Expanded human NK cells from lung cancer patients sensitize patients’ PDL1−negative tumors to PD1-blockade therapy
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer death worldwide despite the significant progress made by immune checkpoint inhibitors, including programmed death receptor-1 (PD1)/PD ligand 1 (PDL1)-blockade therapy. PD1/PDL1-blockade has achieved unprecedented tumor regression in some patients with advanced lung cancer. However, the majority of patients fail to respond to PD1/PDL1 inhibitors. The high rate of therapy non-response results from insufficient PDL1 expression on most patients' tumors and the presence of further immunosuppressive mechanisms in the tumor microenvironment. Here, we sensitize non-responding tumors from patients with lung cancer to PD1-blockade therapy using highly cytotoxic expanded natural killer (NK) cells. We uncover that NK cells expanded from patients with lung cancer dismantle the immunosuppressive tumor microenvironment by maintaining strong antitumor activity against both PDL1+ and PDL1- patient tumors. In the process, through a contact-independent mechanism involving interferon γ, expanded NK cells rescued tumor killing by exhausted endogenous TILs and upregulated the tumor proportion score of PDL1 across patient tumors. In contrast, unexpanded NK cells, which are susceptible to tumor-induced immunosuppression, had no effect on tumor PDL1. As a result, combined treatment of expanded NK cells and PD1-blockade resulted in robust synergistic tumor destruction of initially non-responding patient tumors. Thus, expanded NK cells may overcome the critical roadblocks to extending the prodigious benefits of PD1-blockade therapy to more patients with lung cancer and other tumor types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".