Mechanisms and Therapeutic Strategies to Overcome Immune Checkpoint Inhibitor Resistance in Melanoma, Head and Neck, and Triple-Negative Breast Cancers
Bibliographic record
Abstract
Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized cancer treatment by harnessing the host immune system to target malignancies. Melanoma, head and neck squamous cell carcinoma (HNSCC), and triple-negative breast cancer (TNBC) were among the first solid tumors to gain regulatory approval for ICIs due to their immunogenicity and unmet clinical needs. Melanoma exemplifies the success of ICI therapy, with durable responses driven by its high mutation burden and neoantigen landscape, yet both primary and acquired resistance remain major challenges. In contrast, HNSCC demonstrates clinically meaningful but modest responses in the context of a highly immunosuppressive tumor microenvironment, while TNBC derives limited benefit from ICI, often requiring combination strategies to achieve efficacy. Resistance to ICIs arises from complex tumor-intrinsic, microenvironmental, and systemic mechanisms that collectively undermine effective anti-tumor immunity. This review highlights both shared and cancer-specific mechanisms of ICI resistance across melanoma, TNBC and HNSCC. We also discuss emerging strategies, including combination therapies, neoantigen-based vaccines, adoptive T cell therapies, and precision oncology approaches, to overcome resistance and improve clinical outcomes. Together, these insights provide a framework for optimizing immunotherapy and advance durable benefit in these challenging malignancies.
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 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) | 0.001 | 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 | 0.002 |
| 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".