Dissecting the NK Cell Population in Hematological Cancers Confirms the Presence of Tumor Cells and Their Impact on NK Population Function
Bibliographic record
Abstract
The lymphocyte lineage natural killer (NK) cell is part of the innate immune system and protects against pathogens and tumor cells. NK cells are the main cell effectors of the monoclonal antibodies (mAbs) that mediates antibody-dependent cell cytotoxicity (ADCC). Hence, it is relevant to understand NK physiology and status to investigate the biological effect of mAbs in the clinic. NK cells are heterogeneous with multiple subsets that may have specific activity against different attacks. The presence of viral-sculpted NK cell populations has already been described, but the presence of cancer-sculpted NK cells remains unknown. Cancer induces a broad NK cell dysfunction, which has not been linked to a specific population. Here, we investigated the NK cell population by Uniform Manifold Approximation and Projection (UMAP) embed maps in Hodgkin lymphoma (HL) and acute myeloid leukemia (AML) patients at diagnosis and at least 30 days after treatment, which correlates with tumor cell clearance. We found that the NK lineage largely responded to the tumor by generating antitumor NK cells and renewing the population with a subset of immature NK cells. However, we failed to identify a specific “memory-like” subset with the NK cell markers used. Moreover, in patients in relapse, we found essentially the same NK populations as those found at diagnosis, suggesting that NK cells equally respond to the first or second tumor rise. Finally, we observed that previous cytomegalovirus (CMV) infection largely affects the tumor-associated changes in NK population, but the CMV-associated CD57+NKG2C+ NK cell population does not appear to play any role in tumor immunity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".