Bibliographic record
Abstract
Melanoma has long been recognized as a potentially immunogenic tumour, but only recently has it become clear that the reason for this resides in its many ultraviolet (UV)-induced mutations and expression of multiple autoantigens which can be targeted by the immune system. The first successful applications of immune-based treatments included passive immunotherapy using high-dose interleukin (IL)-2 and/or adoptive transfer of natural killer (NK)-cells, as well as active immunotherapy using whole cell-derived or peptide vaccines. In the intervening decades, it has become clear that these approaches can lead to durable responses in stage III/IV melanoma, and even to functional cures - but only in a vanishingly small fraction of patients. With the advent of immune checkpoint blockade first with anti-cytotoxic T-lymphocyte 4 (CTLA-4), then with anti-programmed cell death 1 (PD-1) antibodies, and combinations thereof, the small percentage of responding patients may be increased to half, a major accomplishment in this refractory disease. Improved techniques for identifying mutation-derived neoantigens and thus more sophisticated active immunotherapies, probably combined with checkpoint blockade, currently hold great promise for further increasing the fraction of responding patients. As additional immunomodulatory antibodies and therapies become available, it will be increasingly important to develop diagnostic tools to determine which particular therapy is likely to elicit the best response for the individual patient. Practically speaking, therapy selection and efficacy monitoring on the basis of the results of a blood test would be most desirable. The purpose of this review is to consider the feasibility of identifying 'immune signatures' for predicting responses and determining mechanisms responsible for success or failure of these immunotherapies.
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.
How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".