Immunosurveillance and Immunoediting of Breast Cancer via Class I MHC Receptors
Bibliographic record
Abstract
Abstract Ly49 receptors, which recognize “self” class I major histocompatibility complex (MHC-I) molecules, enable natural killer (NK) cells to detect loss of MHC-I expression on transformed and virally infected cells. The impact of NK cell–mediated MHC-I surveillance on immunoediting of breast cancer is still not fully understood. This work assesses the impact of Ly49 receptors on tumor development in terms of cancer control and in driving immune-evading cancer mutations. Genetically modified Ly49-deficient mice and those lacking NK cells through antibody depletion were less able to control E0771-derived mammary tumors in an MHC-I–dependent fashion. Similarly, Ly49-deficient MMTV-PyVT–transgenic mice developed spontaneous mammary tumors faster than Ly49-sufficient MMTV-PyVT mice. Fewer CD69+ and granzyme B+ NK cells were detected among the tumor-infiltrating lymphocytes in Ly49-deficient than in Ly49-sufficient MMTV-PyVT mice. Furthermore, tumors from Ly49-deficient mice displayed reduced MHC-I expression, suggesting that tumors growing in these mice lacked an Ly49-derived pressure to maintain MHC-I expression. These same MHC-I-low tumors from Ly49-deficient mice were unable to flourish when transferred to Ly49-sufficient hosts, confirming that this tumor mutation was in response to an Ly49-deficient environment. This work demonstrates a role for Ly49 receptors in the control of mammary cancer, and provides evidence to support a model of tumor immunoediting, in which selective pressures from the immune system drive immune-evasive cancer mutations. Cancer Immunol Res; 5(11); 1016–28. ©2017 AACR.
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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".