Dogs with acute leukemia have shared and unique genetic mutations compared to human acute leukemia
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".