Investigating the Role of the SUMO System in PAX5- ETV6-driven Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Acute Lymphoblastic Leukemia (ALL) is the most common childhood cancer, diagnosed in ~1140 children every year in Canada. Despite a high 5-year survival rate, it remains one of the leading causes of cancer morbidity and mortality in children. Predisposing factors of ALL include several chromosomal translocations that give rise to fusion proteins. Among them is PAX5-ETV6, a gene fusion present in ~1% of pediatric ALL cases. Very little is known about the PAX5-ETV6 protein interactome. Using proximity-dependent biotinylation, I found a prominent association between PAX5-ETV6 and components of the SUMO system, which plays important roles in heterochromatin formation and maintenance. A better understanding of this interaction could help shed light on the initiation mechanism for this disease and importantly - could be targeted therapeutically. So, I investigated the role of the SUMO system in PAX5-ETV6 leukemogenesis and examined the possible therapeutic effects of SUMO inhibitors on these types of cancers.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".