Exploiting autoimmunity unleashed by low‐dose immune checkpoint blockade to treat advanced cancer
Bibliographic record
Abstract
As a result of the cancer immunotherapy revolution, more than 2000 immuno-oncology agents are currently being tested or are in use to improve responses. Not unexpectedly, the 2018 Nobel Prize in Physiology or Medicine was awarded to James P. Allison and Tasuku Honjo for their development of cancer therapy by the blockade of co-inhibitory signals. Unfortunately, manipulation of the co-inhibitory receptors has also resulted in a safety issue: widespread iatrogenic immune-related adverse events (irAEs). Autoimmunity is emerging as the nemesis of immunotherapy. Originally, it was assumed that CTLA-4 blockade selectively targets T cells relevant to the antitumour immune response. However, an uncontrolled pan T cell activation was induced compromising tolerance to healthy self-tissues. The irAEs are very similar to that of a chronic graft-versus-host-disease (GVHD) reaction following allogeneic bone marrow transplantation (BMT). We hypothesized that ipilimumab induced a graft-versus-malignancy (GVM) effect, which eradicated metastatic melanoma in a minority of patients, but also involved an auto-GVHD reaction that resulted in widespread autoimmunity in the majority. Therefore, we argued for a profound theoretical point against the consensus of experts. The task is not to desperately put the genie back in the bottle by immune-suppressive treatments, but instead to harness the autoimmune forces. In this way, the same goal could be achieved by an antibody as by the adoptive transfer of alloreactive donor lymphocytes, but without severe GVHD. The proof-of-principle of a low-dose-combination immune checkpoint therapy, consisting only of approved drugs and treatments, was demonstrated in 111 stage IV cancer patients.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".