PD-1 blockade delays tumor growth by inhibiting an intrinsic SHP2/Ras/MAPK signalling in thyroid cancer cells
Bibliographic record
Abstract
BACKGROUND: The programmed cell death-1 (PD-1) receptor and its ligands PD-L1 and PD-L2 are immune checkpoints that suppress anti-cancer immunity. Typically, cancer cells express the PD-Ls that bind PD-1 on immune cells, inhibiting their activity. Recently, PD-1 expression has also been found in cancer cells. Here, we analysed expression and functions of PD-1 in thyroid cancer (TC). METHODS: PD-1 expression was evaluated by immunohistochemistry on human TC samples and by RT-PCR, western blot and FACS on TC cell lines. Proliferation and migration of TC cells in culture were assessed by BrdU incorporation and Boyden chamber assays. Biochemical studies were performed by western blot, immunoprecipitation, pull-down and phosphatase assays. TC cell tumorigenicity was assessed by xenotransplants in nude mice. RESULTS: Human TC specimens (47%), but not normal thyroids, displayed PD-1 expression in epithelial cells, which significantly correlated with tumour stage and lymph-node metastasis. PD-1 was also constitutively expressed on TC cell lines. PD-1 overexpression/stimulation promoted TC cell proliferation and migration. Accordingly, PD-1 genetic/pharmacologic inhibition caused the opposite effects. Mechanistically, PD-1 recruited the SHP2 phosphatase to the plasma membrane and potentiated its phosphatase activity. SHP2 enhanced Ras activation by dephosphorylating its inhibitory tyrosine 32, thus triggering the MAPK cascade. SHP2, BRAF and MEK were necessary for PD-1-mediated biologic functions. PD-1 inhibition decreased, while PD-1 enforced expression facilitated, TC cell xenograft growth in mice by affecting tumour cell proliferation. CONCLUSIONS: PD-1 circuit blockade in TC, besides restoring anti-cancer immunity, could also directly impair TC cell growth by inhibiting the SHP2/Ras/MAPK signalling pathway.
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.
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 | 0.001 |
| 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".