Lymphotactin Expression by Engineered Myeloma Cells Drives Tumor Regression: Mediation by CD4+ and CD8+ T Cells and Neutrophils Expressing XCR1 Receptor
Bibliographic record
Abstract
The C chemokine lymphotactin has been characterized as a T cell chemoattractant both in vitro and in vivo. To determine whether lymphotactin expression within tumors could influence tumor growth, we transfected an expression vector for lymphotactin into SP2/0 myeloma cells and tested their ability to form tumors in BALB/c and nude mice. Transfection did not alter cell growth in vitro. Whereas SP2/0 cells gave rise to a 100% tumor incidence, lymphotactin-expressing SP2/0-Lptn tumors invariably regressed in BALB/c mice and became infiltrated with CD4(+) and CD8(+) T cells and neutrophils. Regression of the SP2/0-Lptn tumors was associated with a type 1 cytokine response and dependent on both CD4(+) and CD8(+) T cells, but not NK cells. Both SP2/0 and SP2/0-Lptn tumors grew in nude mice, but growth of the latter tumors was retarded and associated with heavy neutrophil responses; this retardation of SP2/0-Lptn tumor growth was reversed by neutrophil depletion of the mice. Our data also indicate that mouse neutrophils express the lymphotactin receptor XCR1 and that lymphotactin specifically chemoattracts these cells in vitro. Thus, lymphotactin has natural adjuvant activities that may augment antitumor responses via effects on both T cells and neutrophils and thereby could be important in gene transfer immunotherapies for some cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".