Barking up the right tree: Immune checkpoint signatures of human and dog cancers
Bibliographic record
Abstract
In the quest for improved therapeutics targeting immune checkpoints (ICs), we turn to spontaneously developing dog (canine) cancers, some of which genetically and clinically mirror the human counterparts. Despite its potential, canine cancer immunology remains largely unexplored. Here, we examine the RNA-seq-based expression of 44 ICs across 14 canine cancer types and an extensive human dataset. We unveil diverse canine IC expression patterns and unique human IC signatures that reflect the histological type and primary site of cancer. We uncover a striking similarity between canine brain cancers, osteosarcoma, and their human counterparts, identifying them as prospective immunotherapy models. Four ICs - CD160, A2AR, NKG2A, and OX40 - are key to the differences observed between species. We also find that individual human IC signatures vary in how well they align with their respective cancer types, a pattern that may influence future approaches to personalized therapy. This exploration highlights aspects of canine and human cancer immunology that warrant further study, setting the stage for discoveries at their crossroads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".