Epigenetic Silencing of TAP1 in Aldefluor+ Breast Cancer Stem Cells Contributes to Their Enhanced Immune Evasion
Bibliographic record
Abstract
Abstract Avoiding detection and destruction by immune cells is key for tumor initiation and progression. The important role of cancer stem cells (CSCs) in tumor initiation has been well established, yet their ability to evade immune detection and targeting is only partly understood. To investigate the ability of breast CSCs to evade immune detection, we identified a highly tumorigenic population in a spontaneous murine mammary tumor based on increased aldehyde dehydrogenase activity. We performed tumor growth studies in immunocompetent and immunocompromised mice. In immunocompetent mice, growth of the spontaneous mammary tumor was restricted; however, the Aldefluor+ population was expanded, suggesting inherent resistance mechanisms. Gene expression analysis of the sorted tumor cells revealed that the Aldefluor+ tumor cells has decreased expression of transporter associated with antigen processing (TAP) genes and co-stimulatory molecule CD80, which would decrease susceptibility to T cells. Similarly, the Aldefluor+ population of patient tumors and 4T1 murine mammary cells had decreased expression of TAP and co-stimulatory molecule genes. In contrast, breast CSCs identified by CD44+CD24− do not have decreased expression of these genes, but do have increased expression of C-X-C chemokine receptor type 4. Decitabine treatment and bisulfite pyrosequencing suggests that DNA hypermethylation contributes to decreased TAP gene expression in Aldefluor+ CSCs. TAP1 knockdown resulted in increased tumor growth of 4T1 cells in immunocompetent mice. Together, this suggests immune evasion mechanisms in breast CSCs are marker specific and epigenetic silencing of TAP1 in Aldefluor+ breast CSCs contributes to their enhanced survival under immune pressure.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".