Epigenetic modification of <i>CSDE1</i> locus dictates immune recognition of nascent tumorigenic cells
Bibliographic record
Abstract
Weak immunogenicity of tumor cells is a root cause for the ultimate failure of immunosurveillance and immunotherapy. Although tumor evolution can be shaped by immunoediting toward a less immunogenic phenotype, mechanisms governing the initial immunogenicity of primordial tumor cells or original cancer stem cells remain obscure. Here, using a single tumor-repopulating cell (TRC) to form tumors in immunodeficient or immunocompetent mice, we demonstrated that immunogenic heterogeneity is an inherent trait of tumorigenic cells defined by the activation status of signal transducer and activator of transcription 1 (STAT1) protein in the absence of immune pressure. Subsequent investigation identified that the RNA binding protein cold shock domain–containing protein E1 (CSDE1) can promote STAT1 dephosphorylation by stabilizing T cell protein tyrosine phosphatase (TCPTP). A methyltransferase SET and MYN domain–containing 3 (SMYD3) was further identified to mediate H3K4 trimethylation of CSDE1 locus, which was under the regulation of mechanotransduction by cell-matrix and cell-cell contacts. Thus, owing to the differential epigenetic modification and subsequent differential expression of CSDE1, nascent tumorigenic cells may exhibit either a high or low immunogenicity. This identified SMYD3-CSDE1 pathway represents a potential prognostic marker for cancer immunotherapy effectiveness that requires further investigation.
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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".