PDX models reflect the proteome landscape of pediatric acute lymphoblastic leukemia but divert in select pathways
Bibliographic record
Abstract
Abstract Murine xenografts of pediatric leukemia are known to accurately recapitulate genomic aberrations. How this translates to the functional capacity of the proteome is unknown. Here, we studied global protein abundance, phosphorylation, and proteolytic processing in 11 pediatric B- and T-cell acute lymphoblastic leukemia patients and 19 corresponding xenografts. Protein level differences that stratified pediatric disease subtypes at diagnostic and relapse stages were largely recapitulated in xenograft models. Patient xenografts lacked multiple human leukocyte antigens, and complement proteins, and presented incomplete response mechanisms to the host immune system which is absent in the murine model. The dominant expression of MKI67 and cell cycle proteins indicated a high proliferative capacity of xenografted cells residing in the spleen. Structural genomic changes and mutations found in patients were reflected at the protein level. The post-translational modification landscape is shaped by leukemia type and host and only to a limited degree by the patient of origin. This study portrays how genomic and host factors shape protein and post-translational modification landscapes differently, and confirms murine patient-derived xenograft as competent model system while highlighting important areas of diverging biology.
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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 | 0.000 |
| 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) | 0.002 | 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 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".