Determinants for Antitumor and Protumor Effects of Programmed Cell Death
Bibliographic record
Abstract
Cytotoxic anticancer therapies activate programmed cell death in the context of underlying stress and inflammatory signaling to elicit the emission of danger signals, cytokines, and chemokines. In a concerted manner, these immunomodulatory secretomes stimulate antigen presentation and T cell-mediated anticancer immune responses. In some instances, cell death-associated secretomes attract immunosuppressive cells to promote tumor progression. As it stands, cancer cell death-induced changes in the tumor microenvironment that contribute to antitumor or protumor effects remain largely unknown. This is complicated to examine because cell death is often subverted by tumors to circumvent natural, and therapy-induced, immunosurveillance. Here, we provide insights into important but understudied aspects of assessing the contribution of cell death to tumor elimination or cancer progression, including the role of tumor-associated genetics, epigenetics, and oncogenic factors in subverting immunogenic cell death. This perspective will also provide insights on how future studies may address the complex antitumor and protumor immunologic effects of cell death, while accounting for variations in tumor genetics and underlying microenvironment.
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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".