Gotlib, Purvesh Khatri, Nobuko Hijiya, Kathleen M. Sakamoto

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensCentre Hospitalier Universitaire Sainte-Justine
Organismes subventionnairesnon disponible
Mots-clésCD34MyeloidBone marrowTranscriptomeBiologyLeukemiaGeneMedicineImmunologyGene expressionGeneticsStem cell

Résumé

récupéré en direct d'OpenAlex

Introduction Pediatric chronic myeloid leukemia (CML) accounts for 10-15% of pediatric myeloid leukemias and 2-9% of all pediatric leukemias. There are several unique characteristics of CML diagnosed in children, adolescents, and young adults, compared to adults. They present with higher white blood counts and larger spleens, suggesting that the biology of pediatric CML is different from adult CML. We hypothesize that the differences in clinical presentation of pediatric CML patients are due to unique molecular characteristics that differ from adult CML patients. To test this hypothesis, we studied the transcriptomic signature of pediatric CD34+ CML cells compared to adult CML and normal age-matched bone marrow CD34+ cells. Methods CD34+ cells were isolated by FACS from pediatric CML (n=9), adult CML (n=10), pediatric normal (n=10) and adult normal (n=10) bone marrow samples. Total RNA was isolated from cells, and cDNA libraries were generated. Prepared libraries were sequenced on the Illumina HiSeq 4000 instrument. Raw sequences were trimmed and aligned to the hg38 reference genome with STAR/2.5.1b aligner. Gene level counts were determined with STAR -quantMode option using gene annotations from GENCODE (p5). Differential gene expression and pathway analysis were conducted with R/3.5.3. Counts were normalized with trimmed mean of M-values (TMM) from the EdgeR/ 3.24.3 package and further transformed with VOOM from the Limma/ 3.38.3 package. A linear model using the empirical Bayes analysis pipeline also from Limma was then used to obtain p-values, adjusted p-values and log-fold changes (LogFC). We performed three comparisons: (1) Pediatric CML vs Normal, (2) Adult CML vs Normal, and (3) Pediatric CML vs Adult CML. A False Discovery Rate (FDR) of £ .05 and absolute log2 fold-change > 1 was used to define differentially expressed genes in each comparison. Over-representation analysis was used to identify potentially unique pathways based on differentially expressed genes. Clinical and demographic features at diagnosis were extracted for pediatric and adult CML patients and compared using Fisher's exact test (categorical variables) or Wilcoxon rank sum test (continuous variables). Results Pediatric patients were diagnosed with CML at a median of 11 years (interquartile range (IQR): 10-14) compared to 54 years (IQR: 33-62) for adult patients. At diagnosis, pediatric patients had higher platelet counts (p=0.001) and larger spleen sizes (p=0.010) than adult patients, whereas the white blood cell count and phase at diagnosis did not differ. We found 606 genes (210 up- and 396 down-regulated) differentially expressed in pediatric CML CD34+ cells compared to pediatric normal controls. Interestingly, transcriptional regulators involved in blood cell differentiation including GATA1, TAL1, and KLF1 were differentially enriched in pediatric CML. In comparing adult CML patients to normal adult CD34+ cells, we found 920 genes (379 up- and 541 down-regulated) differentially expressed. Among all dysregulated genes we identified (1352 genes), 174 genes (54 up- and 120-down-regulated) overlapped when comparing pediatric and adult CML patients. Significantly enriched pathways in both adult and pediatric CML cells included PI3K/AKT signaling, MAPK signaling, and Notch/Wnt signaling, which have been previously reported. We found 437 unique genes that were dysregulated only in pediatric CML (270 up- and 167 down-regulated). Notch/Wnt signaling and Rho signaling pathways were significantly enriched. DLC1, a tumor suppressor gene that encodes a RhoGTPase-activating protein, has been known to be downregulated in solid tumors and hematologic malignancies. Interestingly, our data showed that DLC1 is significantly upregulated by 3-fold (p=0.0238) in pediatric CML, but not adult CML CD34+ cells. In addition, we observed that ABR, an inducer of C/EBPa that encodes an activator of RhoGEF and GTPase, was significantly downregulated by 2-fold (p=0.0119) in pediatric but not in adult CML CD34+ cells. Conclusion These results demonstrate unique molecular characteristics of pediatric CML that may contribute to the clinical differences at presentation between adult and pediatric disease. A better understanding of the particular biology of pediatric CML might impact the treatment of those patients in the future. Disclosures Gotlib: Deciphera: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: co-chair of the Study Steering Committee and Research Funding; Blueprint Medicines Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Chair of the Response Adjudication Committee and Research Funding, Research Funding.

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 seuil0,008

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 Opus0,017
Tête enseignante GPT0,269
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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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