NANOG confers resistance to complement-dependent cytotoxicity in immune-edited tumor cells through up-regulating CD59
Bibliographic record
Abstract
Abstract Cancer immunoediting drives the adaptation of tumor cells to host immune surveillance. Previously, we have demonstrated that immunoediting driven by cytotoxic T lymphocytes (CTLs) enriches NANOG+ tumor cells with immune-refractory properties. Here, we found that CTL-mediated immune pressure triggered cross-resistance of tumor cells to the complement system, a part of the innate immune system. In this process, NANOG upregulated the membrane-bound complement regulatory protein (mCRP) CD59 through promoter occupancy, thereby contributing to the resistance of tumor cells against complement-dependent cytotoxicity (CDC). Notably, targeting of NANOG sensitized the immune-refractory tumor cells to trastuzumab-mediated CDC. Collectively, our results revealed a possible mechanism through which selection imposed by T-cell based immunotherapy triggered complement-resistant phenotypes in the tumor microenvironment (TME), by establishing a firm molecular link between NANOG and CD59 in immune-edited tumor cells. We believe these results hold important implications for the clinical application of CDC-mediated therapeutic antibody.
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 | 0.000 |
| 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".