Spontaneous Mammary Tumors Differ Widely in Their Inherent Sensitivity to Adoptively Transferred T Cells
Bibliographic record
Abstract
Immunotherapy of cancer can lead to the selection of antigen loss variants, which provides strong rationale to target oncogenes that are essential for tumor growth or viability. To investigate this concept, we tagged the HER2/neu oncogene with epitopes from ovalbumin to confer recognition by T-cell receptor transgenic CD8(+) (OT-I) and CD4(+) (OT-II) T cells. Transgenic mice expressing neu(OT-I/OT-II) developed mammary adenocarcinomas at 6 to 10 months of age. Adoptively transferred naive OT-I cells (with or without OT-II cells) proliferated vigorously on encountering neu(OT-I/OT-II)-expressing tumors. This was followed by the complete regression of 37% of tumors, whereas others showed partial/stable responses (40%) or progressive disease (23%). Those tumors undergoing complete regression never recurred. In mice with multiple primary tumors, simultaneous regressions and nonregressions were often seen, indicating that immune evasion occurred at a local rather than systemic level. The majority of nonregressing tumors expressed Neu(OT-I/OT-II) and MHC class I, and many avoided rejection through a profound block to T-cell infiltration. Thus, T cells directed against an essential oncogene can permanently eradicate a subset of spontaneous, established mammary tumors. However, in other tumors, local barriers severely limit the therapeutic response. To maximize the efficacy of immunotherapy against spontaneous cancers, predictive strategies that take into account the heterogeneity of the tumor microenvironment will be required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".