New pathways in immune stimulation: targeting OX40
Bibliographic record
Abstract
Immune checkpoint blockers (ICB) reinvigorate the immune system by removing the molecular brakes responsible for the scarce activity of immune phenotypes against malignant cells. After having proven their remarkable role as monotherapy, combinations of anti-Programmed cell death 1 (PD-1)/Programmed death-ligand 1 (PD-L1) agents with cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) antibodies, chemotherapy and/or anti-angiogenic compounds provide unprecedented results and durable responses across a variety of tumour types. Nevertheless, the main drawbacks of ICB are represented by primary and acquired resistance, translating into disease progression, as well as by immune-related toxicities. In this sense, novel strategies to foster the immune system through its direct stimulation are being tested in order to provide additional clinical improvements in patients with cancer. In this scenario, the co-stimulatory molecule OX40 (CD134) belongs to the next generation of immune therapeutic targets. Preliminary results of early clinical trials evaluating OX40 stimulation by means of different agents are encouraging. Here we review the rationale of OX40 targeting, highlighting the combination of OX40-directed therapies with different anticancer agents as a potential strategy to foster the immune system against malignant phenotypes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".