CD103+CD56+ ILCs Are Associated with an Altered CD8+ T-cell Profile within the Tumor Microenvironment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Immunotherapies have had unprecedented success in the treatment of multiple cancer types, albeit with variable response rates. Unraveling the complex network of immune cells within the tumor microenvironment (TME) may provide additional insights to enhance antitumor immunity and improve clinical response. Many studies have shown that NK cells or innate lymphoid cells (ILC) have regulatory capacity. Here, we identified CD103 as a marker that was found on CD56+ cells that were associated with a poor proliferative capacity of tumor-infiltrating lymphocytes in culture. We further demonstrated that CD103+CD56+ ILCs isolated directly from tumors represented a distinct ILC population that expressed unique surface markers (such as CD49a and CD101), transcription factor networks, and transcriptomic profiles compared with CD103-CD56+ NK cells. Using single-cell multiomic and spatial approaches, we found that these CD103+CD56+ ILCs were associated with CD8+ T cells with reduced expression of granzyme B. Thus, this study identifies a population of CD103+CD56+ ILCs with potentially inhibitory functions that are associated with a TME that includes CD8+ T cells with poor antitumor activity. Further studies focusing on these cells may provide additional insights into the biology of an inhibitory TME.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it