Clinical Potential of Kinase Inhibitors in Combination with Immune Checkpoint Inhibitors for the Treatment of Solid Tumors
Bibliographic record
Abstract
Oncogenic kinases contribute to immunosuppression and modulate the tumor microenvironment in solid tumors. Increasing evidence supports the fundamental role of oncogenic kinase signaling networks in coordinating immunosuppressive tumor microenvironments. This has led to numerous studies examining the efficacy of kinase inhibitors in inducing anti-tumor immune responses by increasing tumor immunogenicity. Kinase inhibitors are the second most common FDA-approved group of drugs that are deployed for cancer treatment. With few exceptions, they inevitably lead to intrinsic and/or acquired resistance, particularly in patients with metastatic disease when used as a monotherapy. On the other hand, cancer immunotherapies, including immune checkpoint inhibitors, have revolutionized cancer treatment for malignancies such as melanoma and lung cancer. However, key hurdles remain to successfully incorporate such therapies in the treatment of other solid cancers. Here, we review the recent literature on oncogenic kinases that regulate tumor immunogenicity, immune suppression, and anti-tumor immunity. Furthermore, we discuss current efforts in clinical trials that combine kinase inhibitors and immune checkpoint inhibitors to treat breast cancer and other solid tumors.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".