Overcoming Resistance Mechanisms to Immune Checkpoint Inhibitors: Leveraging the Anti-Tumor Immune Response
Bibliographic record
Abstract
As far back as 3000 years ago, the immune system was observed to play a role in mediating tumor regression. Since then, many strategies have been developed to leverage the anti-tumor immune response. However, while many patients respond to ICIs up front some do not, and many of those that do eventually experience tumor progression. Currently, there are several predictive biomarkers of the immune checkpoint inhibitor response; however, no one test appears to be universally predictive and their application varies by disease site. There are many ways in which cancer cells develop primary or acquired resistance to immune checkpoint inhibitors. Efforts to reverse resistance include ways to combat T cell exhaustion, reprogram the tumor microenvironment, increase the availability of tumor neo-antigens, target alternative immune checkpoints, restore a normal/healthy patient gut microbiome, oncolytic viruses and tumor vaccines. The most studied and most promising methods include combining ICIs with therapies targeting alternative immune checkpoints and restoring a normal/healthy patient gut microbiome. This review will discuss T cell-mediated immunity, how this is leveraged by modern immunotherapy to treat cancer and mechanisms of immune checkpoint inhibitor resistance, while highlighting strategies to overcome primary and secondary resistance mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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; 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".