Determinants for Antitumor and Protumor Effects of Programmed Cell Death
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it