Proteomic characterization of EL4 lymphoma‐derived tumors upon chemotherapy treatment reveals potential roles for lysosomes and caspase‐6 during tumor cell death in vivo
Bibliographic record
Abstract
The murine mouse lymphoblastic lymphoma cell line (EL4) tumor model is an established in vivo apoptosis model for the investigation of novel cancer imaging agents and immunological treatments due to the rapid and significant response of the EL4 tumors to cyclophosphamide and etoposide combination chemotherapy. Despite the utility of this model system in cancer research, little is known regarding the molecular details of in vivo tumor cell death. Here, we report the first in-depth quantitative proteomic analysis of the changes that occur in these tumors upon cyclophosphamide and etoposide treatment in vivo. Using a label-free quantitative proteomic approach a total of 5838 proteins were identified in the treated and untreated tumors, of which 875 were determined to change in abundance with statistical significance. Initial analysis of the data reveals changes that may have been predicted, such as the downregulation of ribosomes, but demonstrates the robustness of the dataset. Analysis of the dataset also reveals the unexpected downregulation of caspase-3 and an upregulation of caspase-6 in addition to a global upregulation of lysosomal proteins in the bulk of the tumor.
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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) | 0.001 | 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".