Dogs with acute leukemia have shared and unique genetic mutations compared to human acute leukemia
Notice bibliographique
Résumé
Abstract Introduction: Acute leukemia represents a heterogeneous group of aggressive hematopoietic malignancies in humans, broadly encompassing myeloid (AML) and lymphoid (ALL) subtypes. Advances in human AL therapy have been driven by molecular profiling and the development of targeted treatments. However, preclinical models that capture disease heterogeneity and recapitulate the tumor microenvironment remain limited. Pet dogs spontaneously develop acute leukemias that share many clinical, biologic, and therapeutic features with their human counterparts, providing an opportunity to use dogs as a robust comparative and translational model. Despite this potential, the molecular drivers of canine acute leukemia are poorly defined. Methods: We extracted DNA from blood, bone marrow or lymph node aspirates and buccal swabs (as a germline control) from 33 dogs prospectively recruited for a study on acute leukemia. Using a flow cytometric-based antibody panel against lineage-associated antigens, leukemias were categorized as AML (only expressing myeloid antigens), ALL (only expressing lymphoid antigens), acute leukemia of ambiguous lineage (ALAL, expressing myeloid and lymphoid antigens), and acute lineage negative leukemia (ALNL, lacking lineage-associated markers). Extracted DNA was submitted for whole-exome (200x) or whole-genome sequencing (50-100x). Somatic variant calling was performed following GATK best practices, using Mutect2 in tumor–normal mode with a canine panel of normals (n = 77) and a curated germline variant database (n = 722). Candidate variants were filtered to those predicted to have moderate-to-high functional impact in known cancer-associated genes and excluded splice site mutations. Results: On flow cytometric analysis, 23, 2, 2 and 6 cases were classified as AML, ALL, ALAL and ALNL, respectively. Somatic mutations were identified in 31 of 33 (93.9%) tumor samples. Of the samples with identified variants, at least one somatic mutation in the RTK–RAS pathway (e.g., NRAS, KRAS, PTPN11, FLT3, KIT) was detected in 71% of cases, paralleling human AL where this pathway is a major driver. NRAS was the most frequently mutated gene (23%), with recurrent hotspots (G12, G13, Q61) shared with human leukemia. Additional pathway alterations were identified in Hippo (23), NOTCH (23%), and PI3K/WNT (10). Mutations in epigenetic modifiers, including KDM5C (10%), KAT6B (10%), and EZH2 (10%), were also common. In contrast, canonical human AL drivers DNMT3A and NPM1 were absent, and recurrent mutations were also seen in MED12 (13%), TPR (13%), CLIP1 (10%) , FAT3 (10%) and MAML2 (10%). Most variants were missense mutations, with a median of six variants per sample. Conclusions: Our results demonstrate that canine acute leukemia harbors both conserved and unique mutational events compared to humans. The high frequency of RTK–RAS pathway alterations highlight the potential of dogs as a spontaneous, immunocompetent model for dissecting leukemogenic mechanisms and advancing the development of pathway-targeted therapies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».