Predicting interactome network perturbations in human cancer: application to gene fusions in acute lymphoblastic leukemia
Bibliographic record
Abstract
Genomic variations such as point mutations and gene fusions are directly or indirectly associated with human diseases. They are recognized as diagnostic, prognostic markers and therapeutic targets. However, predicting the functional effect of these genetic alterations beyond affected genes and their products is challenging because diseased phenotypes are likely dependent of complex molecular interaction networks. Using as models three different chromosomal translocations-ETV6-RUNX1 (TEL-AML1), BCR-ABL1, and TCF3-PBX1 (E2A-PBX1)-frequently found in precursor-B-cell acute lymphoblastic leukemia (preB-ALL), we develop an approach to extract perturbed molecular interactions from gene expression changes. We show that the MYC and JunD transcriptional circuits are specifically deregulated after ETV6-RUNX1 and TCF3-PBX1 gene fusions, respectively. We also identified the bulk mRNA NXF1-dependent machinery as a direct target for the TCF3-PBX1 fusion protein. Through a novel approach combining gene expression and interactome data analysis, we provide new insight into TCF3-PBX1 and ETV6-RUNX1 acute lymphoblastic leukemia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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 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".