Charting a killer course to the solid tumor: strategies to recruit and activate NK cells in the tumor microenvironment
Bibliographic record
Abstract
has dramatically changed the landscape in the development of novel adoptive cell therapies for treating cancer over the last decade. NK cells have become a key player for cancer immunotherapy due to their innate ability to kill malignant cells while not harming healthy cells, allowing their potential use as an "off-the-shelf" product. Furthermore, recent advancements in NK cell genetic engineering methods have enabled the efficient generation of chimeric antigen receptor (CAR)-expressing NK cells that can exert both CAR-dependent and antigen-independent killing. Clinically, CAR-NK cells have shown promising efficacy and safety for treating CD19-expressing hematologic malignancies. While the number of pre-clinical studies using CAR-NK cells continues to expand, it is evident that solid tumors pose a unique challenge to NK cell-based adoptive cell therapies. Major barriers for efficacy include low NK cell trafficking and infiltration into solid tumor sites, low persistence, and immunosuppression by the harsh solid tumor microenvironment (TME). In this review we discuss the barriers posed by the solid tumor that prevent immune cell trafficking and NK cell effector functions. We then discuss promising strategies to enhance NK cell infiltration into solid tumor sites and activation within the TME. This includes NK cell-intrinsic and -extrinsic mechanisms such as NK cell engineering to resist TME-mediated inhibition and use of tumor-targeted agents such as oncolytic viruses expressing chemoattracting and activating payloads. We then discuss opportunities and challenges for using combination therapies to extend NK cell therapies for the treatment of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".