RNA-Seq analysis of gene expression in 25 cases of canine lymphoma undergoing CHOP chemotherapy
Bibliographic record
Abstract
OBJECTIVES: Canine lymphoma, the most common hematological cancer in dogs, shares many molecular and clinical characteristics with human Non-Hodgkin lymphoma (NHL). The standard treatment for canine lymphoma is "CHOP" multiagent chemotherapy protocol consisting of Cyclophosphamide, Doxorubicin (Hydroxydaunorubicin), Vincristine (Oncovin™), and Prednisone. Approximately 70-85% of patients treated with CHOP achieve clinical remission. However, duration of remission varies and the majority of dogs eventually relapse. To identify possible biomarkers for patients failing to achieve remission, we performed RNA-Seq analysis on 25 cases of canine lymphoma obtained prior the start of their CHOP therapy regime and assessed gene expression associated with patient progression free survival (PFS). DATA DESCRIPTION: The data consists of (1) raw RNA-Seq reads in 75 bp fastq format from fine needle aspirate samples of enlarged lymph nodes from canine patients with naturally occurring lymphoma; (2) Fragments Per Kilobase Million (FPKM) values for each sample; (3) raw transcript counts for each sample; (4) anonymized patient details including PFS; (5) heat map of gene expression and (6) Cox proportional hazard analysis showing significantly expressed genes. These data may be useful for comparative analysis of gene expression in human NHL and analysis of gene expression associated with disease outcome in canine lymphoma.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".