Presence of Recurrent Somatic Mutations in Mesenchymal Stromal Cell Fractions Isolated from Acute Myeloid Leukemia As an Evidence of Clonality
Notice bibliographique
Résumé
Introduction: Acute myeloid leukemia (AML) involves somatic mutations in hematopoietic stem cells (HSCs). Recent evidence suggests the bone marrow microenvironment, particularly mesenchymal stromal cells (MSCs), also influences AML development. Abnormalities in MSCs from AML patients have been observed (von der Heide et al., 2016), potentially impacting leukemia progression. This study aims to identify MSC somatic mutations and their consequences, further exploring their role in AML. Understanding MSCs' impact on the leukemic microenvironment may shed light on AML pathophysiology and guide targeted therapies, particularly benefiting patients with adverse risk disease. Methods: A total of 28 bone marrow cases were collected from AML patients with adverse risk, including those with complex karyotype, monosomy karyotype, chromosome 5/7/17 abnormalities, TP53 mutation, or relapsed/refractory AML. From these cases, 14 paired MSC and leukemic cell (LC) fractions were analyzed and are presented here. Magnetic bead sorting was used to isolate CD33+ LCs, while the plastic adherence method was employed for MSC culture. Following magnetic isolation, CD33- cells were plated in DMEM-LG with FBS and pen/strep, and MSC colonies formed within 48 hours. Contaminating cells were removed through media changes, and passaging was performed at 70-80% confluency. Flow cytometry at passage 3 characterized MSCs based on cell surface expression of CD105, CD73, and CD90, in accordance with the ISCT guideline. DNA and RNA were extracted from MSCs at passage 3 and subjected to whole exome sequencing (WES) using the Illumina platform, along with the LC fractions. WES aimed for 200X depth for the paired DNA samples. A standard bioinformatics pipeline was used for sequence alignment and variant calling, with variants requiring sufficient read depth (≥30x) and variant allele frequency in the case (>5%) and in the control (<5%). Synonymous variants were filtered out, and gene ontology and pathway enrichment analyses explored the biological significance and potential involvement in leukemogenesis of the identified mutations. Whole transcriptomics for gene expression analyses was also conducted for both MSC and LC fractions. Results: In 14 analyzed cases, a total of 548 somatic variants were identified in the MSC fraction, affecting 480 genes, including 518 nonsynonymous single nucleotide variants (SNVs), 7 stop-gain SNVs, 1 stoploss, 1 non-frameshift insertion, 2 frameshift deletions, 1 frameshift insertion, and 2 non-frameshift deletions, with 16 unknown variants. The average read depth was 75 (range: 15 - 887), and the average variant allele frequency was 50% (range: 11% - 90%). On average, 38 genes were mutated per MSC case, with 8 genes detected in at least 3 cases, 25 in exactly 2 cases, and 447 genes found in only one case. In the leukemic fraction, 686 somatic variants in 595 genes were identified, with an average read depth of approximately 75 (range: 10 - 2596) and an average variant allele frequency of 47% (range: 8% - 85%). On average, 48 mutations were detected per leukemic case, with 12 mutations found in at least 3 cases, 40 in 2 cases, and 543 in only one case. Among the 14 cases, 10 cases (71%) had mutations in known driver genes for AML ( FLT3, NPM1, IDH1, IDH2, DNMT3A, RUNX1, TP53) exclusively in the leukemic fraction. As seen in Figure 1., of the 480 genes mutated in the MSC fraction, 69 overlapped with the 595 genes mutated in the leukemic fraction, while 411 genes were uniquely mutated in the MSC fraction. Further analysis is ongoing, and pathway analysis will be conducted once the remaining 14 cases are processed. Conclusions: This study identifies distinct somatic mutations in AML patients' MSC and leukemic cell fractions, revealing genomic complexity and crosstalk impacting leukemia progression. Understanding the functional implications of these mutations is crucial for unraveling their roles in leukemogenesis and developing personalized therapeutic interventions targeting MSC somatic mutations.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 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 ».