The present and future of immunocytokines for cancer treatment
Bibliographic record
Abstract
Monoclonal antibody (mAb) therapy has successfully been introduced as treatment of several lymphomas and leukemias. However, solid tumors reduce the efficacy of mAb therapy because of an immune-suppressive tumor micro-environment (TME), which hampers activation of effector immune cells. Pro-inflammatory cytokine therapy may counteract immune suppression in the TME and increase mAb efficacy, but untargeted pro-inflammatory cytokine therapy is limited by severe off-target toxicity and a short half-life of cytokines. Antibody-cytokine fusion proteins, also referred to as immunocytokines, provide a solution to either issue, as the antibody both acts as local delivery platform and increases half-life. The antibody can furthermore bridge local cytotoxic immune cells, like macrophages and natural killer cells with tumor cells, which can be eliminated after effector cells are activated via the cytokine. Currently, a variety of different antibody formats as well as a handful of cytokine payloads are used to generate immunocytokines. However, many potential formats and payloads are still left unexplored. In this review, we describe current antibody formats and cytokine moieties that are used for the development of immunocytokines, and highlight several immunocytokines in (pre-)clinical studies. Furthermore, potential future routes of development are proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".