Gene Expression Analysis of CML Patients across the Age Spectrum
Notice bibliographique
Résumé
Abstract Chronic myeloid leukemia (CML) accounts for 2-9% of leukemias in children and adolescents, and occurs with much greater frequency in adults. Compared to adults, children with CML tend to present with higher white blood cell 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 are due to unique molecular characteristics that differ from adult CML. To test this hypothesis, we compared the transcriptomic signature of pediatric and adult CML CD34+ cells and healthy age-matched CD34+ cells. CD34+ cells were isolated by FACS from pediatric CML (n=9), adult CML (n=10), pediatric healthy (n=10), and adult healthy (n=10) bone marrow samples. 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 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. Four comparisons were performed: (1) pediatric CML vs pediatric healthy, (2) adult CML vs adult healthy, (3) pediatric CML vs adult CML, and (4) pediatric healthy vs adult healthy. A False Discovery Rate of ≤ .05 and absolute log2 fold-change > 1 was used to define differentially expressed genes (DEGs) in each comparison. To identify potentially unique pathways based on DEG, pathway over-representation was calculated with either goana from the limma package or clueGO. At diagnosis, pediatric patients had higher platelet counts (p=0.001) and larger spleen sizes (p=0.010) than adult patients. Median WBC counts were 273,000 and 143,000 in pediatric and adult patients respectively. A total of 1352 genes were differentially expressed in either adult or pediatric CML CD34+ cells compared to healthy CD34+ cells, 174 of which were expressed similarly in pediatric and adult CML CD34+ cells (54 up- and 120 down-regulated). There were 746 differentially expressed genes (325 up- and 421 down-regulated) in adult CML CD34+ cells compared to adult healthy CD34+ cells, and 432 differentially expressed genes (156 up- and 276 down-regulated) in pediatric CML CD34+ cells compared to pediatric healthy CD34+ cells. In direct comparison of pediatric and adult CML CD34+ cells, 446 genes (270 up and 176 down) were dysregulated in pediatric CML CD34+ cells. Pathway analysis showed that Rho signaling pathway was downregulated in pediatric CML CD34+ cells and several genes in Rho pathway were uniquely dysregulated. ARHGAP27 and VAV2 were significantly upregulated in adult CML CD34+ cells by 3.7-fold (p=0.0453) and 11-fold (p=0.0072), respectively, compared to pediatric CML CD34+ cells. In addition, several genes involved in the NADPH oxidase pathway, one of the best-characterized Rho GTPase-regulated systems, were differently expressed in CML. NCF1, CYBB, and S100A8 were significantly upregulated in adult CML CD34+ cells by 4-fold (p=0.0045), 3.26-fold (p<0.0001), and 3.09-fold (p<0.0001), respectively, compared to pediatric CML CD34+ cells. Furthermore, DLC1, which is known as a negative regulator of Rho pathway, was significantly upregulated in pediatric CML CD34+ cells by 2.47-fold (p=0.0493) compared to adult CML CD34+ cells. 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 molecular biology of CML across the ages will provide new insights into the pathogenesis of pediatric CML and potentially inform future treatment decisions. Disclosures Davis: Jazz Pharmaceuticals: Research Funding; Novartis Pharmaceuticals: Honoraria. Hijiya: Novartis: Consultancy; Stemline Therapeutics: Consultancy.
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 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 | 0,000 | 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 | 0,000 |
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.
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 ».